This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Autotune is an open-source runtime optimizer for local LLMs that reduces KV cache memory, improves first-token latency, and dynamically adapts inference settings to your hardware and workload. It works with Ollama, MLX, and as an API. Results from benchmarks show that Autotune can lower time-to-first-token by 39%, wall time for agentic workflows by 46%, and KV cache memory usage by 67%. Features include an OpenAI-compatible local API, a built-in CLI, RAM management, and model recommendations.
I was building and using software that utilized local large language models and my computer would often freeze or I would have to wait forever for the output. I wanted something that ensured that the model would be stable and fast on MY computer. That’s why I built Autotune.
Autotune is a free runtime optimization layer that sits between you and your local model. It employs a handful of optimizations to ensure that the model running on your computer works as well as it can. These optimizations include precise KV cache allocation, dynamic RAM pressure management, system prompt prefix caching, smart context reduction, hardware-aware model recommendations, and more.
What this means for you is faster response times (especially for agents!), less computer struggles, and more RAM for all your other apps.
Works with Ollama, MLX, and as an OpenAI-compatible API.
I’ve spent a bit of time working on this so it would mean a lot to me if y’all checked it out - I think it can help you.
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About Autotune on Product Hunt
“Run local LLMs faster and smoother on your device”
Autotune was submitted on Product Hunt and earned 15 upvotes and 2 comments, placing #85 on the daily leaderboard. Autotune is an open-source runtime optimizer for local LLMs that reduces KV cache memory, improves first-token latency, and dynamically adapts inference settings to your hardware and workload. It works with Ollama, MLX, and as an API. Results from benchmarks show that Autotune can lower time-to-first-token by 39%, wall time for agentic workflows by 46%, and KV cache memory usage by 67%. Features include an OpenAI-compatible local API, a built-in CLI, RAM management, and model recommendations.
Autotune was featured in Developer Tools (512.6k followers), Artificial Intelligence (468.7k followers) and Tech (623.7k followers) on Product Hunt. Together, these topics include over 325.5k products, making this a competitive space to launch in.
Who hunted Autotune?
Autotune was hunted by Tanav Chinthapatla. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Autotune stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Tanav Chinthapatla
Hi everyone 👋
I was building and using software that utilized local large language models and my computer would often freeze or I would have to wait forever for the output. I wanted something that ensured that the model would be stable and fast on MY computer. That’s why I built Autotune.
Autotune is a free runtime optimization layer that sits between you and your local model. It employs a handful of optimizations to ensure that the model running on your computer works as well as it can. These optimizations include precise KV cache allocation, dynamic RAM pressure management, system prompt prefix caching, smart context reduction, hardware-aware model recommendations, and more.
What this means for you is faster response times (especially for agents!), less computer struggles, and more RAM for all your other apps.
Works with Ollama, MLX, and as an OpenAI-compatible API.
I’ve spent a bit of time working on this so it would mean a lot to me if y’all checked it out - I think it can help you.
Thanks everyone!