In close collaboration with Poolside, we’re happy to announce the support of Laguna S 2.1, their new open-weight model for agentic coding and long-horizon work. It’s available today in ZML/LLMD with DFlash support. Most importantly (and as always with us), it’s available on all the supported platforms: NVIDIA, AMD, Intel, Google TPU and Apple Metal. It's a good model, sir. https://lnkd.in/eia_WSux
Today we’re releasing Laguna S 2.1, our new open-weight model for agentic coding and long-horizon work. Laguna S 2.1 is a 118B-parameter Mixture-of-Experts model with 8B active parameters per token, up to 1M tokens of context, and thinking and no-thinking modes. It is capable enough to compete with models several times its size, yet small enough to run locally on a single NVIDIA DGX Spark. What sets Laguna S 2.1 apart is its persistence. Across long-horizon coding and research tasks, it holds onto a goal, uses tools, checks its work, recovers when an approach fails, and continues making progress for hours with little or no intervention. That persistence comes with a practical balance of cost, speed, and ownership. Laguna S 2.1 weight class is designed to take on real, long-running agentic work at a cost and speed that make it practical to run often and at scale. Because it activates only 8B parameters per token, long agent runs and reinforcement learning loops are faster and less expensive than they would be with much larger models. We’re releasing Laguna S 2.1 under OpenMDW-1.1 with checkpoints in BF16, FP8, INT4, and NVFP4, alongside official GGUF and MLX quantizations. The weights are available today on Hugging Face. Run it through pool, vLLM, SGLang, Ollama, llama.cpp, ZML, MLX, or NVIDIA TensorRT-LLM, or access it through OpenRouter and the Poolside API.