primitive-ai/Nex-N2.5-mini-mixed-NVFP4-FP8

Six of forty expert layers at 8 bits, chosen by what actually moves the output.

Mixed-precision quantization of nex-agi/Nex-N2.5-mini to 26.04 GB, on one 96 GB card.
34 of the 40 routed-expert layers at NVFP4 and six at FP8; attention, linear attention, shared experts, embeddings and the vision tower stay BF16.

Three builds, one measurement. We quantized this model's experts three ways and measured all of them against the BF16 original under one protocol: this one, an NVFP4 build at 23.91 GB, an FP8 build at 38.13 GB, and this one. On accuracy the four are one band, and this card says so rather than claiming a win the measurement does not support. Where this build does differ is in how far its representations sit from the BF16 original, and in landing between the two endpoints on both size and speed.

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Why this quant

• 🔬 The six FP8 layers were measured, not guessed. Every layer was scored by running the real model on tool-call prompts with all experts at NVFP4, upgrading one layer at a time to FP8, and seeing how far the final hidden state moved back toward BF16. Layers 7, 13, 18, 23, 29 and 35 won.
• 📉 Closest to the original of the three quantized builds. Those six layers remove 19.7% of the representation drift that 4-bit quantization introduces, for six layers of forty.
• 🎯 Accuracy is level with BF16, like every build here. 90.6 knowledge and 76.5 tool-calling against the original's 89.6 and 75.3.
• ⚖️ Between the endpoints on both axes. 26.04 GB against 23.91 and 38.13; 147 tok/s single-stream and 990 at concurrency 32, in both cases between the 4-bit and 8-bit builds.
• 🧩 Stock vLLM, no patches. vLLM resolves the compressed-tensors scheme per MoE layer and runs both kernels in one model: FlashInfer CUTLASS for the NVFP4 layers, Triton FP8 for the other six.

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Serve it

hf download primitive-ai/Nex-N2.5-mini-mixed-NVFP4-FP8 --local-dir ./Nex-N2.5-mini-mixed-NVFP4-FP8

docker run --gpus all --ipc=host -p 8000:8000 -v $PWD:/models \
  vllm/vllm-openai:v0.28.0 \
  --model /models/Nex-N2.5-mini-mixed-NVFP4-FP8 \
  --max-model-len 32768 --gpu-memory-utilization 0.92 \
  --enable-auto-tool-choice --tool-call-parser qwen3_coder \
  --reasoning-parser qwen3

Qwen3_5MoeForConditionalGeneration is in the 0.28.0 release, so no nightly is needed. The numbers on this page were measured on 0.28.1rc1.dev472. We booted this build on 0.28.0 as well: 181 s to healthy, both MoE kernels selected as expected, generation sane.

Two things about this model's chat template are worth knowing before you wire it up. It emits an empty thinking block by default, so replies arrive with two leading newlines and the reasoning field holds only whitespace; a short correct answer looks like "\n\n4". And reasoning_effort (low / medium / xhigh) is a per-request field here, unlike some other reasoning models where it is fixed at server start.

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Measured

One RTX PRO 6000 Blackwell, 96 GB, one card. The 1,170-item knowledge suite and the 200-item tool-calling suite, temperature 0.6 / top_p 0.95 / top_k 20, thinking on, a 16,384-token budget, concurrency 32, auto-scored with no LLM judge. Throughput is 8K in / 512 out, prefix-cache free, two seeds per cell. Every row below was run on the same box in the same sitting.

build	size	knowledge	tool-calling	call	abstain	finished	tok/s @1	tok/s @32
BF16 original	70.24 GB	89.6	75.3	79.7	58.7	99.7%	141.0	664
FP8 experts	38.13 GB	90.9	75.5	79.4	60.0	99.5%	163.8	901
this repo, mixed	26.04 GB	90.6	76.5	80.9	58.7	99.2%	147.2	990
NVFP4	23.91 GB	90.5	77.0	81.9	57.5	99.3%	145.3	1019

Tool-calling is the mean of two runs per build (three for BF16). Pooled within-build spread is 0.60 standard deviations over those nine runs, and the four build means span 1.7, so the column is one band. Read the ordering with that in mind: it runs opposite to precision, which no mechanism would predict, and is the clearest evidence that this suite cannot resolve the difference between 4-bit and 16-bit experts on this model.

Speed does separate, and it crosses over. FP8 experts decode 13% faster single-stream because the 4-bit path pays for activation quantization and group scales on every token, a cost that does not amortize at batch 1. At concurrency 32 the ranking inverts and NVFP4 wins by 13%, because there the bottleneck is weight bandwidth and 4-bit moves half the bytes. Pick by the concurrency you actually run.

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Comparable with our other models

Accuracy numbers move for reasons that have nothing to do with the model: a shorter token budget, a different temperature, or whether the model was allowed to reason at all. So every number in this table, on this card and on our other cards, comes from the one fixed protocol described above, the same 1,370 items, auto-scored, no LLM judge.

model	shape	size	overall	knowledge	call	abstain	finished	out/answer
Laguna-XS-2.1	31 B MoE	19.3 GiB	81.7	83.8	68.4	73.5	98.9%	1097
Nemotron-3.5-Lightning-30B-A3B	30 B MoE+Mamba	19.2 GiB	87.1	87.9	85.4	70.5	97.9%	1429
Ornith-1.5-35B-A3B	35 B MoE	22.6 GiB	88.7	91.7	74.4	60.0	99.3%	760
Muse-Glimmer-30B	30 B MoE	20.4 GiB	86.6	88.8	78.6	54.5	99.7%	800
Qwen3.8-27B	27 B dense	20.7 GiB	88.8	90.4	85.5	54.5	99.7%	651
Granite-4.2-30B	30 B dense	18.1 GB	85.5	86.2	85.8	60.8	98.5%	1502
Nex-N2.5-mini NVFP4	35 B MoE, 3 B active	23.91 GB	88.5	90.5	81.9	57.5	99.3%	524
Nex-N2.5-mini mixed (this repo)	35 B MoE, 3 B active	26.04 GB	88.5	90.6	80.9	58.7	99.2%	504
Nex-N2.5-mini FP8	35 B MoE, 3 B active	38.13 GB	88.7	90.9	79.4	60.0	99.5%	545
K2-Horizon-MoVA-36B-A4B NVFP4	37 B MoE+MoVA, 4 B active	36.7 GB	84.1	86.5	71.8	60.4	95.6%	1234
K2-Horizon-MoVA-36B-A4B mixed	37 B MoE+MoVA, 4 B active	44.5 GB	84.9	87.3	73.5	58.9	96.3%	1118
Laguna-S-2.1	110 B MoE	64.0 GiB	84.3	87.1	64.6	81.0	97.3%	995
Qwen3.8-Flash-Next	180 B MoE, 6 B active	183.7 GB	90.3	92.2	84.8	56.7	99.5%	686

overall pools the two suites as 1,370 items, weighted 85.4% knowledge and 14.6% tool calling by item count. Read it with finished: overall scores an answer that overran the token budget as wrong, and cannot say whether the model needed the room or failed to stop. A gap under 1.0 in overall is a tie. Sizes are as each card reports them, which mixes GB and GiB.

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How the six layers were chosen

A layer's own quantization error turns out to be a poor guide. Measured in isolation, every layer of this model is distorted by about the same fraction at 4 bits: 2.39% on average, 1.48% to 2.64% across all 40. Nothing in that spread tells you which layers to protect.

So the selection was made on the model instead of on the tensors. With all 40 layers at NVFP4, one layer at a time was upgraded to FP8 inside the running engine, and the final hidden state was compared against the BF16 original over 128 tool-call prompts:

gain(l) = || all-NVFP4 - BF16 ||  -  || all-NVFP4 except l at FP8 - BF16 ||

Layers 7, 13, 18, 23, 29 and 35 came out on top, and together they remove 19.7% of the drift that 4-bit quantization introduces. The measurement is deterministic: three re-runs on identical weights moved the result by 0.00000, and two independent passes agreed to the last decimal.

Two results from that sweep are worth stating because they contradict the obvious approach:

• Upgrading a layer can make the model worse. On 25 of the 40 layers, moving that layer from NVFP4 to FP8 pushed the final hidden state further from BF16, by as much as 19% of the total gap. Quantization errors partially cancel across layers, and improving one layer in isolation can break a cancellation. Any selection method that scores layers independently is blind to this.
• Local error picks the wrong layers. Ranking by each layer's own block-output error selects the top of the stack (32, 35 through 39), which by the end-to-end measure removes only 5.5% of the gap, a third of what the layers above achieve for the same six layers. Calibrating that local criterion on tool-call prompts alone rather than a general mix reproduced the same six layers exactly, so the problem is the criterion, not the calibration data.

What the allocation does not do is move the benchmark. All four builds score inside one band, and this card's table says so.

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What's quantized to what

Nex-N2.5-mini is 35.11 B parameters, and they are distributed very unevenly:

	params	share
routed experts, 40 layers × 256 × (gate+up+down)	32.21 B	91.8%
linear attention (Gated DeltaNet), 30 layers	1.01 B	2.9%
embed_tokens and lm_head, untied, vocab 248320	1.02 B	2.9%
vision tower, 27 blocks	0.45 B	1.3%
full attention, 10 layers	0.27 B	0.8%
shared experts, routers, norms	0.15 B	0.4%

tensors	count	format
routed experts, 34 layers	26,112 modules	NVFP4, group 16
routed experts, layers 7 / 13 / 18 / 23 / 29 / 35	4,608 modules	FP8 E4M3, per output channel
everything else	946 tensors	BF16, byte-identical to the source

compressed-tensors, format mixed-precision, two config groups whose targets name specific layers. vLLM resolves the scheme per MoE layer, so interleaving is fine, but all three projections of one layer must agree, which is why the unit of allocation is a whole layer. The source ships experts as two batched 3-D tensors per layer; they are unfolded into per-expert modules, which is the layout vLLM's loader consumes. gate and up share one weight_global_scale per expert. vLLM fuses those two halves and keeps a single scale, warning and taking the maximum when they disagree, which silently shrinks half the weights, so the build is audited for that before release: 20,480 fused groups checked, no mismatches.

Weights-only round-to-nearest, no calibration. Nothing in the quantization is fitted to data.

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