HawkBearPig/GLM-5.3-Int4-Int8Mix-RTN-g64

GLM-5.3 Int4/Int8 mixed-precision round-to-nearest, group 64

A mixed-precision quantization of GLM-5.3 (754B-parameter MoE, model type glm_moe_dsa) made from the original BF16 release (zai-org/GLM-5.3-BF16, not the derived FP8 checkpoint). Built for the DGPP inference engine on four NVIDIA DGX Sparks (TP=4), where the whole model has to fit in ~100 GiB per rank with room left for context.

Total size	398 GiB (≈ 99.8 GiB per rank at TP=4)
Routed experts, layers 3–77 (mlp.experts.*.{gate,up,down}_proj)	int4 symmetric, group 64, bf16 scales
Attention (q_a, q_b, kv_a_proj_with_mqa, kv_b, o_proj) and shared experts, layers 3–77	int8 symmetric, group 64, bf16 scales
Dense layers 0–2, DSA indexers, MoE routers, norms, embeddings, lm_head, the whole MTP block (layer 78)	bf16, byte-exact from the source
Format	compressed-tensors pack-quantized (weight_packed int32, weight_scale bf16, weight_shape), declared in config.json

Method

Plain round-to-nearest with per-group min-max scales (llm-compressor's QuantizationModifier, no calibration data, no activation-aware smoothing), one decoder layer at a time. Every layer is independent of every other, and because nothing is folded into the norms there is no interaction with the router or the DSA indexer. This is the simplest possible checkpoint in this format and the baseline the AWQ companion is measured against.

Measured error (per layer)

Relative RMS error against the bf16 layer on 32 × 2048 real tokens. "Contribution" is the error of what the layer adds (output minus input); "residual" is the error of the full residual stream leaving the layer, which is the quantity that propagates.

layer	variant	contribution	residual
3	this checkpoint (RTN int4 g64)	2.40 %	0.54 %
3	AWQ int4 g64, fold-corrected (companion)	2.38 %	0.53 %
3	fp8 e4m3 g128 on every projection, for reference	2.46 %	0.55 %
3	int8 g64 on every projection, for reference	1.49 %	0.33 %
6 (owns an indexer)	this checkpoint	3.44 %	1.58 %
6	AWQ, fold-corrected (companion)	3.27 %	1.50 %
21	this checkpoint	11.09 %	0.59 %
21	AWQ, fold-corrected (companion)	10.95 %	0.58 %
42 (owns an indexer)	this checkpoint	9.89 %	2.01 %
42	AWQ, fold-corrected (companion)	9.85 %	2.00 %
63	this checkpoint	12.50 %	2.34 %
63	AWQ, fold-corrected (companion)	12.50 %	2.34 %

The contribution metric grows with depth because each layer's addition shrinks relative to the residual stream; the residual error rises from 0.5 % (layer 3) to 2.3 % (layer 63). On this model the int4/int8 mix lands at the error of an all-fp8 recipe at half the bytes. The AWQ companion (HawkBearPig/GLM-5.3-Int4-Int8Mix-AWQ-g64) improves on this checkpoint by 0.9 / 4.8 / 1.2 / 0.4 / 0.02 % of the error at layers 3 / 6 / 21 / 42 / 63: real in the shallow indexer layer, negligible at depth. Per layer the two checkpoints are equivalent; this one is the simpler artifact (no calibration, no smoothing, no correction step).

No end-to-end evaluation yet (perplexity, benchmarks): the numbers above are per-layer.

Loading

Standard compressed-tensors layout: model.safetensors.index.json, per-layer shards layer-NNN.safetensors, passthrough.safetensors (embeddings, norms, lm_head, MTP block), config.json with quantization_config, tokenizer files. Any loader that understands glm_moe_dsa and compressed-tensors pack-quantized W4A16/W8A16 should read it; only DGPP has been exercised. vLLM / transformers loading is untested.

Tooling: llm-compressor 0.13.0, compressed-tensors 0.18.0, transformers 5.14.1, torch 2.13.0, CPU-only build.

License: MIT, inherited from GLM-5.3.