This article explains LLM inference optimization techniques for production deployment. It covers the two-phase inference process (prefill and decode), memory management strategies like KV caching and PagedAttention, and methods including model compression and speculative decoding to improve speed, cost, and reliability without retraining.
This article explains LLM inference optimization techniques for faster, cheaper production deployments. It covers the two-phase inference process (prefill and decode), memory management strategies like KV caching and PagedAttention, and methods such as model compression and speculative decoding to reduce cost and improve throughput.
A developer discusses limitations of provider-managed KV caching for agent swarms and long-running agents, noting the lack of manual control over cache management and the challenges this creates for forking agents from shared prefixes or persisting caches across sessions.