Local models and inference engineering · Module 1: Local models and inference engineering in practice
KV caches and long-context memory
Learning outcome: Explain why longer conversations can increase inference memory.
In many autoregressive transformer implementations, a key-value cache retains intermediate attention information for tokens already processed. Reusing it avoids recomputing that information at every generation step. It is an inference mechanism, not a permanent knowledge database.
Cache memory depends on the model architecture, stored precision, context length and concurrent sequences. Some runtimes offer cache offloading or quantisation with their own trade-offs. Measure using the expected conversation length, rather than testing only a tiny prompt. Do not assume every model architecture uses the same cache design or that a weight-memory estimate includes it.
Worked example
A fictional local assistant loads successfully and answers a short question. With a long document and several simultaneous requests, memory use rises sharply. The team had budgeted only for model weights. A representative test includes the longer context and concurrency before choosing operating limits.
Try it yourself
Design a test matrix with short and long inputs, one and several concurrent requests, and a fixed output limit. Record peak memory and whether requests complete.
You can do this exercise on paper. If using an assistant, keep to the fictional material and an approved tool.
Compare your approach
The matrix should isolate context and concurrency effects. Keep the model and runtime configuration fixed for the first comparison. A failure under the larger case is evidence for a limit or redesign, not proof that the short test was invalid.
Check your understanding
A KV cache is best understood as what?
- A permanent collection of verified facts
- A replacement for the model weights
- Reusable inference state for previously processed tokens
Show the answer and explanation
Answer: Reusable inference state for previously processed tokens. The cache reuses attention-related inference state for previously processed tokens in relevant architectures. It is neither a verified fact store nor a replacement for the weights.
Workbook
Download the course workbook to record your answer and compare it after the knowledge check.