arXiv:2607.04302v1 Announce Type: cross Abstract: We present HiFA4, a post-training operator-level design that executes both QK^T and PV in FlashAttention as 4-bit HIF4 Cube GEMMs for LLM inference on Ascend NPUs, while maintaining the online softmax state in FP16. To our knowledge, HiFA4 is the first Ascend-HIF4-targeted design of this kind evaluated on standard NLP benchmarks. HiFA4 combines two mechanisms. Smooth-QK applies a calibration-static per-channel equivalent rescaling to Q and K after RoPE, transferring quantization difficulty from K to Q without per-tile online reduction at infere
The continuous demand for more efficient LLM inference necessitates novel hardware-software co-design approaches, particularly as AI models grow larger and more complex.
Achieving efficient 4-bit FlashAttention on non-NVIDIA hardware like Ascend NPUs can significantly reduce the computational and energy costs of LLM inference, democratizing access to powerful AI.
This development indicates growing sophistication in optimizing LLM operations on alternative hardware architectures, potentially broadening the competitive landscape beyond NVIDIA.
- · Huawei Ascend
- · Cloud Providers (non-NVIDIA)
- · LLM Developers
- · Edge AI Implementations
- · Inefficient Inference Solutions
- · Proprietary Hardware with Limited Optimization
This technology enables faster and more cost-effective LLM inference on Ascend NPUs.
Increased adoption of Ascend NPUs in AI workloads, diversifying the global AI compute supply chain.
Potential for new AI services and applications that were previously cost-prohibitive due to demanding inference requirements.
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Read at arXiv cs.AI