LLM Caching (3-Layer Cache) - Interactive Visualization

LLM caching reduces inference cost and latency by serving repeated or semantically similar queries from cache instead of calling the model. The three layers work in order: first an exact hash lookup (3ms), then a semantic similarity search using query embeddings (12ms), then KV cache prefix sharing which reuses computed key-value pairs from the model's attention mechanism (40ms). A cache miss falls through to full LLM inference at ~820ms. In production chatbots, 40-60% cache hit rates are common, cutting cost proportionally.

• Visualize all 3 cache layers with per-layer hit counts and latency
• Simulate incoming requests - watch hits and misses in real time
• Semantic similarity threshold slider - see how lower thresholds inflate hit rate (but risk wrong answers)
• Eviction policy selector: LRU, TTL, or LFU
• Live stats: hit rate, average latency, total time saved
• Used in: GPTCache, LangChain cache, RedisAI, custom semantic cache systems

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