Laguna XS 2.1 — Poolside opens its weights, and it actually fits your GPU

Poolside — the coding-model lab that trains from scratch rather than fine-tuning someone else's base — published open weights for the first time on July 2: Laguna XS 2.1, a 33B-total / 3B-active MoE trained on 30 trillion tokens for agentic coding, under the permissive OpenMDW-1.1 license. Unlike almost every frontier-lab drop this year, this one is genuinely consumer-runnable: official BF16/FP8/INT4/NVFP4 checkpoints, an official GGUF repo, and DFlash speculator models that Poolside claims double local decode speed. At INT4 it fits a 24 GB card. It lands as a fast take plus a model entry — not yet a planner pick.

Verdict: The first open-weight model from a from-scratch coding lab — 33B-A3B, fits a 24 GB card at 4-bit, permissive OpenMDW license

The take

The facts, verified against the Hugging Face repos via the HF API (`poolside/Laguna-XS-2.1`, license tag openmdw-1.1, safetensors total 33.4B; official `Laguna-XS-2.1-GGUF` repo created June 29; public announcement and blog July 2): 33B MoE with ~3B active, trained from scratch on 30T tokens, agentic-coding focus, 256K context on the hosted API, runs under vLLM/SGLang/TensorRT-LLM/transformers with llama.cpp support arriving via the official GGUFs. OpenMDW-1.1 is the Linux Foundation's permissive model license — genuinely open for commercial use, though not a classic OSI software license. Also on OpenRouter (free and paid tiers) if you want to try before downloading ~20 GB.

Why it matters: every previous open model in the local agentic-coding class (Qwen3-Coder-30B-A3B, North Mini Code) descends from a general-purpose lab's training pipeline. Poolside is a coding-first lab whose entire training stack — data, RL environments, evaluation — is purpose-built for software engineering, and this is the first time any of it ships as downloadable weights. The 33B-A3B shape is exactly the format that made Qwen3-Coder the community daily driver: big enough to be capable, sparse enough to be fast on one GPU.

Our call: model entry, no planner pick yet. Two reasons: the benchmark story is entirely vendor-reported so far, and our coding picks follow proven community adoption — Qwen3-Coder-30B-A3B and North Mini Code have months of real-repo track record. If Laguna earns the same signal (independent SWE-bench-class numbers, community quants, r/LocalLLaMA daily-driver reports), it is a natural third option in the same VRAM class. Meanwhile: if you run agentic coding locally on a 24 GB card, this is the most interesting thing to try since North Mini Code — verify on your own repo.

Where this fits

Models: Qwen3-Coder-30B-A3B · North Mini Code (30B-A3B) · Qwen 3.5 35B-A3B · Ornith-1.0 (9B / 31B / 35B-MoE / 397B-MoE)

Hardware: NVIDIA RTX 4090 · NVIDIA RTX 5090 · NVIDIA RTX 3090 (used, single)

Sources

Next step

Try this in the planner→