GLM-5.3-Flash-NVFP4

NVFP4 (4-bit float, group size 16) quantization of zai-org/GLM-5.3-Flash, covering the routed experts of every MoE layer — including layer 45, the multi-token-prediction (MTP) layer — in a single, uniform scheme.

328.4 GB → 194.6 GB, weight-only (activations stay bf16).

No calibration data is used in this quantization step, and none is needed: the scheme quantizes each group of 16 weights with a scale derived from those 16 values (memoryless_minmax), so there is no forward pass for calibration data to inform. The checkpoint contains no activation-scale tensors and declares input_activations: null.

What is quantized

num_hidden_layers: 45 plus num_nextn_predict_layers: 1 puts the MTP layer at index 45, and it is not a small head: 288 routed experts, ~7.25 B parameters, its own DSA attention and eh_proj/enorm/hnorm plumbing. Keeping it in the same scheme as every other expert layer means the checkpoint has one expert layout throughout — 37,152 packed expert tensors (43 × 288 × 3).

Weight fidelity: mean relative reconstruction error against the dequantized FP8 source is 0.0918 over sampled routed-expert weights, against NVFP4's intrinsic floor of ~0.093 (measured: gaussian 0.0951, laplace 0.0927) — i.e. at the format's limit.

Evaluation

eval-gate medium tier, SGLang TP4 on 4×B200:

Scores are percentages. bfcl_v4 (limit 300 per subset) and hle (limit 200 per subset) are bounded runs and are bounds, not full benchmark scores. aime26 uses repeats: 4 over 30 problems.

Read the last two columns before the second. These runs used max_tokens: 32768 with reasoning_effort: max. On hle and aime26 that budget is too small: a generation that hits the cap never emits the graded answer block and scores exactly zero — verified, 36/36 truncated aime26 samples had an empty answer extraction. So the score column is a blend of real accuracy and token-budget attrition, and the honest reading of this model's aime26 is 97.62 on the problems it was allowed to finish, not 68.33.

For scale: RadixArk's NVFP4 conversion of this model (the SGLang vendor's own, via NVIDIA ModelOpt) reports AIME 2026 at 92.45 % using max_tokens=131072, with a p90 of 53,342 generated tokens — 63 % above the entire cap used here.

Do not compare the hle figure to the base model's published HLE. They measure different things, in four ways that all push this number down:

• no tools. The upstream number is HLE with tools; this run uses none.
• max_tokens 32,768 vs upstream's 163,840 — the truncation above.
• bounded prefix (n=1,476) rather than the full ~2,500-sample set.
• judge is GLM-5.2, not the GPT-class judge upstream used.

Every one of these settings is held identical across the checkpoints this was compared against, which is what makes them useful for comparing quantizations — and is exactly why they are not absolute scores.

Serving

Image, digest-pinned — glm5_next is a new architecture, so a generic SGLang tag will not load it:

lmsysorg/sglang:glm-5.3-flash@sha256:e6f5482505e7502f791fe4615ad1fbec118cbbd6b44e98f2479b16b98b985ad6

In-image: sglang 0.0.0.dev1+g033446bb05, torch 2.13.0+cu130, transformers 5.16.0.dev0.

docker run --gpus all --ipc=host --shm-size 32g -p 8000:8000 \
  -v /path/to/GLM-5.3-Flash-NVFP4:/models/GLM-5.3-Flash-NVFP4 \
  lmsysorg/sglang:glm-5.3-flash@sha256:e6f5482505e7502f791fe4615ad1fbec118cbbd6b44e98f2479b16b98b985ad6 \
  python3 -m sglang.launch_server \
    --model /models/GLM-5.3-Flash-NVFP4 \
    --served-model-name GLM-5.3-Flash \
    --quantization modelopt_fp4 \
    --tensor-parallel-size 4 \
    --kv-cache-dtype fp8_e4m3 \
    --moe-runner-backend flashinfer_trtllm \
    --disable-shared-experts-fusion \
    --reasoning-parser glm45 \
    --tool-call-parser glm47 \
    --chunked-prefill-size 32768 \
    --mem-fraction-static 0.80 \
    --cuda-graph-max-bs 64 \
    --max-running-requests 256 \
    --trust-remote-code \
    --host 0.0.0.0 --port 8000

--quantization modelopt_fp4 is required. SGLang's compressed-tensors MoE path supports only W4A4 NVFP4; a weight-only NVFP4 MoE checkpoint loads only through the modelopt path (ModelOptNvFp4FusedMoEMethod).

vLLM reads the same modelopt NVFP4 format and is expected to work with a build supporting glm5_next, but was not tested here:

vllm serve /path/to/GLM-5.3-Flash-NVFP4 --served-model-name GLM-5.3-Flash \
  --quantization modelopt_fp4 --tensor-parallel-size 4 \
  --kv-cache-dtype fp8_e4m3 --trust-remote-code --host 0.0.0.0 --port 8000

This is a reasoning model served with --reasoning-parser glm45: the chain of thought arrives in reasoning_content and content stays empty until it finishes, so give requests a real token budget (2048+).

Notes

• Weight-only: activations are bf16, trading some W4A4 throughput for needing no calibration set.

• Speculative decoding (MTP/EAGLE) works, and needs two things beyond the defaults. Layer 45's experts are NVFP4 like every other expert layer, and the NextN draft model loads from them — measured on 4xB200 TP4:

  draft model	Glm5NextForConditionalGenerationNextN, 1.68 GB/rank
  accepted tokens per verification step	2.475
  draft acceptance rate	49.2 %
  single-request decode	288 tok/s

  1. Declare the checkpoint as modelopt_mixed. The quantization_config shipped here is flat (quant_method: modelopt, quant_algo: NVFP4), and SGLang's NextN path discards per-layer quantization metadata under --quantization modelopt_fp4, then reads the packed 4-bit layer-45 weights as if unquantized — the size of tensor a (4096) must match the size of tensor b (2048) you will otherwise hit (4096 = one byte per value, 2048 = two 4-bit values per byte). Replacing config.json with the provided config.modelopt_mixed.json, which names layers 3–45 explicitly, fixes it. No weight changes.
  2. Use a NextN-name-aware SGLang build. Stock rewrites model.layers.<n> → model.decoder by substring, so this model's model.language_model.layers.45.* becomes model.language_model.decoder.* and the draft modules never bind. This is an upstream defect that affects every GLM-5.3-Flash conversion, not just this one.

  Without both, serve without --speculative-algorithm — which is how the scores above were produced, so they are unaffected either way.

• The vision tower is not quantized, so the size reduction applies to the text model.

License

MIT, inherited from the base model.