With the K8V4 configuration providing 59% memory savings, you can effectively run contexts 2.4× longer on the same hardware. A model with a 2048 token context can now handle about 5000 tokens, while an 8K context model can reach approximately 19.5K tokens.
In practical terms, this means processing entire books at once on a MacBook, analyzing large codebases without splitting files, or maintaining comprehensive conversation history in chat applications.
The memory savings scale linearly with context length - the longer your context window, the more absolute memory you save. On my M4 MacBook with 8K context, I reduced KV cache from 176MB to 72MB. At 128K context, that same percentage saving would free up gigabytes.
This optimization is most valuable when you're context-window limited rather than model-parameter limited. If you're hitting OOM errors due to long inputs rather than large model weights, KVSplit directly addresses your bottleneck.
In practical terms, this means processing entire books at once on a MacBook, analyzing large codebases without splitting files, or maintaining comprehensive conversation history in chat applications.
The memory savings scale linearly with context length - the longer your context window, the more absolute memory you save. On my M4 MacBook with 8K context, I reduced KV cache from 176MB to 72MB. At 128K context, that same percentage saving would free up gigabytes.
This optimization is most valuable when you're context-window limited rather than model-parameter limited. If you're hitting OOM errors due to long inputs rather than large model weights, KVSplit directly addresses your bottleneck.