Self-hosting large language models (LLMs) can lead to crashes under heavy load due to the KV cache, which consumes significant GPU memory per request and grows with context length and concurrency. This memory usage, rather than model weights, is the primary factor determining server stability. Implementing admission control, which estimates the KV cache budget before accepting a request, is crucial for maintaining LLM server uptime. This involves calculating the per-token KV cache size and the remaining memory after accounting for model weights and overhead to determine the total KV tokens that can be processed concurrently. AI
IMPACT Optimizing self-hosted LLM deployments by managing KV cache memory can improve efficiency and stability for AI operators.
RANK_REASON Technical guide on optimizing LLM deployment infrastructure.
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