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AI Research
arXiv CS.AI·Jun 9·5 min read
APEX4: Efficient Pure W4A4 LLM Inference via Intra-SM Compute Rebalancing
TL;DR
Existing systems struggle with W4A4 quantization due to overhead from group dequantization on CUDA Cores. APEX4 was developed to optimize intra-SM (Streaming Multiprocessor) compute balance, focusing on the Tensor Cores to CUDA Cores throughput ratio.
✦ Why It Matters
Engineers can optimize LLM inference performance by focusing on compute balance between Tensor Cores and CUDA Cores.
Key Takeaways
- Utilize APEX4 for optimized LLM inference on supported GPUs.
- Leverage pure INT4 GEMM kernels to reduce dequantization overhead.
- Achieve significant speedups without sacrificing model accuracy.
- Evaluate GPU architecture compatibility for maximum performance gains.
How It Works
APEX4 optimizes LLM inference by balancing the compute load between Tensor Cores and CUDA Cores. It adapts the granularity of operations based on the identified throughput ratio, allowing for efficient processing of INT4 data while minimizing the dequantization delays that typically hinder performance.