LMCache/LMCache

Features

• Prefill-Decode Disaggregation - Implements an architecture that separates prompt processing from token generation by transferring KV caches across compute nodes.
• LLM KV Cache Stores - Provides a distributed storage system for reusing key-value caches across multiple LLM serving engines.
• Inference Acceleration - Accelerates prefill and decode disaggregation by transferring cached states between distributed compute nodes.
• KV Cache Optimizations - Implements real-time cache routing between servers to optimize inference speed and reduce redundant computation.
• Device Offloading - Saves GPU cycles by offloading key-value caches from GPU memory to CPU RAM or other devices.
• LLM Serving Architectures - Coordinates shared cache layers and peer-to-peer tensor transfers across a cluster of multiple inference engine instances.
• Disaggregated Inference - Implements a disaggregated architecture that separates prefill and decode stages across distinct hardware nodes.
• Prefix Caching - Checks for existing cached prefixes to reuse KV cache states and reduce time-to-first-token.
• Token Sequence Indexing - Maps token sequences to cached entries using chunking and hashing to enable cross-request lookups.
• KV Cache Quantization - Uses reduced precision formats and serialization to reduce the memory footprint and increase transfer speeds of KV caches.
• Content Caching Controls - Provides administrative controls to look up, purge, pin, or migrate specific cache entries at runtime.
• Distributed Caches - Enables direct data transfers between serving instances to facilitate global lookup and sharing without a central bottleneck.
• Backend Implementation Extensions - Provides high-performance C++ connectors to integrate diverse external stores as persistent cache layers.
• Distributed Caching - Provides a distributed C++ backend supporting TCP and RDMA for high-performance cache storage and retrieval.
• Tiered Memory Management - Implements a tiered memory system that manages cache data between pinned DRAM and NVMe storage.
• Persistent Storage Backends - Asynchronously offloads cache chunks from fast memory to durable backends to ensure data persistence.
• Shared Memory Transports - Achieves zero-copy transfers by sharing tensors between the cache server and inference engine using shared memory.
• Storage Backend Adapters - Uses a pluggable interface to integrate diverse persistent storage backends including S3, Redis, and local filesystems.
• Storage Tiering - Moves cache data across a hierarchy of GPU memory, CPU DRAM, and persistent backends to balance latency and capacity.
• Tiered Caching - Manages a hierarchy of memory and persistent storage backends to optimize LLM cache replication and retrieval.
• Cluster Membership Coordination - Registers individual cache servers with a central coordinator to provide a unified view of active cluster instances.
• Remote Server Fleet Management - Provides a centralized service to track server instances, manage global fingerprints, and coordinate quota evictions.
• Storage Backend Integrations - Integrates multiple external storage backends, including S3 and local filesystems, as persistent cache overflow.
• Asynchronous Cache Offloading - Writes cache chunks to secondary storage in background threads to prevent blocking the primary inference pipeline.
• Peer Discovery Services - Tracks live server instances and manages peer discovery and heartbeats via a central coordinator service.
• Inter-Worker Tensor Transfers - Transfers cache data from prefill workers to decode workers over high-speed interconnects like NVLink or RDMA.
• Disaggregated Cache Sharing - Coordinates cache distribution across multiple servers using peer-to-peer sharing and disaggregated architectures.
• Inference Benchmarking Tools - Provides utilities for measuring throughput, latency, and cache hit rates using simulated workloads and trace replay.
• Persistence Layers - Offloads infrequently used cache blocks to disk or remote backends to ensure they survive process restarts.
• Serving Frameworks - Links the cache management layer to external serving frameworks to enable the reuse of cached tensors across instances.
• Non-Prefix Block Reuse - Retrieves cached blocks from any position in a prompt using selective recomputation to maintain output quality.
• Cross-Deployment Cache Sharing - Enables the reuse of cached tensors across different hardware configurations and distributed serving engines.
• Cache Prefetching Policies - Defines how cached keys are distributed across secondary adapters and prefetched into primary memory.
• Cache Eviction Policies - Sets and monitors storage limits for specific cache identifiers to trigger automatic eviction of data.
• Tiered Lifecycle Monitoring - Records the time from allocation to eviction and reuse gaps for cache chunks across different storage tiers.
• Serializers - Implements custom serialization and deserialization logic to control how tensors are encoded for different storage adapters.
• Intermediate Output Caching - Caches intermediate encoder outputs for multimodal inputs like images and video to bypass redundant computation.
• Least Recently Used Caches - Employs an LRU eviction policy to manage byte budgets and storage limits for cached tensors.
• Shared Memory Data Exchange - Exchanges tensors between the cache server and inference engine using shared memory to eliminate serialization overhead.
• Cascading Storage Adapters - Distributes data across a sequence of storage backends and queries them in order during cache lookups.
• Distributed Cache Routing - Connects the cache layer to specific network hosts and ports to manage distributed memory across multiple machines.
• Cache Server Deployments - Manages the deployment and lifecycle of multiprocess cache servers using custom Kubernetes resources.
• Layer-wise Streaming - Streams cache data on a per-layer basis to overlap memory loading with the model's forward pass.
• Operation Pipelining - Overlaps the computation of one model layer with the storage or retrieval of another to mask latency.
• DaemonSet Cache Orchestration - Runs a single cache server per node shared by multiple pods via Kubernetes DaemonSets to optimize resources.
• Runtime Configurations - Allows modification of backend storage mappings, device paths, and capacity at runtime without restarting the server.
• Proactive Cache Prefetching - Implements techniques to populate cache layers before the inference engine explicitly requests the data chunks.
• Peer-to-Peer Networking - Enables direct memory cache sharing and global lookups across cluster nodes using P2P networking.
• Centralized Node Registries - Uses a centralized registry to track active server instances and manage node membership via heartbeats.
• Data Path Orchestration - Optimizes transfers by selecting between server-driven or engine-driven data paths and managing shared memory pools.
• Process Isolations - Runs the cache manager as a standalone daemon to ensure cache persistence during inference engine crashes.
• Master-Worker Coordination - Uses a centralized controller server to coordinate key-value cache instances and manage worker nodes.
• LRU Cache Eviction - Automatically evicts the least recently used cache entries when predefined memory watermarks or byte budgets are reached.
• Plugin-Based Logic Extensions - Loads third-party storage connectors as standalone packages at runtime to extend cache capabilities.
• Standalone Service Modes - Allows the cache engine to operate as a standalone service independent of a specific inference engine.
• Inference Performance Monitoring - Tracks cache metrics and per-request latency spans using OpenTelemetry to diagnose inference bottlenecks.
• Cache Performance Metrics - Calculates the fraction of requested tokens served from caches to evaluate the effectiveness of the tiering layer.
• Cache Behavior Simulators - Analyzes lookup logs through a cache simulator to measure token hit rates across different memory capacities.
• AI Inference Benchmarks - Provides tools to simulate machine learning workloads and measure key inference performance metrics like time-to-first-token and throughput.
• Server Management APIs - Provides a standalone control plane with HTTP APIs for administrative server operations, health monitoring, and checksum verification.
• KV Cache Management - Fast context loading and knowledge fusion for LLMs.
• Memory Management - KV cache layer for accelerating LLM inference.
• Model Serving & Deployment - Accelerates LLM inference via KV cache management.

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