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.