⚙️ Caching

Caching#

Storing computed results to serve repeated requests faster.

Important

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What it is#

Caching stores the results of expensive computation and reuses them instead of recomputing — trading memory for speed and cost. If the same work would produce the same answer, a cache returns it instantly.

In model serving#

The signature example is the KV cache in transformers, which keeps the key/value tensors of earlier tokens so generating each new token doesn’t re-process the whole sequence. It grows linearly with sequence length and can exceed the model weights in memory — which is why it is often quantized. Beyond that, prediction caching reuses answers to repeated requests and feature caching precomputes costly features.

The trade-offs#

A cache must handle staleness — cached values can go out of date when inputs or the model change — so it needs invalidation and eviction policies, and it consumes memory that must be budgeted against the speed it buys.

Theme: MLOps, Serving & Monitoring · All terminology

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Mind map — connected ideas

Quantization · TPU Clusters · Inference Cost (Inference $) · Cloud Inference · Latency Guardrails · Compute budgets

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More in MLOps, Serving & Monitoring

AWS SageMaker Endpoints · Cloud Inference · Cloud Inference with Big Payloads · Compute budgets · Continuous Retraining · Feature Values · Guardrails (in ML & Data Systems) · Inference Cost (Inference $) · Latency Guardrails · Manual review minutes · Model KPIs (Key Performance Indicators) · Model Stability · Monitoring Pipelines · Ops Health Dashboard