Turns out, the next Laguna also fits on a @NVIDIAAI DGX Spark 👀

Today we’re releasing Laguna XS 2.1. It’s a small upgrade to the Laguna XS.2 model, the same 33B total / 3B active MoE and stronger results on multilingual coding and terminal-style tasks. Available now on @huggingface, @OpenRouter, and via Poolside API.

Today we’re releasing the weights for Laguna M.1, our most capable model to date, with a 256K context length. Both base and post-trained checkpoints are now available on Hugging Face under Apache 2.0.

Today we’re publishing the technical report behind Laguna M.1 and Laguna XS.2. This report opens up more of what went into them: Model Factory, pre-training data, distributed training, post-training, agent RL, quantization, and evaluation. poolside.ai/assets/laguna/la…

Today we’re releasing Laguna XS.2, Poolside’s first open-weight model. It’s a 33B total / 3B active MoE model built for agentic coding and long-horizon tasks. Trained fully in-house on our own stack. Runs on a single GPU. Released under Apache 2.0. Links 👇 Weights: huggingface.co/poolside/Lagu… API: platform.poolside.ai/ Blog: poolside.ai/blog/laguna-a-de…

Training AI models requires storing temporary data mid-process. That data sits in GPU memory taking up space until it's needed. The standard fix has always been to delete it and redo the work later. It works, but it's wasteful.

Hello world! We've launched our site & written about the journey we're embarking on... poolside.ai