Quickstart Examples#
This section provides quick examples to help you get started with LMCache’s key features.
KV Cache Offloading#
KV cache offloading allows you to move KV caches from GPU memory to CPU memory or other storage devices. This feature is particularly useful when:
There are requests shares the same prefix (e.g., long system prompt, re-using chat history in chat applications, or caching offline-processed data).
The GPU memory is limited to save all the KV caches.
By offloading KV caches, LMCache can reduce both time-to-first-token (TTFT) and GPU cycles.
See Example: Offload KV cache to CPU for more details.
KV Cache Sharing#
KV cache sharing enables sharing the KV cache across different LLM instances. This feature is beneficial when:
There are multiple LLM instances running in the same system.
The requests that share the same prefix may go to different LLM instances.
Sharing KV caches also reduces TTFT and GPU computation by eliminating redundant calculations across different LLM instances.
See Example: Share KV cache across multiple LLMs for more details.
Disaggregated Prefill#
Disaggregated prefill separates the prefill and decode phases across different compute resources. This approach:
Allows specialized hardware allocation for each phase of inference
Enables more efficient resource utilization in distributed settings
Improves overall system throughput by optimizing for the different computational patterns of prefill vs. decode
This architecture is particularly valuable in large-scale deployment scenarios where maximizing resource efficiency and keeping a stable generation speed are both important.
See Example: Disaggregated prefill for more details.