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Diagnosing FP4 inference: a layer-wise and block-wise sensitivity analysis of NVFP4 and MXFP4
🤖AI Summary
Research analyzes FP4 quantization sensitivity across different layers in large language models using NVFP4 and MXFP4 formats on Qwen2.5 models. The study finds MLP projection layers are most sensitive to quantization, while attention layers show substantial robustness to FP4 precision reduction.
Key Takeaways
- →MLP up- and down-projection layers consistently show highest sensitivity to FP4 quantization across all model scales tested
- →Attention projection layers demonstrate substantial robustness to FP4 quantization compared to other components
- →Early transformer blocks can be highly sensitive to quantization, particularly under MXFP4 format
- →The research provides systematic analysis across three Qwen2.5 model scales (0.5B, 7B, 14B parameters)
- →FP4 quantization is being adopted in cutting-edge architectures like Blackwell and AMD CDNA to reduce LLM deployment costs
#fp4-quantization#llm-optimization#transformer-analysis#model-efficiency#nvidia#amd#qwen#inference-optimization#low-precision
Read Original →via arXiv – CS AI
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