novita/Deepseek-R1-W4AFP8

Deepseek-R1-W4AFP8

Model Overview

• Model Architecture: DeepseekV3ForCausalLM
  • Input: Text
  • Output: Text
• Model Optimizations:
  • Dense Weight quantization: FP8
  • MOE Weight quantization: INT4
  • Activation quantization: FP8
• Release Date: 25/10/2025
• Version: 1.0

Quantized version of deepseek-ai/Deepseek-R1-W4AFP8

Model	MMLU
novita/Deepseek-R1-W4AFP8	0.8705

Model Optimizations

These models were obtained by quantizing the weights and activations of DeepSeek models to mixed-precision data types (W4(int)A(FP)8 for MoE layers and FP8 for dense layers). This optimization reduces the number of bits per parameter 4/8, significantly reducing GPU memory requirements.

Use with SGLANG

This model can be deployed efficiently using the SGLANG backend with only H200x4, as shown in the example below.

python -m sglang.launch_server --model novita/Deepseek-R1-W4AFP8  --mem-fraction-static 0.85 --disable-shared-experts-fusion --tp-size 4