![Pacing the Frontier – Tahuna: AI Training Infrastructure, Now Open Source [P]](https://zhuo7.cn/storage/uploads/covers/2026/09/6ea5f4c7-5cd5-4d90-b420-8b7b8e80f5c0.webp)
Dario says we need to pace the frontier.
Good news: we’ve been pacing Tahuna for months.
Today, Tahuna is open source—as promised back in April.
We built it so small teams could train models, run inference, orchestrate GPUs, and experiment with autonomous research without first becoming a small cloud provider.
The basic workflow:
init → sync → train → artifacts → serve
Under the hood: content-addressed code and data sync, compute provisioning, reproducible manifest-pinned runs, metrics, checkpoints, artifacts, and inference deployments.
We also started building Hillclimb, an autonomous experimentation loop that proposes and runs iterative improvements.
The first public-preview release supports RunPod and R2. It includes Docker self-hosting instructions, a coding-agent setup skill, and examples for SFT, RL agentic search, and MNIST.
Repository: https://github.com/TahunaLabs/tahuna-oss
If you think it sucks, excellent: fork it, fix it, and send a PR so it sucks less for everyone.
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