Quantization Method
This checkpoint was produced with our in-house post-training quantization
toolchain and exported in the
NVIDIA Model Optimizer
format. Only the weights and activations of the linear
operators within the routed MoE experts are quantized to NVFP4, with static
per-tensor activation scales. The KV cache is quantized to FP8 with static
unit scales (kv_cache_quant_algo: FP8).
Quick Start
The commands below enable speculative decoding with the
DFlash 2 draft model
(lossless, 7 draft tokens per verification step); remove the
--speculative-* flags to serve without it.
Serve with SGLang (main):
python3 -m sglang.launch_server \ --model-path incoai/GLM-5.3-NVFP4 \ --tp 8 \ --quantization modelopt_fp4 \ --reasoning-parser glm45 \ --tool-call-parser glm47 \ --chunked-prefill-size 8192 \ --speculative-algorithm DFLASH \ --speculative-draft-model-path incoai/GLM-5.3-DFlash2 \ --speculative-draft-attention-backend trtllm_mha
Or with vLLM (v0.28.0 or later):
vllm serve incoai/GLM-5.3-NVFP4 \ --tensor-parallel-size 8 \ --reasoning-parser glm45 \ --tool-call-parser glm47 \ --enable-auto-tool-choice \ --speculative-config '{"method":"dflash","model":"incoai/GLM-5.3-DFlash2","num_speculative_tokens":7}'
Accuracy
We compare Z.ai's original FP8 release with this NVFP4 checkpoint.
Higher is better.
Table with columns: Precision, GPQA Diamond, AIME 2025, MATH-500, HLE, AA-LCR| Precision | GPQA Diamond | AIME 2025 | MATH-500 | HLE | AA-LCR |
|---|
| FP8 | 91.1 | 94.3 | 95.6 | 35.9 | 73.6 |
| NVFP4 | 91.2 | 95.1 | 95.2 | 35.2 | 73.0 |
License
This model is a quantized version of GLM-5.3 and is distributed under Z.ai's
GLM-5.3 License,
which it inherits from the base model.