Inference
KV Cache
A store of the key and value tensors a transformer has already computed for earlier tokens, kept in memory so they can be reused at each new decoding step.
In depth
A store of the key and value tensors a transformer has already computed for earlier tokens, kept in memory so they can be reused at each new decoding step. Without it, an autoregressive model would recompute attention inputs for the whole sequence every time it predicts one more token. The trade-off is memory: the cache grows with context length and batch size, which is why long-context serving is often limited by KV cache size rather than model weights. Inference servers manage it with techniques like paged allocation, offloading, and quantized caches.
Primary source: huggingface.co
Example
Inference servers manage it with techniques like paged allocation, offloading, and quantized caches.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Inference.
FAQ
Common questions
What is KV Cache?
A store of the key and value tensors a transformer has already computed for earlier tokens, kept in memory so they can be reused at each new decoding step.
Why does KV Cache matter for AI developers?
KV Cache sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about KV Cache?
Developers Digest publishes tutorials and videos that cover Inference topics including KV Cache. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
Multi-Query Attention (MQA)
Multi-query attention is a variant of multi-head attention where all query heads share a single key head and a single value head.
Inference
State Space Models (SSM)
State space models are sequence models that carry a fixed-size hidden state forward through the input, updating it with learned linear equations, rather than comparing every token to every other token like attention.
Inference
Continuous Batching
A serving technique that schedules work one model iteration at a time, so requests can join or leave a running batch at every token step instead of waiting for the whole batch to finish.
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