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Arxiv
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Mind the Memory Gap: Unveiling GPU Bottlenecks in Large-Batch LLM Inference
- Large language models often face inefficient resource utilization during inference due to their auto-regressive nature.
- Existing literature typically explains performance plateau in large-batch inference as a shift to the compute-bound regime, but a new study reveals it remains memory-bound.
- Researchers propose a Batching Configuration Advisor (BCA) to optimize memory allocation, reducing GPU memory requirements and improving resource utilization.
- The study challenges conventional assumptions, offers insights and strategies for better resource utilization, especially for smaller language models.
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