AxionML DeepSeek-V4-Pro-NVFP4
Mirrored by AxionML for open-source serving and deployment use cases. Part of AxionML's effort to provide ready-to-serve quantized models for the community.
Quantized by NVIDIA. The weights in this repository are an unmodified copy of nvidia/DeepSeek-V4-Pro-NVFP4 (revision
1449d1e641023406daf6b432361486c768aad740). All credit for the quantization belongs to NVIDIA.
This is an NVFP4-quantized version of deepseek-ai/DeepSeek-V4-Pro (1.6T total parameters, 49B activated), quantized with NVIDIA Model Optimizer.
About NVFP4 quantization: NVFP4 on Blackwell couples a compact E2M1 FP4 codebook with blockwise FP8 (E4M3) scaling over 16-element micro-blocks, so that 4-bit stored values remain numerically useful for neural-network computation. The E2M1 codebook provides a small, nonuniform set of representable magnitudes up to ±6 and relies on saturating behavior rather than IEEE NaN/Inf encodings to maximize usable range per bit. Using an FP8 block scale (rather than power-of-two-only E8M0) enables fractional scales and error-minimizing scale selection. On Blackwell Tensor Cores, native FP4 multipliers exploit E2M1 simplicity while higher-precision FP32 accumulation protects dot-product accuracy.
Ready for commercial and non-commercial use under MIT.
Model Summary
| Architecture | MoE with hybrid attention (Compressed Sparse + Heavily Compressed Attention) |
| Total Parameters | 1.6T |
| Activated Parameters | 49B |
| Reasoning Modes | Non-think / Think High / Think Max |
| Context Length | 1M tokens |
| Checkpoint Size | ~913 GB |
Evaluation Results
| Benchmark | FP8 (AA reference) | FP8 (NVIDIA) | NVFP4 |
|---|---|---|---|
| GPQA Diamond | 89.00 | 89.49 | 89.33 |
| AA-LCR | 66.00 | 66.89 | 66.33 |
| τ²-Bench Telecom | 96.00 | 94.25 | 94.83 |
| SciCode | 50.00 | 51.08 | 53.45 |
| IFBench | 76.00 | 77.82 | 77.21 |
Scores reported by NVIDIA for this checkpoint.
Quantization Details
- Quantization format: NVFP4 weights and activations for the routed MoE experts; attention and shared experts keep their source FP8 precision
- Calibration dataset: cnn_dailymail, Nemotron-Post-Training-Dataset-v2
- Tool: NVIDIA Model Optimizer v0.44
Usage
Deploy with SGLang
python3 -m sglang.launch_server \
--model-path AxionML/DeepSeek-V4-Pro-NVFP4 \
--tensor-parallel-size 8 \
--trust-remote-code
Deploy with vLLM
vllm serve AxionML/DeepSeek-V4-Pro-NVFP4 \
--tensor-parallel-size 4 \
--trust-remote-code \
--kv-cache-dtype fp8
SGLang auto-detects NVFP4 from hf_quant_config.json (sgl-project/sglang#25820). vLLM validated upstream on GB300 (TP4). Superseded by AxionML/DeepSeek-V4-Pro-0813-NVFP4.
Limitations
The base model was trained on data that may contain toxic language and societal biases. The quantized model inherits these limitations. It may generate inaccurate, biased, or offensive content. Please refer to the original model card and the upstream quantized model card for full details.
Credits
- Base model: deepseek-ai/DeepSeek-V4-Pro
- Quantization: nvidia/DeepSeek-V4-Pro-NVFP4 by NVIDIA
- Mirror: AxionML