GPU idle time and fragmentation slash LLM inference throughput

GPU idle time and memory fragmentation significantly reduce inference throughput in large language model serving, often masked by compute utilization metrics. Research, including work on vLLM's PagedAttention and findings from the Mingxin FX100, highlights that data movement and KV Cache management are critical bottlenecks. Offloading KV Cache to external storage, as demonstrated by the Mingxin FX100, can improve throughput by up to 40% and reduce time-to-first-token by over 25%, by keeping GPUs continuously saturated and minimizing recomputation. AI

IMPACT Optimizing GPU utilization and memory management is crucial for reducing LLM serving costs and improving performance.

RANK_REASON The item discusses research findings and technical analysis related to LLM inference optimization, citing academic papers and technical implementations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• arXiv
• Efficient Memory Management for Large Language Model Serving with PagedAttention
• FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
• Mingxin FX100
• Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving
• NeurIPS '22
• NVME-of queue management in host clusters
• Proceedings of the 29th Symposium on Operating Systems Principles
• vLLM

• infra

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