Deploying large language models (LLMs) in an enterprise environment has transitioned from a proof-of-concept exercise to a rigorous engineering discipline. Yet, accurately predicting the capacity of an inference server under real-world, concurrent load remains a formidable challenge. Infrastructure engineers frequently confront complex configuration spaces, questioning whether tuning parameters like –max-num-batched-tokens or –gpu-memory-utilization in vLLM will … Continued
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