Leveraging the True Depth of LLMs
Authors: Ramón Calvo González
Daniele Paliotta
Matteo Pagliardini
Martin Jaggi
François Fleuret
Venue: Accepted by TMLR ()

Abstract: The remarkable capabilities of Large Language Models (LLMs) are overshadowed by their immense
computational cost. While recent work has shown that many LLM layers can be reordered or even
removed with minimal impact on accuracy, these insights have not been translated into significant
inference speedups. To bridge this gap, we introduce a novel method that restructures the
computational graph by grouping and evaluating consecutive layer pairs in parallel. This approach,
requiring no retraining, yields a 1.19x throughput gain on Llama 2 7B while reducing the average
benchmark accuracy by only 1.5%. We demonstrate the practical value of this method for large-scale
LLM deployment and show that some of the lost accuracy can be recovered with lightweight
fine-tuning of the parallelized layers.

Published in Transactions on Machine Learning Research (01/2026)
Leveraging the True Depth of LLMs
Ramón Calvo González
University of Geneva
Daniele Paliotta University of Geneva
Mattéo Pagliardini EPFL
Martin Jaggi EPFL
François Fleuret
University of Geneva FAIR at META
Reviewed on OpenReview: https: // openreview. net/ formm Jd=JecJ6Y
Abstract
The remarkable capabilities of Large Language Models (LLMs) are overshadowed by their immense computational cost. While recent work has shown that many LLM layers can be reordered or even removed with minimal impact on accuracy, these insights have been translated into significant inference speedups. To bridge this gap, we introduce novel method that restructures the computational graph by grouping and evaluating consecutive layer pairs in parallel. This approach, requiring retraining, yiclds a 1.19x throughput gain on Llama 2 7B while reducing the average benchmark accuracy by only 1.5%. We demon- strate the practical value of this method for large-scale LLM deployment and show that some of the lost accuracy can be recovered with lightweight fine-tuning of the parallelized layers.
1 Introduction
All Painta (Liama 78)
The rapid advancement LLMs has revolutionized Arti- ficial Intelligence applications across industries. However, the ever-increasing computational demands of these mod- cls, with parameters often numbering hundreds of billions, present significant commercial challenges. Efficient infer- ence is erucial for organizations that deploy these models at scale, as directly impacts operational costs, user ex- perience, and environmental sustainability (Singh al., 2025; Xu al., 2024; Wu et al., 2022). Monthly cloud computing expenses for LLM inference reach millions of dollars for high-traffic applications, making optimiza- tion techniques essential, In addition, reducing inference latency critical for real-time applications and for de- ploying models on devices with more limited compute.
Pareto Pront (Llumo
Perplecity (log scall)
All Pores (Tlama 3.2 5R)
Pareto Trom (Llmmo 30)
16
Roselne
Bisetine (Llama 322 RB)
12
Average Time (s)
Figure 1: The effect of LP on execution time (4K tokens) and perplexity (measured against RedPa- jama (Together Computer, 2023)).

---

Published in Transactions on Machine Learning Research (01/2026)
Thus, the development and implementation of efficient in- ference methods has become key differentiator in the competitive Al market, driving both innovation and prof- itability.
FFN
FFN1
FFN2
LLMs have evolved to incorporate architectures with hun- dreds of layers (OpenAI, 2023; authors, 2024). These models are constructed from stacked transformer blocks, each comprising attention and feedforward subblocks, with residual stream traversing the entire architecture to facilitate efficient gradient propagation during training, This architectural choice parallels the design principles of ResNets (He al., 2015), where research has shown that network depth may be partially redundant, allow- ing layer reordering without significant, performance loss (Veit et al., 2016). Recent investigations have revealed similar ficxibility in transformer architectures (Lad et al., 2024), where interventions such as layer removal and swap- ping are applied without large performance degradations. Although these findings challenge our understanding of LLMs' true effective depth, their potential for optimizing inference efficiency remains unexplored.
LN
LN
LN
MHA
MHA1
MHA2
f
LN
LN
LN
(a)
Figure 2: Comparison of normal transformer
block (a) with our layer parallel implementation (b). Divergent paths in (b) are split across the Tensor Parallel axis (Eq. LP).
Inspired by this observed layer independence, inves- tigated several interventions to the computational graph of pre-trained LLMs. Our exploration of layer shuffling, pruming, and merging revealed that multiple consccutive block pairs can be processed in parallel while main- laining accuracy across perplexily and In-Conlext Learning (ICL) benchmarks. This led us to propose Layer Parallelism (LP), novel approach that enhances inference speed when performing inference in the Tensor Parallel (TP) regime. LP modifies the computational graph of pre-trained LLM to reduce the inter-device communication by half, with minimal drop in model performance. Furthermore, show that this performance degradation can be partially mitigated through targeted fine-tuning procedures.
Contributions. Our contributions can be summarized as follows:
We explore the space of interventions the layers of pre-trained LLM and find that some trans- formations, such as contiguous parallelization, preserve model performance.
We find that we can define parallelization transform on the computational graph of two sequential Transformer layers, and stack this parallelization operation across several sequential pairs of layers without losing significant ICT, performance. Our approach, which we call L.P, can be applied to existing Transformer models.
We show that by fine-tuning the LP blocks we can recover some of the lost performance, while retaining the previously obtained speed-up.
2 Related work
The effective depth of Deep Networks. Theoretically, given enough width, any feed-forward network with at least one hidden layer model any function (Pinkus, 1999). In practice, it is easier to achieve high expressivity by incrcasing the model's depth. However, naively incrcasing network depth can complicate optimization, since the gradients now have to flow through many layers. To alleviate this problem, ResNets (He et al., 2015) introduced skip connections at regular intervals to allow an easy flow of the gradient to the first layers. Alternatively, Inception (Szegedy et al., 2014) explored approaches to boost computational power by adding additional processing units along different parallel pathways in the computational network, rather than just along single sequential path. A unification of both methods can be found in the Highway
2

---

Published in Transactions on Machine Learning Research (01/2026)
Networks (Srivastava et al., 2015), where the skip connection of the residual blocks consists of another block of compute. Nowadays, residual connections are ubiquitous in large models.
Efficient inference of LLMs. Several complementary approaches exist for enhancing the computational efficiency of large-scale models, primarily through muuning/pparitt, quantization, and parallelism. Pruming (LeCun et al.; 1989; Han et al., 2015; 2016; Frantar & Alistarh, 2023) constitutes a dimensional reduc- tion methodology that systematically eliminates redundant parameters while preserving model performance, thereby introducing architectural sparsity. This methodology is founded on empirical evidence demonstrating that neural networks frequently exhibit overparameterization, containing numerous weights with negligible contributions to the output. Through sophisticated pruning strategies, the inherent sparsity support in con- temporary accelerators can be leveraged to enhance both memory utilization and computational efficiency (Zhang et al., 2020; Wang et al., 2021). Early-exit (Teerapittayanon et al., 2017: Zhou et al., 2020) can be seen as a way of runtime layer-wise pruning, which halts the LLM forward pass when the next token certainty is high in the intermodiate layers. This approach can also be thought of as a way of reducing the effective depth of the model at test time. In contrast, quantization encompasses the transformation of ceating--pintt numerical representations (predominantly FP32) into reduced-precision integer formats, such as INT8 or INT4 (Shen et al., 2019; Han et al., 2016; Jacob et al., 2018). When implemented on hardware accelerators, these lower-precision representations allow for higher FLOPs and better use of the memory bandwidth, addressing a primary bottleneck in modern large-scale models (Gholami et al., 2024); moreover, integer-based computations yield enhanced processing speed and substantially improved energy efficiency (Horowitz, 2014). Finally, parallelization techniques during inference, such as tensor and pipeline parallelism, enable the distribution of computational workload across multiple accelerators, thereby reduc- ing latency and incrcasing throughput, although this often requires carcful consideration of communication overhead and load balancing (Li et al., 2024; Narayanan et al., 2021).
Tensor-parallel optimizations. Inter-device communication remains the dominant bottleneck in tensor parallelism, as cach transformer sub-modulc imposes a synchronization step. Recent work targets this cost by either removing synchronization points or shrinking the communicated tensors. Sync-Point Drop (Kim et al., 2025) cuts the number of all-reduce operations by modifying block structure so that local attention outputs propagate without immediate aggregation; layers are ranked by sensitivity to synchronization removal, and only the sensitive ones receive targeted tuning, yielding 20% speedup with roughly 1% accuracy loss. A complementary direction reduces communication volume. (Dong et al., 2024) quantizes per-layer activation exchanges, reaching a 3.8x compression ratio with about a 2% drop on the evaluated benchmarks.
Parallel attention-feedfowaan fusion. GPT-J (Wang & Komatsuzaki, 2021) introduced a parallel formulation of the transformer decoder layer, executing attention and feedforward sub-blocks concurrently:
y = x MHA(LNNHAA + FFN(LNFFx(z))
For models trained with tensor parallelism, this modification halves the number of required all-reduce op- crations. Additionally, it reduces memory bandwidth usage by climinating one read and write operation of hidden states from HBM. The input projections for the attention and MLP sub-blocks can also be fused into a single kernel, further increasing arithmetic density. Consequently, training time is reduced by approx- imately 15% without observable performance degradation. PaLM (Chowdhery et al., 2023) also adopted this formulation, noting that negative effects from deviating from the standard transformer self-attention diminish with increasing model size. This parallel formulation continues to be employed in more recent LLMs, including Gemini 1.5 Flash (Georgiev et al., 2024).
In contrast to these methods, which require training new model from scratch with modified architecture, our approach is applied post-hoc to already-trained models (authors, 2023; 2024; Yang et al., 2025). This highlights two other key differences. First, the granularity of parallelism differs: GPT-J-style models paral- lelize the attention and feed-forward sub-blocks within a single layer, whereas our method parallelizes entire consecutive layers, directly reducing the model's effective depth. Second, LP accepts a trade-off by approxi- mating the original computation, which results slight performance degradation in exchange for inference acceleration. This degradation can be largely recovered with light fino-tuning. The GPT-J architecture, by contrast, is exact by definition, as the model was trained with it from the beginning.
3

---

Published in Transactions on Machine Learning Research (01/2026)
Parallelism via Computational Graph Optimization. Recent research has investigated architectural layer-level optimization strategies to enhance transformer model inference efficiency. The Staircase Trans- former (Cutler et al., 2025) implements parallel layer execution with dynamic recurrent computation based on model requirements. Similarly, the Staggering Transformer (Cai et al., 2024) achieves layer parallelization by connecting layer 4 at time step both the (4-1) output of layer xx-1 and the output of layer 4--2 To the best of our knowledge, no rescarch has addressed the fusion of consecutive layers through tensor parallelism.
3 Effective Depth
In this section we investigate the effective depth of pretrained LLMs by applying several transforma- tions and measuring the resulting perplexity degra- dation. We reveal loose dependencies between in- termediate layers. The transformations consist of shuffling, merging, and pruning transformer layors. To avoid the combinatorial explosion resulting from considering all possible subsets of transformer layers, instead apply transformations to all contigu- ous stretches of layers. If L (l...... ...,,. are the ordered layers, then we apply our transformations to all the sublists {Liji= with 1 S S e S N. Previous works have shown that-at at least when considering
Output
Output
Output
Output
L1
L2
2 L1 + 12
L1 T.2
T,2
T
H
Input
Input
Input
Input
(a)
(b)
(c)
(d)
Figure 3: Diagram of transformations applied in
$ 3. Diagrams (a,b,c,d) represent shuffling, merging, pruning- the importance of layers well-behaved, pruning and parallel respectively. with low-importance layers close to one another (Men et al., 2024), which justifics considering contiguous stretches of layers only.
Shuffling, pruning and merging blocks. We start by investigating the effect of several transformations on the model's perplexity. First, we experiment with shuffling contiguous stretches of layers (Fig. 3a), re- ordering them according to random permutations. Results, shown in Fig. 4(a), reveal that while shuffling the early and late layers is detrimental, there are large stretches intermediate blocks that can be shuffled with surprisingly low impact on perplexity. For instance, one can shuffle layers 15 through 24 of Llama 7B and only increase perplexity by 2.9. This suggests that many layers may operate at a similar level of abstraction, challenging the classical belief of strictly hierarchical representations. This observed layer decoupling is key insight. We also experiment with pruning (Fig. 3c) and merging (Fig. 3b) contiguous layers. Pruning, studied in prior works (Gromov et al., 2024; Jung et al., 2019), involves removing layers entirely. Merging involves averaging the weights of consecutive layers. We find that both transformations lead to a more significant perplexity increase compared to shuffling (see Fig. 4b and Fig. 4c). Merging, in particular, offers no advantage over pruning, suggesting that naively combining weights from different layers is ineffective. These initial experiments indicate that while layers are robust to reordering, their individual parameters are crucial.
Running blocks in parallel. The observed layer decoupling suggests that specific transformer operations may be executed independently, providing an opportunity for parallel computation. More precisely, let's consider two sequential transformer layers 4 and 4x+1, each comprising attention and Feed-Forward Network (FFN) sub-blocks (Ax(+) and Fx).), respoctively). The standard sequential output y for these layers, given an input æ, is given by:
y = x + (x)
+ FF(lx + (x()))
AR+1 + As(x) Fa(2 Ax(=))
+ FREI(# + Aạ(x) + Fx(x Ax()))
(1)
+ Ax+1
+ Ax(x) F2(x Aa(x))))
(SEQ)
4

---

Published in Transactions on Machine Learning Research (01/2020)
Proprte Jaa maki
20
26
30
(a) Shuffling.
(b) Pruning.
(c) Merging.
(d) Parallel.
(e) 2-Parallel.
Changes in perplexity when applying transformations on contiguous stretches of layers. Each of the five heatmaps above corresponds to a transformation of a group of consecutive layers, where the row index s corresponds to the first layer of the group, and the column index to the last. The color coding indicates how the perplexity estimated on subset of RedPajama (Together Computer, 2023) is impacted by the corresponding modification of the model. The perplexity for the base Llama 2 7B model is 6.2. In (a), we shuffle- for each forward- the layers from s to e. We can see that many consocutive layers can be shuffled with little impact on the overall perplexity. For instance, shuffling layers 15 to 25-10 layers in total-raise the perplexity only to 9.1. In (b), we prune contiguous stretches of layers. We can see that not many blocks can be removed without starting to significantly degrade the perplexity. In (c) we merge contiguous layers. The results with merging no advantage in merging layers, most likely result of averaging matrices that originate from different initial values. In (d) we run contiguous blocks in parallel. Given the success of shuffling, it makes sense that this approach works well. Running blocks 17 to 27 raises the perplexity to 9.3. Finally, in (e) we run pairs of consecutive loyers in parallel. As a result, we can parallelize much longer stretches of layers. For instance, we can apply this transformation from layer to 29 and only increase the perplexity to 9.1. This reduces the depth of the model from 32 to 19. This result makes it possible for us to leverage this parallelism for faster inference we discuss in $ 4.
Figure 4:
nearly identical to those for pruning. This reveals there is
The highlighted terms represent the first block's contribution to the second block's processing. Given the observed layer independence, we can hypothesize that these terms have minimal impact, leading to the following approximation:
i = x (x Fa(a + Ax())) Ast1(z) Fa+I( Ax+1(z)) R x + (a) ( + Fa(x + Ax(x)) + (x)) + Fx41 + Ax(x) + (z))
(PAR) (LP)
This approximation enables parallel execution of blocks l and f++1 through divergent computational paths. We experiment with running contiguous stretches of layers in parallel and show our results in Fig. 4d. Wo observe results similar to shuffling. Unlike shuffling, this approach allows us to potentially improve the runtime through enhanced parallelism. We show how we can, for instance, run layers 17 to 27 in parallel, only losing 3.1 perplexity points, while reducing the depth of the model from 32 to 23.
To assess how strongly attention and FFN sub-blocks rely on the residual stream generated by preceding layers, we apply a CKA-based comparison (Kornblith et al., 2019). For each prompt drawn from small RedPajama subset, we record module activations under two conditions: a standard forward pass and a counterfactual pass in which the incoming residual contribution is removed before processing the next block. The standard decoder update for layer ie is:
hg Ax(x) + Fe + Ac())
Ap+1 As+1(hs)
FE+1 FR+1 (hx AR+1)
ho+1 he + Ap+1 + Fb41
5

---

Published in Transactions on Machine Learning Research (01/2026)
CKA similarity with counterfactual residual stream
1.0
0.8
CKA Similartyy
0.6
0.2
Attention CKA
10
Layer index
Figure 5: CKA similarity for Qwen3-4B between the original MHA /FFN activations and the counterfactual activations that exclude incoming residuals. Higher values imply greater invariance to the upstream residual stream. A plateau of high CKA similarity between pairs of layers is preceded by a sharp similarity decline at layer 16, which coincides with the performance degradations experienced when applying different levels of LP at different positions.
Counterfactual activations are obtained by subtracting the residual stream before evaluating the next block:
hug = h4 - Ax(x) fx(x + Ax(x))
Āst1 = Ak+1(h+)
Fx+1 F + As+1)
Similarity between original and counterfactual activations then computed via CKA:
t CK,(AA;
St = CKA(Fr,
As shown in Fig. 5, layers that tolerated the 2-parallel intervention also show high CKA similarity, indicating limited dependence of their attention and FFN computations on the immediate residual input.
Contiguous 2-parallel. Instead of parallelizing long stretches of layers, we experiment with rumning pairs of consecutive layers in parallel. This springs from the assumption that local ordering matters less than global ordering, i.c. shuffling consccutive layers introduces fower potential issues than shuffling layers separated by larger distances. As example, if apply the proposed transformation to layers (f1s, (10 f17, E (19), ) it would result in the following process: (1) the two layers (C1s, (16) process the input in parallel (according to equation (PAR)), (2) the output is forwarded to layers (Ci7,li)) which process it in parallel; finally, in (3) their joint output is fed to layer b1 which processes it on its own as any normal layer. The effect of such a transformation on the compute graph can be seen in Fig. 4e. Remarkably, it is possible to run wide stretches of consecutive pairs of blocks in parallel with only a minor degradation perplexity. For instance, one can apply this Iransformation from layer 4 layer 29 with only a perplexity increase of 2.9, while reducing the model depth from 32 to 19. The success of this approach led us also try running triplets of consecutive layers in parallel, but found it to perform worse
4 Efficient Parallelization of Blocks
Naively trying to fuse two attention or MLP sub-blocks does not result in a noticeable improvement of inference speed for large batch sizes and sequence lengths, since in these situations, inference approaches

---

Published in Transactions on Machine Learning Research (01/2026)
the the compute-bound regime. For this reason focus our attention on the Tensor Parallel setting, where each module's weights are split over two more GPUs. Rearranging the computational graph of two contiguous layers like in Fig. 2b effectively reduces the number of inter-GPU synchronizations by half. Now, each divergent path is computed in parallel over multiple accelcrators, and only the single intermediate and final results need to be synchronized. While this approach not. numerically equivalent to (PAR), we nonetheless- and quite surprisingly- show that it works well on already trained models, circumventing the need to train from scratch.
LP Multi-Head Attention. Traditional tensor parallelism in MHA distributes attention heads evenly across GPUs (Shocybi et al., 2020), performing self-attention and output projection locally before gathering results through an all-reduce summation. Fach GPU processes tensors of dimensions Q, K,V,at all E RT* where T' is sequence length, D is feature dimension, and g is the number of parallel workers. The local output projection produces low-rank o, RTxD D for each worker and then final all-reduce operation performs = Ei Os, computing the final output.
To implement LP, we increase the depth of the query, key, and value weight matrices (Wo, WK. Wv E R(a (alnaaa)x)))) and widen the output projection (Wo E A.)), where n, represents heads per GPU and ha head dimensionality. The reduction operation now will simultaneously compute the full-rank output projections and the sum of all parallel layers (Fig. 6). Note that this approach requires that n, is divisible by the total number of GPUs.
LP FFN. Standard tensor parallelism for hngle-hiddeenllyerrr FFNs splits the first layer's output across devices, generates low-rank outputs from the second layer, and sums them through reduction. To parallclize two FFN layers, we double the first layer's output dimensionality and perform separate output (low-rank) projections for each layer. A single reduction operation then serves the dual purpose computing full outputs for each layer and combining their results, as shown in Fig. 2(b). In summary, LP for FFN just concatenates the up-projection weights and continues as normal TP, allowing for multiple GPUs to be allocated per parallelized layer.
Handling of the LayerNorms. Since we assumee at least one GPU per parallelized layer, can assign each original LayerNorm to the divergent path that contains the attention and FFN blocks from its original layer. We have also observed that using the same merged LayerNorm with linear interpolation and spherical linear interpolation in each divergent path yiclds good results. For the sake of simplicity, we conduct all of our experiments using the original LayerNorms on each divergent path.
GPU
Layer & Layer
01 E RTxxD
E RTxD
E RTxD
K
Self att.
all
02 E RTxD
Figure 6: LP attention implementation. This diagram shows the implementation of the LP attention from Fig. 2b. The stacked layers represent different GPUs, the colors indicate different layers and the arrows express linear projections. Tn this case, the number of GPUs and the number of parallelized layers coincides and is two, which is the set-up that use for all our experiments in this work.
7

---

Published in Transactions on Machine Learning Research (01/2026)
7,5
20
18
Perpleaty
A-18
Am15
Perplestt
Aw20
Dustics
14
6.5
12
15
25
Lnd Index
End Index
Llama2 7B.
(b) Llama3.2 3R.
Figure Perplexity when running pairs of consecutive layers in parallel. Perplexity of Llama2 7B and Llama3.2 2 3B models the test set of RedPajama (Togeeeerr Computer, 2023) when applying Layer Parallelism to A consecutive layers. The parallelized interval for each data point is [end index A, end index]. where end inder is the last layer in the LLM to which LP was applied.
Table 1: 5-shot In- In-Context Learning accuracies across standard benchmarks. Effective Depth shows the minimum number of sequential operations from input to output after applying L.P. We use the ablation on Fig to choose the LP configurations that minimized the perplexity. *ifeval was evaluated on 0-shot performance.
Eff. Depth
Speed
Avg
ReL.
MMLU
PIQA
Arc
Are C.
WinoG
OBQA
hswag
GSM8K
ifeval*
Llama 2 7B (Chat)
32 (base)
x1.00
53.80
1.00
47.27 0.4
77.69 1.0
79.63 a.s 49.06 1.5
72.06 1.3
33.00 2.1
58.87
0.5
22.97 1.2
43.65
27 (ours)
x1.15
53.89
1.00
47.46 0.4
77.09
1.0
78.03 0. 48.81 15
71.82 1.3 33.80 2.1 58.39
0.5 22.21 1.1
47.36
26 (ours)
x1.19
53.25
0.99
47.24 0.4
76.66
1.0
77.48 0.9 47.01
1.5
71.51
1.8 34.00 2.1 57.79
0.5
19.03 1.1
48.56
25 (ours)
x1.23
52.33
0.97
47.67 044
76.33
1.0
77.19 0.9
46.50 1.5
70.09
1.3 33.20 2.1
57.24
0.5
14.78 1.0
47.96
24 (ours)
x1.28
49.62
0.92
45.47
76.55
1.0
75.67
43.69
1.5 67.88
1.3 30.40
2.1
55.82
0.5
9.63 0.8
41.49
23 (ours)
x1.31
47.71
0.89
42.71
75.24
1.0
74.28
41.13 1.4
66.54
1.3 30.80
2.1
54.06
0.5
6.97
37.65
Llama 3.2 3B (Instruct)
(base)
x1.00
60.88
1.00
59.56
77.09
79.29
46.50 1.8
70.24
30.80 2.1
52.53
64.75
67.15
(ours)
x1.12
57.16
0.94
59.14
76.22
1.0
76.60
1.5
70.09
30.0
51.31
45.87
60.31
(ours)
x1.15
55.44
59.05
75.46 1.0
76.18
44.71 1.5
68.98
2.0
51.16
35.71
59.95
(ours)
x1.19
51.59
0.85
56.12
1.0 74.70
75.04
1.5
66.77
28.80
2.0
50.88
0.5
0.8
55.64
(ours)
23
48.03
0.79
50.72 0.4
1.0
72.56
0.9
40.19
1.4
64.09
1.4
28.40
2.0
49.47
0.5
3.11 0.5
49.04
(ours)
x1.28
45.76
0.75
40.70 0.4
74.10
1.0
70.75
0.9
39.42
1.4
62.75
1.4
29.00
2.0
47.55
0.5
3.11
0.5
44.48
Qwen3
4B
(Instruct)
36 (base)
x1.00
63.72
1.00
70.16
76.44
1.0
84.76
58.79 1.5
66.30 1.3
37.20 2.0
52.77
84.99 1.0
42.09
31 (ours)
x1.13
57.72
0.91
68.87
74.59
1.0
81.82
53.67 1.6
65,98
35.20
2.2
50.10
53.75
35.49
30 (ours)
x1.15
55.45
0.87
67.49
74.97
1.0
81.84
52.39 1.4
65.11
33.40
2.1
48.95
36.77
38.13
29 (ours)
x1.18
51.84
0.81
63.56
74.70
1.0
79.88
50.43 1.4
63.22
33.00
47.25
17.97
36.57
28 (ours)
x1.21
49.09
0.77
53.95
74.32
79.29
48.98
62.75
30.0
45.24
12.66
34.65
27 (ours)
x1.25
44.68
0.70
44.09
72.96
76.68
44.54
59.67
29.60
43.22
3.56
27.82
Qwen3 14B
(Instruct)
(base)
x1.00
68.75
1.00
78.83 0.4
80.90 1.0
87.75 0.s
66.21 1.4
74.51 1.8
40.40 2.1
61.33 0.5
82.26 1.2
46.52
35 (ours)
x1.12
65.04
0.95
77.92 644
79.82
1.0
86.95 0.a 64.68 1.5
73.56
1.3
38.80 2.0
58.25 0.5
61.64 1.1
43.76
34 (ours)
x1.15
66.29
0.96
77.42 0.4
78.89
1.0
85.61 0.8
62.71
1.5
1. 73.88
37.80
2.0
57.92
0.5
75.36 1.0
47.00
33 (ours)
x1.18
64.64
0.94
75.88 0.4
79.05
1.0
85.61 0.9
60.58
1.4
72.69
1.4
37.40
22
56.93
0.5
69.83 1.0
43.76
32 (ours)
x1.21
62.84
0.91
73.70 0.4
78.89
1.0
84.81 0.9
59.73
1.4
71.03
1.4 36.80
2.0
55.75
0.5
64.67 0.9
40.17
31 (ours)
x1.24
60.11
0.87
71.56
78.56
1.0
85.52
59.98
1.5
68.59
133
36.40
20
54.12
0.5
48.22 0.8
38.01
30 (ours)
x1.27
55.18
0.80
65.43
77.58
1.0
83.84
55.89
1.5
67.32
34.60
2.1
51.97
0.5
23.43
0.8
36.57

---

Published in Transactions on Machine Learning Research (01/2026)
Table 2: Benchmark accuracy restoration for LP-applied Qwen3 models. D'ine-tune entries use 4096
additional training steps; left columns report the fine-tuned accuracy, and right columns report the no- imetune/baseline baseline values.
Fine-tuned
No fine-tuning
Model
Eff. depth
MMLU
Are C.
GSM-8K (%)
MMLU
Arc C.
CSM-8K (%)
(Raseline)
70.16
58.79
69.11 0.4
54.10 1.8
62.47 1.3
68.87 0.4
53.67
53.75 1.0
Qwen3 4B
68.43 0.4
54.10 1.8
56.56 1.4
67.49 0.4
50.43
36.77 0.9
61.96 0.4
51.02 1.6
48.29 1.4
44.09 0.4
44.54 1.5
3.56 0.5
40 (Basclinc)
78,83
66.21
82.26
Qwen3 14B
0.3
63.05
81.73
77.92
64.68
61.64
74.38 0.4
59.30
71.27
73.70
59.73
64.67 0.8
Cache Prefill Llama-2-7b
Cache
-llaa 3.2-3B
140
3107
Time (m/3)
Time [mv)]
13:00
2008
2000
2000
2006
Sequence Length
Length
autoregressive Llama-2-7b
Autoregressive Llama-3.2-38
140000
10000
10000
0800
Tirre (1r))
(ms)
1000
40000
2309
40000
/800
Sequence
Sequence Length
Token Generation Llama-2-7b
Token Generation Llama-3.2-33
Tealitlaym
Soude
Time (mc)
Time [mm)
Soquence Long th
Sequence Longth
Figure 8: Wall clock time to complete the following inference tasks: KV Cache pre-filling, autore- gressive generation, and single token generation with a pre-filled KV Cache. A indicates how many layers have been merged using LP (e.g. A of indicates that groups of 2 layers have been converted effective layers). The gains in inference speed are roughly proportional to the amount of LP. The 1-token generation task for Llama 3.2 3B does not saturate the GPU compute until sequence length of 2048. Even in this regime, LP benefits from considerable speed-ups.
2
5 Experiments & Results
In this section, we evaluate Layer Parallelism across three dimensions: inference speed improvements, impact on In- Context Learning performance, and the potential to recover model accuracy through targeted fine- tuning of parallelized layers.

---

Published in Transactions on Machine Learning Research (01/2026)
Experimental protocol. For all our experiments, use node with x2 A100 SXM4 80Gb GPUs, x4 AMD EPYC 7742 CPUs, and 512Gb of RAM. We test. for varying sequence lengths, up to 4096 (Tlama's 's context window), with a batch size of unless indicated otherwise. We consider two models of the Liama family: Llama2 7B, and Llama3.2 3B, as well as two sizes from Qwen3: 4B and 14B. Given a desired effective depth; we replace the required number of normal layers with LP layers. For Llama 7B and 3.2 3B, the LP layers are selected based on the configuration that minimized the PPI. for a given amount of LP (Fig. 7). For Qwen3, LP is applied until the 4th to last decoder layer. The rest of the layers implement the tensor parallel approach described in (Shoeybi et al., 2020). For evaluation, accuracies using the 1m-eval package (Gao et al., 2024). We test the ICL accuracy of the models on several tasks: MMLU (Hendrycks et al., 2021), PiQA (Bisk et al., 2019), ARC Easy, ARC Challenge, Winogrande (Sakaguchi et al., 2021), Open OpenBookQA (Mihaylov et al., 2018), Hellaswag (Zellers et al., 2019), GSM-8K (Cobbe et al., 2021) and ifeval (Zhou et al., 2023). The perplexity of the models is always evaluated against a subset of the test set of RedPajama (Together Computer, 2023).
measure the ICL 5-shot
Impact of LP on PPL and ICL accuracies. We first examine how perplexily evolves when applying LP across layer sequences of different lengths and depths. Fig. 7 reveals common optimal sequence end- index minimizing perplexity, found at layers 28 for Llama2 7B and 25 for Llama3.2 3B. Table 1 compares the In- Co-Centext Learning performance across models with varying effective depths. Performance declines gradually as LP increases, followed by a sharp drop beyond a certain threshold. Specifically, this occurs after reducing effective depth by 9 layers for Qwen3 14B, by layers for Llama2 7B and Qwen3 4B, and by layers for Llama3.2 3B. These results indicate that larger models are more robust to the computational graph modifications from LP,, suggesting that our approach is likely applicable to current commercial-scale LLMs used in major deployments.
It is worth noting that, unlike the other benchmarks, GSM-SK already drops severely in performance when applying low amounts of LP. Recent mechanistic interpretability research shows that LLMs have special circuitry for math operations, localized in a small set of parameters (Stolfo et al., 2023; Yu & Ananiadou, 2024). (Christ et al., 2025) identify math specific parameters, and report a drop in accuracy of 17% when pruning them. We hypothesize that the changes in the computational graph by the use of LP in some of the late layers of the LLM interfere with these fragile and sparse subnetworks, while leaving general language competence largely intact.
Impact on the inference speed. We run an ablation over several configurations and input sequence lengths on Figure 8 to test the speed on three different tasks: KV-Cache pre-filling, autoregressive generation up to the sequence (eigth(with KV-Cache) and 1-token generation with a pre-filldd KV-Cache of the corresponding sequence length. Our ablations show that the speed gain strongly correlated with the reduction of the effective depth of the model. For the effective depths of 25 (A= = 14) in Llama 2 7B, we observe an average speed-up of 1.29x at the largest sequence length in the 1-token generation task. Likewise, for an effective depth of 23 (A- 10) in Llama 3.2 3B, report speed-up of 1.22x. For more aggressive parallelism, A = 18 and A- = 16, we report a speed-up of 1.38x and 1.35x, at the expense of a large drop in ICL accuracy.
Fine-tuning for performance recovery. While LP provides speed improvements, associated architectural modifications may degrade model performance. To counteract this, we explored whether fine-tuning could effectively restore the original model's capabilities. We apply LP to some configurations of Qwen3-4B and Qwen3-14B (Table 2), and fine-tune the T.P layers on randomly selected samples from the RedPajama. training set (Together Computer, 2023). We employ batch size of 32, linear learning rate schedule starting at 1e-4 and the AdamW optimizer (Loshchilov & Hutter, 2017). We observe a significant restoration of the benchnark accuracies for Qwen3-4B with an effective depth of 27, especially on GSM-SK, which recovered from near-zero levels. Less aggressive usage of LP results in a less pronounced recovery of the accuracy, and fails to fully recover the original model's performance. It is possible that additional fine-tuning, or smarter tuning strategies, could yield further improvements, but resource constraints limited the scope of our experiments.
10

---

Published in Transactions on Machine Learning Research (01/2026)
6 Limitations
The effectiveness of our approach exhibits notable variations across model scales. Smaller models show reduced benefits, likely due to their less sparse activation patterns and more tightly coupled layer dependen- cies. This degradation becomes more pronounced as the LP sequence length increases, suggesting a practical upper limit to the number of layer pairs that can be effectively parallelized.
Regarding the fine-tuning, while some performance loss can be mitigated, were unable to fully recover the baseline model's performance levels. This suggests fundamental trade-offs between computational efficiency and model capability that cannot be entirely eliminated through optimization, or that more involved fine- tuning strategics might be required.
Moreover; determining the 'true' effective depth- the optimal configuration of parallel layer pairs-remaine remains an open challenge as there is no theoretical framework for predicting the optimal grouping strategy.
These limitations highlight important directions for future research, particularly in developing more robust methods for determining optimal layer groupings and investigating the interplay between our approach and other efficiency-oriented techniques.
7 Conclusion
In this work, we presented Layer Parallelism, a novel approach that exploits independence patterns between transformer layers to optimize LLM inference. By restructuring the computational graph to enable parallel execution of consecutive layer pairs through tensor parallelism, achieved substantial speed improvements without model retraining. Our method reduced the effective depth of Llama 2 7B by 21% while maintaining 98% of the original performance (without fine-tuning), yielding up to a 1.29x improvement in inference speed for single-token generation with long sequences. Moreover, we show that we can recover some of the lost accuracy through naive fine-tuning.
These results challenge the conventional view that transformer layers must process information strictly se- quentially, suggesting instead that certain layers can operate independently without significant, performance loss. From practical standpoint, LP offers straightforward approach to improve inference efficiency in production environments. Future work could focus on developing thcoretical franeworks to predict optimal layer groupings, investigating interactions with other efficiency techniques such as quantization, and under- standing the fundamental principles behind layer independence. Despite its limitations, LP represents a practical advancement in making LLM deployment more efficient and economically viable.
Acknowledgments
We would like to thank Benjamin Rio for the thoughtful discussions and help running some of the performance experiments. Ramón's research is supported by META.
References
Llama2 authors. Llama 2: Open foundation and fine-tuned chat models, 2023.
Llama3 authors. The llama herd of models, 2024. URL http:://arrvv.or///a///00.. 217833
Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. Piqa: Reasoning about physical commonsense in natural language, 2019. URL attps://arxivv wttp/bs/////l..ooe/ 11641.
Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D. Loe, Deming Chen, and Tri Dao. Medusa: Simple Ilm inference acceleration framework with multiple decoding heads, 2024. URL
org/abs/2401.107
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. Palm: Scaling language modeling with pathways. Journal of Machine Learning Research, 24(240):1-113, 2023.
11

---

Published in Transactions on Machine Learning Research (01/2026)
Bryan R. Christ, Zack Gottesman, Jonathan Kropko, and Thomas Hartvigsen. Math neurosurgery: Isolating language models' math reasoning abilities using only forward passes, 2025. URL https://arxiv.org flos/2410.1990
Karl Cobbe, Vineet. Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. Training verifiers to solve math word problems, 2021. URL https://arxiv. http//axiiv.oogg/ba////a /tggabb///1/....//////////////// 14168.
Dylan Cutler, Arun Kandoor, Nishanth Dikkala, Nikunj Saunshi, Xin Wang, and Rina Panigrahy. Stag- former: Time staggering transformer decoding for runninglayers in parallel, 2025. URL https: org/abs/2501. 15665.
Harry Dong, Tyler Johnson, Minsik Cho, and Emad Soroush. Towards low-bit communication for tensor parallel Ilm inference, 2024. URL /tttps://axiiv. http://wwivv.//aaaa/////a wwggabs/////eeeoeeeo..oooeeeeeee /ttp/////...///
Elias Frantar and Dan Alistarh. Sparsegpt: Massive language models can be accurately pruned in one-shot. In International Conference on Machine Learning, pp. 10323 10337. PMLR, 2023.
Leo Gao, Jonathan Tow, Baber Abbasi, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Lau- rence Golding, Jeffrey Hsu, Alain Le Noachh, Haonan Li, Kyle McDonell, Niklas Muennighoff, Chris Ociepa, Jason Phang, Laria Reynolds, Hailey Schoelkopf, Aviya Skowron, Lintang Sutawika, Eric Tang, Anish Thite, Ben Wang, Kevin Wang, and Andy Zou. A framework for few-shot language model evaluation, 2024. URL https://zenoo:: wtt/r/ooorrs///e00000.. 12608602.
Petko Georgiev, Ving lan Lei, Ryan Burnell, Libin Bai, Anmol Gulati, Garrett Tanzer, Damien Vincent, Zhufeng Pan, Shibo Wang, et al. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXie preprind or orXiv:2403.05530 2024.
Amir Gholami, Zhewei Yao, Schoon Kim, Coleman Hooper, Michael W Mahoney, and Kurt Keutzer. Ai and memory wall. IEFE Micro, 2024.
Andrey Gromov, Kushal Tirumala, Hassan Shapourian, Paolo Glorioso, and Daniel A. Roberts. The unrea- sonable ineffectiveness of the deeper layers, 2024. URL https:// www.awwwww.aaa..eaaaaooao.oawwot atrg//bs//43...... 17887.
Song Han, Jeff Pool, John Tran, and William Dally. Learning both weights and connections for efficient neural networks, 2015. URL https://arxiv.rr orgg/aas/5006000000
Song Han, Huizi Mao, and William J. Dally. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, 2016. URL https://arxiv. http://www.rgga////...... 00149.
Jan Hansen-Palmus, Michael Truong Le, Oliver Hausdörfer, and Alok Verma. Communication compression for tensor parallel Ilm inference, 2024. URL https://arxit: http://///aii..o//////////// wttp/oo/////..../00/ 09510.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition, 2015. URL
03385.
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding, 2021. URL bttps://arxixs org/abaa hts//bb//200 2009 20w.//000000000....
Mark Horowitz. 1.1 computing's energy problem (and what we can do about it). In 2014 JEEE international solid-slate circwils conference digest of technical papers (ISSCC), pp. 10 14. IEEE, 2014.
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. Quantization and training of neural networks for efficient integer-arithmetic- only inference. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.
2704 2713, 2018.
Hyun-Joo Jung, Jacdeok Kim, and Yoonsuck Choc. How compact?: Assessing compactness of representations through layer-wise pruning, 2019. URL https: /tarxii///rxiv.../// wwwrrriw.wrggbb////eeeea httpaa/////99..0/ 02757.
12

---

Published in Transactions on Machine Learning Research (01/2026)
Han-Byul Kim, Duc Hoang, Arnav Kundu, Mohammad Samragh, and Minsik Cho. Spd: Sync-point drop for efficient tensor parallelism of large language models, 2025. URL
20727.
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton. Similarity of neural network representations revisited, 2019. URL
http:///xvvv..orr/a///////// 00414.
Vedang Lad, Wes Gurnee, and Max Tegmark. The remarkable robustness of 1mss: Stages of inference?, 2024. URL https:
19384.
Yann LeCun, John S. Denker, and Sara A. Solla. Optimal brain damage. In Newral Information Processing Systems, 1989. URL
wat.racccoco.ooooooosssSSSSII 77858811
Zonghang Li, Wenjiao Feng, Mohsen Guizani, and Hongfang Yu. Tpi-llm: Serving 70b-scale llns efficiently on low-rcsource edge devices, 2024. URL httpp://aaxxtv.oor////////111 http.//orrg/bb//11.000..... 00531.
Nya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXie preprint 2017.
Xin Men, Mingyu Xu, Qingyu Zhang, Bingning Wang, Hongyu Lin, Yaojie Lu, Xianpei Han, and Weipeng Chen. Shortgpt: Layers in large language models are more redundant than you expect, 2024. URL https://arxiv.ce http://////xvvooo/aaaaa///////// 03853.333333.....
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. Can suit of armor conduct electricity? a new dataset for open book question answering, 2018. URL https://arxiv. org/abs ht/////s//100/...// 0tt//90099.0099....
Deepak Narayanan, Mohammad Shocybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Anand Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Calanzaro, Amar Phan- ishayee, and Matei Zaharia. Efficient large-scale language model training on gpu clusters using megatron- Im, 2021. URL https: //arxvv.///ivv///// http://wwwx.../aaaa//aaa
org/abs//// /214.. 2104.04473.
OPenAL. GPT-4 Technical Report, March 2023. arXiv:2303.06774 [cs].
URL
Allan Pinkus. Approximation theory of the mlp model in neural networks. Acta Numerica, 8:143-195, 1999. doi: 10.1017/5002242900029999
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. Winogrande: An adversarial winograd schema challenge at scale. Communications of the ACM, 64(9):99-106, 2021.
Sheng Shen, Zhen Dong, Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami, Michael W. Mahoncy, and Kurt Keutzer. Q-bert: Hessian based ultra low precision quantization of bert, 2019. URL https://arxiv.cr org/ abs/1909. 05840.
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro. Megatron-Im: Training multi-billion parameter language models using model parallelism, 2020. URL http:://arxivv.or/abb////// wrg//bs//909 1000./0./..00....0... 08053.
https://arxiv..
Aditi Singh, Nirmal Prakashbhai Patel, Abul Ehtosham, Saket Kumar, and Tala Talaci Khoci. survey sustainability in large language models: Applications, economics, and challenges, 2025. URL https:
//arxiv. ///aaaii.rggaas///////aaoa.aaaee ottp.//////....0//// 04782.
Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber. Highway networks, 2015. URL https: wwwp:/vv.rrgbbb/55...o. 00387.
Alessandro Stolfo, Yonatan Belinkov, and Mrinmaya Sachan. A mechanistic interpretation of arithmetic reasoning in language models using causal mediation analysis. arXiv preprint arXiv:2305.1504, 2023.
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanct, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions, 2014. URL https: wwtps//.rrg//bb///eeeeeoeeeeeeee .8422.
13

---

Published in Transactions on Machine Learning Research (01/2026)
Surat Teerapittayanon, Bradley McDanel, and H. T. Kung. Branchynet: Fast inference via early exiting from deep neural networks, 2017. URL https://arxiv https://ar.i...orraaa////aa trrg/bbs/1709 000../11..0....
Together Computer. Rodpajama: An open source recipe reproduce Ilama training dataset, 2023. URL
bettp://githule http:://ggthhbb..mmmooggeetteeet cct.///tgeeeeerrcoottttee//////e jma--atta
Andreas Veit, Michael J. Wilber, and Serge J. Belongie. Residual networks behave like ensembles of relatively shallow networks. In NIPS, pp. 550 558, 2016.
Ben Wang and Aran Komatsuzaki. GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. hthpa///o////ngoooooo/////aaaaaa wra.sssorrtttaaoooor-e-eee--eeee jax, May 2021.
Hanrui Wang, Zhekai Zhang, and Song Han. Spatten: Efficient sparse attention architecture with cascade token and head pruning. In HPCA, pp. 97-110. IEEE, 2021.
Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, James Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien- Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, and Kim Hazelwood. Sustainable ai: Environmental implications, challenges and opportunities, 2022. URL https: //axivv. org/abs/2114 00364.
Mengwei Xu, Wangsong Yin, Dongqi Cai, Rongjie Yi, Daliang Xu, Qipeng Wang, Bingyang Wu, Yihao Zhao, Chen Yang, Shihe Wang, Qiyang Zhang, Zhenyan Lu, Li Zhang, Shangguang Wang, Yuanchun Li, Yunxin Liu, Xin Jin, and Xuanzhe Liu. A survey of resource-efficien Ilm and multimodal foundation models, 2024. URL https: / /ttp///axvvv///////a///////aaava arg/abs/2401. 08092.
An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng, Dayiheng Liu, Fan Zhou, Fei Huang, Feng Hu, Hao Ge, Haoran Wei, Huan Lin, Jialong Tang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jing Zhou, Jingren Zhou, Junyang Lin, Kai Dang, Keqin Bao, Kexin Yang, Le Yu, Lianghao Deng, Mei Li, Mingfeng Xue, Mingze Li, Pei Zhang, Peng Wang, Qin Zhu, Rui Men, Ruize Gao, Shixuan Lüu, Shuang Luo, Tianhao Li, Tianyi Tang, Wenbiao Yin, Xingzhang Ren, Xinyu Wang, Xinyu Zhang, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yinger Zhang, Yu Wan, Yugiong Liu, Zekun Wang, Zeyu Cui, Zhenru Zhang, Zhipeng Zhou, and Zihan Qiu. Qwen3 technical report, 2025. URL https: //arxiv. org http:/abs2555500950 09388.
Zeping Yu and Sophia Ananiadou. Interpreting arithmetic mechanism in large language models through comparative neuron analysis. arXiv preprint arXin:2409.14444, 2024.
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. Hellaswag: Can a machine really finish your sentence? arXiv preprint XrXi::1905. 07830, 2019.
Zhekai Zhang, Hanrui Wang, Song Han, and William J Dally. Sparch: Efficient architecture for sparse matrix multiplication. In 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 264-274. IEEE, 2020.
Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou. instruction-folloiing evaluation for large language models, 2023. URL https://arxiv. /abs/
Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian McAuley, Ke Xu, and Furu Wei. Bert loses patience: Fast and robust inference with early exit, 2020. URL https://arxiv. www.riiv.rgg/bba///e wwwp////g//.......///////////oop 04152.
14

---

Published in Transactions on Machine Learning Research (01/2026)
A Theoretical Analysis of Layer Parallelism
This section provides theoretical justification for Layer Parallelism (LP), analyzing the approximation error introduced by the LP computational graph and connecting it to the empirical observations in the main paper.
A.1 Sequential vs. Layer-Paralles Computation
Consider two consecutive transformer decoder layers & and b in pre-norm architecture. Let Ax()) denote the attention residual and F+(+) the feed-forward residual of layer k.
Exact scquential computation. The standard forward pass computes:
ug x + Ax(2),
(2)
hg us Fx(Ux),
Up+1 he AR+1 (hw);
hx+1 = us+1 + Fi+ (ux+1),
where hx+1 (2) is the two-layer sequential output.
Layer Parallelism computation. LP evaluates both attention modules at the shared input æ, combines their oulputs, and feeds the result to both FFN modules:
û = x + Ax(x) 1 TLP(x) = u + Fi( Fk)1
(6)
(7)
This matches the (LP) equation in $3 and the implementation in Fig. 2(b), where divergent paths share intermediate states.
A.2 First-Order Error Analysis
The approximation error E(x) = Tseq (z) - (z)(x) arises from evaluating submodules at different inputs. We decompose this error into three components.
Component Attention evaluation shift. the sequential computation, Ag+1 is evaluated h x Ax(x) + Fk (ux), whereas LP evaluates it Defining A h4 As(x) Fiu),, a first-order Taylor expansion yields:
Ak+1 (bx) Ast1(x) (2)
where JAzti (x) the Jacobian of A++1 at x.
Component First FEN evaluation shift. In the sequential computation, Fş is evaluated at ug An(x), whereas LP evaluates it at x (x) (x)) Since à uh A
Fa(UK) FL( = Jr, (mx) Ast1(x).
(9)
Component 3: Second FFN evaluation shift. In the sequential computation, F&+1 is evaluated at "k+1 by + A (hx), whereas I.P evaluates at û. The difference
d - -= Fx(4) + [hx+1(h)) - Axt1(x)]-
(10)
Substituting the first-order approximation from (8):
( (ū) Jr (ū) [Fx (ux) + JA (2)) A1].
(11)
15

---

Published in Transactions on Machine Learning Research (01/2026)
Total error bound.
Combining equations (8)-(11) and taking norms:
[|(())] x A |11l + Jrr [ || Fa(x))) + || ||
(12)
attention shift
FFN: shift
PFN&+1 shift
where Jacobian norms are operator norms evaluated at the appropriate inputs (suppressed for clarity).
A.3 Implications for Layer Selection
The error bound (12) reveals when LP introduces minimal degradation:
Small residual updates favor LP. The bound depends on || = [1 + (ux)))l: (x)]l, and [ux]]] Layers where attention and FFN residuals are small relative to the residual stream contribute less error. This consistent with the "residual stream dominance" phenomenon obscrvod deep transformers.
Low Jacobian sensitivity favors LP. The terms || and [| [ measure how sensitive each submodule is to imput perturbations. Layers that are relatively insensitive their exact input introduce less LP error.
Connection to CKA analysis (Fig. 5). The CKA similarity between standard and counterfactual activations (with residual removed) serves as an empirical proxy for the Jacobian sensitivity. High CKA similarity indicates that the module output relatively invariant to the upstream residual- precisely the condition under which the Jacobian terms (12) are effectively small in the directions that matter. The plateau of high CKA values in mid-to-late layers (Fig. 5) corresponds to the region where LP is most effective (Fig. 4e).
Error accumulation through the network. Errors injected at layer k propagate through all subsequent layers via the Jacobian chain:
[ouupputl || S
|&&ll II 114 Jelb
(13)
E>E
where ft is the full residual map of layer L. This implies:
Avoid early layers: Errors introduced early are amplified by many subsequent Jacobians.
Preserve sequential tail: The final layers before the output logits are typically most sensitive (the language modeling head amplifies perturbations), leaving them sequential stabilizes the output distribution.
A.4 Why Contiguous 2-Parallel Works Best
The "2-parallel" scheme (parallelizing consecutive pairs rather than arbitrary groups) succeeds because:
Local ordering matters less than global ordering. Shuffling experiments (Fig. 4a) show that adjacent layers are more interchangeable than distant ones, likely because they operate at similar levels of abstraction.
2. Error terms remain bounded. Within single LP pair, the error is first-order in residual magnitudes. Chaining n LP pairs gives n independent first-order errors rather than a single large error from parallelizing all 2n layers simultaneously.
3. Intermediate synchronization corrects drift. Between LP pairs, the outputs are summed and re-normalized,, preventing error accumulation within the LP region.
This explains why parallelizing triplets performs worse (as noted in $3): the second-order cross-terms become significant, and there intermediate synchronization correct the trajectory.
16

---

Published in Transactions on Machine Learning Research (01/2026)
A.5 Connection to GSM-8K Degradation
The disproportionate drop in GSM-8K accuracy under LP (Table 1) is consistent with recent findings that mathematical reasoning relies on sparse, localized circuits (Stolfo et al., 2023; Christ et al., 2025). These circuits likely have:
Larger effective Jacobians in the relevant directions (high sensitivity to precise intermediate stales).
Less redundancy, so the LP approximation error is not absorbed by parallel pathways.
General language competence, by contrast, is distributed across many redundant pathways and is therefore more robust. to the input perturbations introduced by T.P.
17

---

Published in Transactions on Machine Learning Research (01/2026)
B Comparison with Other Tensor Parallelism Optimizations
In this section, we compare Layer Parallelism (LP) with recent methods that aim to reduce communication overhead in lensor-parallel LLM inference: Sync-Point Drop (SPD) (Kim et al., 2025), selective low-bit communication (Dong et al., 2024), and microscaling (MX) format compression (Hansen-Paa) Pallms et al., 2024).
B.1 Method Overview
Sync-Point Drop (SPD). Kim et al. (2025) selectively removes the all-reduce synchronization after the self-attention output projection, retaining only the FFN synchronization per block. They introduce modified block designs to minimize information loss and classify blocks into three sensitivity categories (in-sensitive, sensitive, extremely sensitive), applying block-to-block distillation to recover accuracy in sensitive layers.
Selective Low-bit Communication. Dong et al. (2024) compress communicated activations by quan- tizing most features to INT4 while keeping a small fraction (1/64) of high-range outlier features in BF16. This reduces communication from 16 bits to ~4.2 bits per value on average, preserving outlier information critical for model performance.
MX Format Compression. Hansen-Palman et al. (2024) apply microscaling (MX) quantization formats (FP4/FP5 with block-wise scaling) to compress activations before inter-device communication, achieving 3.5-4.5x compression ratios.
Layer Parallelism (Ours). LP restructures the computational graph to execute consecutive layer pairs in parallel, reducing the number of sequential synchronization points. Unlike quantization-based methods, LP modifics the computation order rather than the communication encoding.
B.2 Quantitative Comparison
Table 3 compares results across methods. Direct comparison is challenging due to different models, hardware configurations, and evaluation metrics; we match the closest available configurations.
Table 3: Comparison of tensor parallelism optimization methods. Speedup relative to standard tensor parallelism. For accuracy: SPD and LP report average zero-shot accuracy change; Low-bit reports perfor- mance retention; MX reports perplexity increase. Results marked are at 70% SPD application; f indicates hardware-dopendent results on PCle-connccted GPUs (L4); indicates NVLink-conneeted GPUs (A100).
Method
Model
GPUs
Speedup Accuracy Impact Mechanism
7B scase Models
SPDt
LLaMA2-7B
8
1.10x
11.0 avg acc
Drop attn sync Quant. comm
Low-bit
LLaMA2-13B
8
99.5% retained
MX Compress
LLaMA2-7B
21
1.08x
+3.2% PPL
Quant. comm
LP (Ours)
LLaMA3.2-3B
2
1.19x
2.5% avg acc
Parallel layers
13B-scale Models
SPDt
LLAMA2-13B
8
1.12x
1.0% avg acc
Drop attn syne Quant. comm
Low-bit
LLAMA2-13B
8
99.5% retained
MX Compress
LLaMA2-18B
41
2.05x
+3.2% PPL
Quant. comm
LP (Ours)
Qwen3-14B
2
1.15x
4.0% avg
Parallel layers
70B-scal Models
SPD
LLaMA2-70B
8
1.20x
0.9% avg acc
Drop attn sync Quant. comm Quant. comm
MX Compress
I.IaMA2-70B
gt
1.83 2.08x
+1.7% PPT.
MX Compress
LLaMA2-70B
48
70x00.70x
+1.7% PPL
Table provides detailed accuracy comparison on common benchmarks where available.
18

---

Published in Transactions on Machine Learning Research (01/2026)
Table 4: Accuracy comparison on zero-shot benchmarks. Values show absolute accuracy (%) or relative change from baseline. SPD results use ZS-B2B configuration 70% SPD; Low-bit uses INT4+Sclected BF16 at 4.2 bits.
Method
Model
ARC-e ARC-c HellaSwag WinoGrande
Avg
Baseline (no optimization)
LLaMA2-13B
79.5
48.7
60.0
72.2
65.1
Gemma 2 27B
87.7
62.4
65.4
79.1
73.7
Low-bit Communicatiio (Dong al., 2024)
Sel. BF16
LLMMA2-33B
79.1
47.4
59.5
72.9
64.7
(-0.5%)
(-2.8%
(0..9%)
(+1.0%)
(-0.6%)
INT BF16
Gemma 2 27B
86.5
61.0
63.9
76.5
72.0
1.44%
( (-2.2%)
2.3%)
(-3.3%)
2.3%%
SPD (Kim et ol,, 2025) at 70% blocks
BSBBBB
LLaMA2-13B (8-GPU)
~65%
(<11% drop)
BS+B2B
HaMA2-70B (8-GPU)
666
(-0.9%)
B.3 Key Observations
Complementary approaches. The methods target different bottlenecks: LP and SPD reduce the number of synchronization points, while low-bit and MX compression reduce the size of each synchronization. These are largely orthogonal and could be combined- for instance, applying LP to reduce syne-points by 50%, then using INT4 quantization to compress remaining communications by 4x.
Hardware sensitivity. Communication compression methods show strong hardware dependence. Hansen- Palmus et al. (2024) report 2x TTFT speedup on PCle-connected L4 GPUs (64 GB/s bandwidth) but slowdown on NVLink-connocted A100s (600 GB/s bandwidth) due to quantization overhead exceeding com- munication savings. In contrast, LP and SPD benefit from reduced syuc-points regardless of interconnect speed, though gains are more pronounced on slower interconnects.
Model size scaling. Both SPD and LP show improved robustness on larger models:
SPD: 44% of blocks are in-sensitive in LLaMA2-7B 75% in LLaMA2-70B (Kim et al., 2025) LP: Larger models (Qwen3-14B) tolerate greater effective depth reduction than smaller models (LLaMA3.2-3B) before sharp accuracy drops (see Table 1)
Tradc-off charactoristics. Each method exhibits distinct accuracy-speod tradc-offs:
SPD: Graceful degradation; requires per-block sensitivity analysis and optional distillation
Low-bit: Minimal degradation (~0.5 2%) by preserving outlier features; no speedup measured
MX Compress: Perplexity increases 1-3%; speedup highly hardware-dependent
LP (Ours): Uniform accuracy drop across layers; GSM-8K disproportionately affected; fine-tuning
recovers 550% of loss
B.4 Combining LP with Communication Compression
A promising direction is combining LP with quantization-basel compression. Table 5 estimates potential combined benefits.
We leave empirical validation of combined LP + quantization to future work.
19

---

Published in Transactions on Machine Learning Research (01/2026)
Table 5: Estimated combined speedup from LP + communication quantization on banddwiddt--constraineed
hardware. LP reduces sync-points; INT4/MX4 reduces per-syne data volume by ~4x.
Optimization
Syne Reduction
Comm. Compression
Est. Speedup
Baseline (TP)
0% 25 50%
1x
1.0x
LP only
1x
1.15 1.25x
INT4/MX4 only LP + INT4/MX4
0%
4x
1.5 2.0%
25-50%
4x
1.8-2.5x
C
Ablation:
Tokens per second
KV Cache Prefill Llama-2-2
KV Cache Prefill LIama-3.2-3B
3093
1431
Tokensis
Tokensis
2003
2833
54B
2009
30.0
4000
1000
1300
20
2007
Sequence Length
Sequence Length
Autoregressive Llama-2-7b
Autoregressive Lamm-332-3BB
42.5
Talcens/S
2013
J033
00.00
320
22:00
1340
20
200
Sequence Length
Sequence Length
Token Generation Llama-2-7b
1 Token Generation Llama-3.2-22
21
Tokensi5
03-
Tokensis
Bundre
82-
23
Sequence Length
Sequence Length
Figure 9: Tokens per second when completing the following inference tasks: KV Cache pre-filling for a given sequence length, autoregressive generation up to the indicated sequence length, and single token generation with a pre-filled KV Cache the indicated sequence length. The baseline is the original model with all layers making use of Tensor Parallelism. The Parallel Layers number (A) indicates how many layers have been merged using Layer Parallelism (e.g. a A of indicates that groups of layers have been converted to 2 effective layers). The number of tokens is computed as the sum of the input tokens and the output tokens for each forward pass.
20

---

Published in Transactions on Machine Learning Research (01/2026)
D
Generalization to multiple GPUs
GPU
Layer k Layer k +
01 E RTxD
E RTXx
E RTTD
K
Self att.
all
E RTxD
03 E RTXD
E RTXD
Figure 10: Layer Parallelism in the case parallelizing two layers over four accelerators. The stacked layers represent the tensor parallelism, and the colors indicate the processing of different previously contiguous layers. Q, K, V,att E IRTx _ where is the feature dimension and is the total number of accelerators.
Layer Parallelism allows one allocate N accelerators for each layer. The implementation remains the same, but now each layer is parallelized using tensor parallclism over its assigned accelerators. Note that both reduction operations (lensor parallel and layer parallel) are nicely executed with single all-reduce call.
To confirm that the proposed scheme scales to commodity 4-GPU servers, we benchmark Liama 2 7B on a node with 4x NVIDIA A100 80 GB PCIe accelerators while running the 4-GPU LP implementation. For each configuration we measure the wall-clock time the decoding workload, normalize it to the vanilla A setting, and report the resulting relative throughput in Table 6. The gains steadily increase as we parallelize more layers (e.g., A 23 reaches 1.46x), illustrating the benefit of halving the number of synchronization steps per block; however, these aggressive settings correspond to the larger accuracy drops discussed in the main text, so moderate A values offer better accuracy-spedd trade-off.
Table 6: Relative throughput of 4-GPU Layer Parallelism on Llama 2 7B measured on 4x A100 80 GB PCIe GPUs. A denotes the number of layers replaced by LP pairs, with A serving as the bascline (z1.00).
Rel. Speed
0
x1.00
10
x1.24
14
x1.34
18
x1.46
21

---

Published in Transactions on Machine Learning Research (01/2026)
E
Acceleration source
* pph 0PU stheam
5team 17
(a) Flame chart of running two standard Parsolll LLama decoder layers.
sutied gur 8PU1
stream? 7
stream
Festall Seteee westawefe
(b) Flame chart of running two Llama decoder layers with our Layer Parallelism approach.
Figure 11: Comparison of Flame Graphs when running two consocutive Llama 3.2 3B decoder layers with vanilla tensor parallelism (Fig. Ila), and our Layer Parallelism approach (Fig. 11b). Note that the time axis scale different between both graphs. These results were obtained on a workstation using x2 RTX 4090s.
Figure 11 illustrates flame graphs comparing two consecutive Llama 3.2 3B decoder layers using vanilla tensor parallelism and our LP approach. The profiling data summarized in Table 7 reveals that the primary source of acceleration in our LP method stems from reducing the total number of all-reduce synchroniza- tion operations across GPUs. Specifically, the vanilla tensor parallel approach performs synchronization at every decoder layer, resulting higher cumulative synchronization overhead due the fixed latency costs. Size-independent latency is dominated by GPU kernel-launch overhead, per-hop interconnect round-trip la- tency and /sst/am synchronization imposed by the calling context. In contrast, our Layer Parallelism implementation runs pairs of layers simultaneously, effectively halving the number of synchronization points. This reduction in synchronization leads to significant drop in synchronization time from 100.8ms to 50.7ms, directly contributing to the observed improvement in inference speed.
Additionally, Layer Parallelism enables fusion of certain computation kernels paanels-particularry attention and MLP operations across parallelized fayers-which further marginally reduces the computation time from 217ms to 208.7ms. Although these computational gains are modest compared to the savings achieved through fewer synchronization operations, kernel fusion further optimizes hardware utilization and enhances overall throughput.
Table 7: Profiling results comparing vanilla Tensor Parallel and Layer Parallel implementations on two consocutive Llama 3.2 3B decoder layers.
Approach
Total Time (ms)
Sync Time (ms)
Computation Time (ms)
Tensor Parallel
317.8
100.8 50.7 (x1.99)
217.0 208.7 (x1.04)
Layer Parallel (Ours)
259.4 (x1.23)
22


Reviews:
[{"review_id": "8zg85gkKD3", "reviewer": "Reviewer_vws1", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper proposes Layer Parallelism (LP), a method to accelerate large language model inference by exploiting the weak dependency between consecutive Transformer layers. Below are my reviews of this paper:\n\n### **Strengths**\n\n1. **Well-motivated and novel idea.**  \n   The paper provides an interesting perspective: when shuffling the middle layers of a Transformer, the model’s perplexity only increases slightly (by 2.9), while other operations such as pruning, merging, or removing layers lead to significant performance degradation.  \n   This observation supports the existence of **layer decoupling**, inspiring the authors to propose **parallel computation across adjacent Transformer layers** to improve efficiency.  \n   I like this idea—it is simple, intuitive, and can be directly applied to existing large models **without retraining**.\n\n2. **Clear writing and organization.**  \n   The paper is well written and easy to follow. The *Limitations* section clearly acknowledges the constraints and open problems of the proposed method.\n\n3. **Comprehensive experiments.**  \n   The authors evaluate the method on multiple open LLM families (Llama2, Llama3.2, Qwen3), examining both **perplexity trends** and **In-Context Learning (ICL)** performance.  \n\n\n### **Weaknesses**\n\n1. **Lack of theoretical foundation for “Layer Decoupling” and parallel computation.**  \n   Although the empirical results suggest that intermediate layers are loosely coupled, the paper offers **no theoretical explanation** for this phenomenon.  \n   In addition, the transition from Equation (1) to Equation (2) is an **approximation**—although it empirically works but without theoretical grounding (e.g., representation similarity analysis, information flow metrics), the claim remains observational rather than principled, limiting its generalizability.  \n\n2. **Some experimental results are not very convincing.**  \n   For example, in Table 2, Qwen3-4B with an effective depth of 27 shows a **large performance drop** (from 70 to 44 average accuracy).  \n   Even after 8 K fine-tuning steps, the model only recovers to 61.34, still far from the baseline.  \n   It would be important to discuss **whether the efficiency gain justifies such degradation**—i.e., is the trade-off between speed-up and accuracy worthwhile?\n\n3. **Lack of comparison with other tensor-parallel or efficiency methods.**  \n   The paper does not compare LP with existing tensor-parallel optimizations [1][2][3].  \n   In the *Impact on Inference Speed* section, LP achieves 1.29× (Llama 2 7B), 1.22× (Llama 3.2 3B), and up to 1.35–1.38× speed-up for larger Δ, but it is unclear how much accuracy is lost for each setting.  \n   Since LP inevitably introduces performance degradation, a **direct trade-off comparison** with prior efficiency-improvement methods would strengthen the paper’s contribution.\n\n[1] Hansen-Palmus, Jan, et al. \"Communication Compression for Tensor Parallel LLM Inference.\" arXiv preprint arXiv:2411.09510 (2024).\n[2] Kim, Han-Byul, et al. \"SPD: Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models.\" arXiv preprint arXiv:2502.20727 (2025).\n[3] Dong, Harry, et al. \"Towards low-bit communication for tensor parallel llm inference.\" arXiv preprint arXiv:2411.07942 (2024).", "claims_and_evidence_yn": "No", "claims_explanation": "Although the authors claim that layer parallelism improves inference speed, the trade-off between the speed gain and the degradation of model performance remains unclear. The experimental section does not provide a detailed correspondence between inference speed improvements and the extent of performance loss.", "audience_yn": "Yes", "audience_explanation": "This is a paper about improving the inference efficiency of large language models.", "requested_changes": "Provide theoretical justification for the PAR approximation.\nThe transition from Equation (1) to Equation (2) is currently supported only by empirical observations. The authors could strengthen their argument by analyzing layer similarity (e.g., CKA/SVCCA), Jacobian alignment, or mutual information between adjacent layers to demonstrate that their outputs are indeed loosely coupled. A theoretical or analytical framework would make the layer-parallel assumption more principled and generalizable.\n\nQuantify the trade-off between inference speed and accuracy degradation.\nWhile the paper reports overall speed-up ratios (e.g., 1.29×, 1.22×, 1.35×), it does not explicitly link these gains to the corresponding performance drops. Presenting a clear speed–accuracy curve or table showing how inference efficiency scales with accuracy loss would greatly enhance the interpretability and practical relevance of the results.\n\nInclude comparisons with other efficiency-oriented methods.\nTo better contextualize the contribution, the authors should compare LP against existing tensor-parallel or inference acceleration techniques (e.g., DeepSpeed-Inference, FasterTransformer, or attention–FFN fusion strategies). Such comparisons would clarify the unique advantages and limitations of LP.", "broader_impact_concerns": "None", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "865IVgz78E", "reviewer": "Reviewer_3x6E", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper proposes a new technique for speeding up LLM inference by exploiting hidden redundancy in transformer architectures, which they call Layer Parallelism (LP). The core idea is that several adjacent transformer layers behave independently so the model can run pairs of consecutive layers in parallel instead of processing them strictly one after another, without retraining. By reorganizing the computational graph to enable this parallel execution of consecutive layer pairs through tensor parallelism, the authors achieve significant inference speedup with minimal performance degradation. \n\nStrengths:\n- LP introduces a new dimension of parallelism - across consecutive layers. This had not been explored in previous works. \n- This method works on existing models trained on the transformers architecture, without retraining, and leverages tensor parallelism\n- It opens up a new area for exploration - how tensor parallelism in transformers can be further optimized, especially in distributed inference\n\nWeaknesses:\n- The paper lacks a theoretical justification for why certain layers can be safely grouped, although it is supported empirically\n- Benchmark selection is broad but could also include code generation, long-context, multi-turn dialogue tasks to truly test sequential depth\n- The authors test LP on decoder-only architectures, so it’s unclear whether similar redundancy patterns exist in encoder-decoder or multimodal architectures", "claims_and_evidence_yn": "Yes", "claims_explanation": "Mostly, yes. Empirical claims about speedups and the existence of a usable accuracy/speed tradeoff are well-supported. The paper provides convincing wall-clock and benchmark evidence that this method can speed inference on real models without retraining, and that light fine-tuning helps recover quality. However, the broader claims about general applicability, why the effect occurs, and how to choose layers automatically are not yet fully supported.", "audience_yn": "Yes", "audience_explanation": "Yes. The paper is likely to be of interest to the TMLR audience, especially those interested in LLM efficiency and architecture optimization.", "requested_changes": "Major - \n1. Currently, the assumption of partial independence between layers is only supported empirically. Adding a lightweight analysis, such as activation correlation between layers, or attention pattern similarity could strengthen the paper in my view.\n\nMinor - \n1. For reproducibility, it would be good to report mean and standard deviation across multiple runs rather than single-run metrics reported in the paper \n2. In addition to the wall-clock time, it would be good to see energy usage, GPU utilization, and/or memory usage to understand cost saving better", "broader_impact_concerns": "n/a", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "xUPnsEUV5S", "reviewer": "Reviewer_kBxv", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper explores how the computational graph of trained LLMs can be modified to run inference more efficiently on two GPUs.\n\nThe authors discovered that two consecutive transformer layers can be run in parallel (summing their activations), and running multiple pairs of layers in parallel impacts model quality only to a small degree, while decreasing inference time by reducing communication between the GPUs.\n\nLlama and Qwen models ranging from 3B to 14B are investigated. In some cases with more pronounced performance degradation, fine-tuning part of the network can recover part of the difference.", "claims_and_evidence_yn": "No", "claims_explanation": "I think that the broad claim \"This approach, requiring no retraining, boosts inference throughput by 1.05x–1.20x while maintaining 95-99% of the original model’s accuracy on standard benchmarks.\" requires more evidence than what is provided in the paper. The paper only examines two models at a limited scale on few benchmarks, and inference speedup is only demonstrated in a single setup.", "audience_yn": "Yes", "audience_explanation": "It is an interesting observation that transformer layers of a trained LLM can be run in parallel, even though these networks have been trained with layers applied strictly sequentially. These networks are widely used, and new approaches to reduce the inference compute load have a potential for a large impact.", "requested_changes": "1. For better support of the central claim of the abstract, I would like to see the study extended in (some of) the following ways:\n\n   a. Better display of the existing data – this could be improved by adding (or extending) a table to show the following numbers in a single place:  relative reduction of effective depth, relative reduction of metrics (with some estimate of variance), and relative speedup.\n\n    b. How were the benchmarks from Table 1 chosen (the benchmarks partially overlap with the ones from [Llama 2](https://arxiv.org/abs/2307.09288), with slightly different numbers, but the benchmarks reported in [Qwen 3](https://arxiv.org/abs/2505.09388) are quite different)? Why are GSM8K and ifeval not included for the first two models?\n\n    c. Hoes does layer parallelism perform with 1 GPU? How does it perform with 4 GPUs?\n\n    d. How does layer parallelism perform with larger models? Does it work with MOEs?\n\n    e. By how much does fine-tuning improve the results with models other than Qwen3 4B?\n\n2. Appendix Section C states \"that the primary source of acceleration in our LP method stems from reducing the total number of all-reduce synchronization operations across GPUs\". Since this is of central importance for the approach, I would like to see more detailed explanations as on the exact mechanism of this acceleration. It seems that running layers in parallel reduces the number of synchronization points, but increases the data transferred per synchronization by the same factor.\n\n3. For better readability, I would propose to add a third formula after (2, PAR) that defines the exact computation that was used for the reported results. Figure 2b should reference that formula (and maybe even moved out of the  \"Related work\" section).", "broader_impact_concerns": "I don't see any ethical implications specific to layer parallelism that would require adding a Broader Impact Statement to the paper.", "additional_comments": "1. Section 4 mentions \"LP Multi-Head Attention\", but the paragraph text and Figure 5 do not account for multiple heads.", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)