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How to pick a KV cache memory technique for long prompts with LLM Inference Engineering

Compare KV cache compression, PagedAttention, GQA, and Flash Attention to choose a memory approach.

  • Research
  • Intermediate
  • 45 minutes
  • From the README
  • Any agent

Who it is forAn ML engineer sizing GPU memory for a model that serves long conversations or many concurrent users.

The problemThe KV cache grows with the conversation, and memory waste limits how many users one GPU can serve.

The outcomeA short list of which memory technique fits a given serving problem, with the trade-offs for each.

What you need first

  • A web browser to open the linked blog posts
  • Basic familiarity with how text generation works, or willingness to start with the Part 1 basics

Steps

  1. Open the KV Cache in LLMs post at https://outcomeschool.com/blog/kv-cache-in-llms to understand why the cache exists.
  2. Open the KV Cache Compression post at https://outcomeschool.com/blog/kv-cache-compression and read the 'When to use which one' section.
  3. Open the Paged Attention post at https://outcomeschool.com/blog/paged-attention-in-llms to see how it addresses memory waste.
  4. Open the Grouped Query Attention post at https://outcomeschool.com/blog/grouped-query-attention and compare it with MHA and MQA.
  5. Write a table with one row per technique listing the problem it solves and its main trade-off.

Prompt for your agent

Using the amitshekhariitbhu/llm-inference-engineering repo, compare the KV cache techniques for the workload <workload description> and output a table with one row per technique. Verify that each row matches the 'Comparison of the approaches' and 'When to use which one' sections of the KV Cache Compression post.

Caveat. The README gives explanations and links only; it does not show how to apply any of these techniques to a specific model or server.

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