Poolside has released Laguna S 2.1, a 118B-parameter open-weight model built for agentic coding. It is a Mixture-of-Experts (MoE) model with 8B activated parameters per token. It supports a context window of up to 1M tokens in both thinking and no-thinking modes. The weights are on Hugging Face under an OpenMDW-1.1 license, and the model is small enough to run on a single NVIDIA DGX Spark.
On long-horizon coding benchmarks, Laguna S 2.1 holds its own against models several times its size, including DeepSeek-V4-Pro-Max, NVIDIA’s Nemotron 3 Ultra, and Thinking Machines’ Inkling. Laguna S 2.1 is a scale-up of the Laguna XS family, trained on the same pre-training data as XS 2.1.
What is Laguna S 2.1
The model activates roughly 6.8% of its parameters on any given token. All 118B parameters remain resident in memory, but only ~8B route through the network per step. That sparsity is why a mid-size model can behave like a larger one while staying cheap to serve.
Poolside team publishes weights in BF16, FP8, INT4, and NVFP4, along with official GGUF and MLX conversions and DFlash draft models. It went from the start of training to launch in under nine weeks. Pre-training began on 22 May 2026 on 4,096 NVIDIA H200 GPUs. It is the first Poolside model where reinforcement learning ran in FP8 precision.