Prefix caching in AI refers to a technique used to optimize text generation by storing and reusing previously computed intermediate results. This significantly speeds up inference.
How it works:
- Intermediate Results Storage: When generating tokens, the model computes probabilities based on preceding context. Prefix caching stores these computations (e.g., key-value pairs in attention mechanisms).
- Reuse of Computed Values: For subsequent generations sharing a common prefix, the model reuses cached results instead of recomputing them.
- Efficiency Gains: Avoiding redundant calculations leads to substantial efficiency gains for longer sequences or complex outputs.
Applications: Text generation and machine translation.