LLM storage latency tolerance shifts stepwise with GPU utilization

Storage latency tolerance for LLM inference does not decrease linearly with GPU utilization, but rather in a stepwise manner. At higher GPU utilization levels (around 90%), the compute queue saturates, making storage latency a critical bottleneck. This necessitates stable, low-latency storage responses rather than just high bandwidth to avoid GPU idle time and maintain effective compute. For instance, using the Mingxin FX100 with a 480B model, increasing concurrency led to a significant improvement in time-to-first-token, highlighting the importance of storage performance in high-utilization AI workloads. AI

IMPACT Optimizing storage infrastructure is crucial for efficient LLM inference, especially at high GPU utilization, directly impacting deployment costs and performance.

RANK_REASON The item details technical measurements and analysis of storage latency in AI inference workloads, presenting findings from specific reports and deployments. [lever_c_demoted from research: ic=1 ai=0.7]

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• 480B
• DeepSeek-32B
• DeepSeek-70B
• DeepSeek R2
• Huawei Atlas 910B
• Mingxin FX100
• NVIDIA GPUDirect Storage
• NVM Express
• Qwen2.5-32B
• R1
• Rodalies Barcelona line R3
• Śniadecki

• infra

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