Model Description
local-inference-lab/GLM-5.3-Flash-NVFP4 is a quantization-aware distilled version of zai-org/GLM-5.3-Flash-BF16. The student is trained against the original BF16 teacher with NVFP4 expert weights in its forward pass, learning to compensate for quantization error rather than relying on post-training quantization alone.
The architecture is unchanged: 45 decoder layers, including three dense layers and 42 MoE layers with 288 routed experts each, eight active per token, and one shared expert. Attention combines linear attention and DeepSeek Sparse Attention. The release occupies approximately 199 GB on disk.
What's quantized
| Component | Stored format | Distillation |
|---|---|---|
| Routed decoder experts: gate, up and down projections | NVFP4 | Trained |
| Dense MLPs in layers 0–2 and shared experts | BF16 | Trained |
| Router projection matrices | BF16 | Trained |
| Decoder and final RMSNorm weights | BF16 | Trained |
| Routing correction biases | FP32 | Updated during distillation |
| Attention, residual-stream mixing, token embeddings and LM head | Original BF16/FP32 | Frozen |
| Vision encoder | BF16 | Unchanged; outside text distillation |
| Auxiliary MTP routed experts | MXFP8 | Unchanged; outside text distillation |
NVFP4 stores 4-bit E2M1 values with FP8 E4M3 block scales per 16 elements and FP32 global scales. Gate/up projections share a global scale within each expert; down projections have their own. The scales are recomputed from the trained weights during quantization.
The multi-token prediction (MTP) module is retained from the mixed-precision base checkpoint. Its routed experts use MXFP8 E4M3 values with power-of-two block scales per 32 elements; its other parameters retain their original precision. The vision encoder and MTP module were not distilled.
Quantization-aware distillation
The BF16 teacher generates chat responses and supplies probability and hidden-state targets for the student.
During training, the student's routed-expert weights are quantized to NVFP4 and reconstructed for each forward pass. Gradients update the underlying BF16 weights so their low-precision representations better match the teacher's next-token probabilities and final hidden states.
The student uses its own expert routing during training. Routers, shared experts, dense MLPs and normalization weights are also adapted, allowing the unquantized components to compensate alongside the NVFP4 experts.
Training data
The distillation corpus contains approximately 200M prompt-and-response tokens. Assistant responses come from the BF16 GLM teacher using its native chat template.
Activation calibration
Distillation uses BF16 activations. For NVFP4 activation quantization at inference, expert-specific input scales are calibrated separately on the 200M-token chat corpus.
Calibration uses natural routing and covers all 12,096 layer/expert pairs without fallback scales. Gate/up share their expert's input range; down uses the post-activation range. The serving configuration also retains dynamic MXFP8 activation quantization for MTP routed experts.
Requirements
Use a runtime supporting the glm5_next architecture and this mixed
NVFP4/MXFP8 ModelOpt layout, including per-expert activation scales.
The tokenizer, upstream chat template, generation settings, vision assets
and MTP tensors are included. KV cache and runtime buffers require memory
in addition to the model weights.