K/V Cache Quantization in Ollama

A somewhat hidden feature of Ollama is K/V Cache quantization. This post is about how to activate it, it’s benefits and drawback, example numbers, and use cases.

Activating K/V Cache quantization in Ollama:

• not on by default
• Set the OLLAMA_KV_CACHE_TYPE environment variable.
• see Ollama’s FAQ entry How can I set the quantization type for the K/V cache? for supported values.

Benefits:

• lower memory consumption
• most pronounced when using small models with large context windows

Drawback:

• lower accuracy (not much)
• The lmdeploy team has written a blog post about K/V cache quantization accuracy test results.

Example numbers:

• Llama 3.2 8B supports 128.000 tokens context windows.
• Running it with Q4_K_M quantization and the longest possible context length, it consumes
  • 23.3 GB memory without K/V cache qantization,
  • 17.0 GB with Q8_K_0 K/V cache quantization, and
  • 13.8 GB with Q4_K_0 K/V cache quantization.
• it now fits into 16 GB RAM…
• Sam McLeod’s vram-estimator is a nice tool to estimate the memory consumption of models with different quantization settings.

Use cases:

Application	Benefit
code generation	more code in context
question answering	whole docs fit into the context
function calling	more tools
chat	longer conversations
multi-modal	images need many tokens

To learn more, read Sam McLeod’s in-dept blog post about Bringing K/V Context Quantisation to Ollama. Sam helped implement this in Ollama.