autotune is an open-source tool designed to optimize the performance of local AI language models running through Ollama by acting as a transparent proxy between user code and the model backend. Its primary function is to enhance speed and memory efficiency for local large language models, addressing the common issue of excessive RAM allocation and slow response times without requiring any changes to user code or configuration.
The tool automatically adjusts memory usage by precisely sizing the key-value (KV) cache for each request based on the real token count, adding a safety buffer, and allocating only the necessary resources. This approach returns significant amounts of RAM to the system—up to hundreds of megabytes per request—while maintaining model output quality. autotune also implements a live RAM pressure management system, monitoring operating system memory usage before each request and dynamically adjusting context window size and KV precision across four RAM usage tiers. When system memory is under pressure, it reduces context size and switches KV precision from F16 to Q8, halving memory usage for the KV cache without noticeable effects on model output.
Additional features include system prompt prefix caching, which speeds up multi-turn chats by evaluating the system prompt only once and reusing it in follow-up turns, and a model keep-alive mechanism that prevents Ollama from unloading models after periods of inactivity, thereby eliminating cold-start delays. The tool provides a built-in dashboard accessible via a local browser, offering real-time monitoring of optimizations, RAM usage, request statistics, and performance metrics without sending data to external services.
autotune is distributed under the MIT license and can be installed via pip. It is positioned as a solution for developers and users running large language models locally, particularly those using Ollama, who seek improved memory efficiency and faster response times without altering their existing workflows.
autotune sits in PulseGate's Other AI category. It focuses on improving the speed and reliability of running large language models locally by optimizing resource usage. autotune is an open-source project aimed at developers running local LLMs. The project is open source (MIT). It runs on the web and the command line, and it can be self-hosted.
autotune first shipped in 2026. The project is developed in the open on GitHub with 30 stars and 101 commits in the last 90 days. Across PulseGate's embedding index, autotune has few near neighbours, marking it as relatively distinct. Among its 7 catalogued features are memory optimization, response time reduction, and Drop-in Ollama wrapper.
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Show HN: Makes local LLMs faster and more reliable by optimizing for your device discovered by the PulseGate indexer
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