KV Cache Management infographic (Akshay Pachaar tweet)

Akshay Pachaar @akshay_pachaar (2026-07-09)

Tweet linking to X Article with infographic: “KV Cache Management: Faster cheaper inference” Stats: 163 likes, 24 retweets, 89K views

The infographic explains LMCache’s tiered KV cache management system for LLM inference.

LMCache Architecture (from infographic)

User sends Prompt to Inference Engine (vLLM/SGLang)
Inference Engine sends Block IDs (tiny messages) to LMCacheDaemon
LMCacheDaemon initiates parallel search across Storage Tiers:
- GPU Memory (fastest, highest cost)
- CPU RAM
- Local SSD
- Cloud Storage (slowest, lowest cost)
Only uncached tokens are routed to Shared GPU Memory for attention computation
New KV cache is stored to LMCacheDaemon → Shared GPU Memory (+ lower tiers)
Attention Mechanism processes cached + new tokens → Response

Only compute what’s not already cached. Tiered storage hierarchy enables reuse across requests, sessions, and engine instances.

LMCache is a KV cache management layer for LLM inference. It turns KV cache from temporary state into reusable AI-native knowledge.

Key features:

Engine-independent deployment (standalone daemon, no fate-sharing with engines)
Persistent, tiered KV cache offloading (GPU → CPU → SSD → remote)
Production-level KV cache observability (Kubernetes metrics, prefix cache hits, lifecycle)
Pluggable storage and transport backends
Vendor-neutral (works with vLLM, SGLang, multiple hardware vendors)

Updates: