KV Cache

What is KV Cache?

KV cache is a mechanism that stores previously computed key-value attention pairs during language model inference to avoid redundant computation when generating sequential tokens. Without caching, the model would need to recompute attention across all previous tokens for every new token generated. The KV cache trades memory for speed, enabling the autoregressive generation that makes language models practical for real-time applications.

How does KV Cache work?

Transformer-based language models generate text one token at a time. Each new token requires attending to all previous tokens through key-value pairs in the attention mechanism. Without a cache, generating the 100th token would require recomputing attention for all 99 preceding tokens — an O(n^2) operation that grows quadratically.

The KV cache stores the key and value tensors computed during previous forward passes. When generating the next token, the model only computes the new token's query, key, and value vectors, then retrieves cached keys and values for all prior tokens. This reduces per-token computation from O(n) to O(1) relative to sequence position.

For a model with 100 layers and a 200K context window, the KV cache can consume 10-50GB of GPU memory. This memory pressure is often the binding constraint on batch size and context length in production deployments, making KV cache management a critical optimization target.

Why does KV Cache matter?

KV cache enables practical inference speeds. Without it, generating a 1,000-token response from a large model would take minutes instead of seconds. The cache converts what would be quadratic-time generation into linear-time generation, making conversational AI economically viable.

KV cache also enables prompt caching services offered by API providers. When multiple requests share a common prefix (like a system prompt), the provider caches and reuses the KV state for that prefix, reducing both latency and cost by up to 90% for the cached portion.

Best practices for KV Cache

• Monitor KV cache memory usage to prevent out-of-memory errors that crash inference servers under load
• Use prompt caching features from API providers when multiple requests share common system prompts or context
• Consider KV cache compression techniques (quantization, eviction policies) for long-context deployments
• Place shared context at the beginning of prompts to maximize cache hit rates across requests