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LatestPoolsideM.1
PoolsideText / LLM

Poolside Releases Laguna-M.1, an Open MoE Model

The AI coding startup steps into open weights with an Apache-2.0 mixture-of-experts model built for text and code.

Jun 15, 2026
NotableApache 2.0
Laguna-M.1

Poolside, the startup best known for its work on AI-assisted software development, has published its first open-weights model. Laguna-M.1 arrives on Hugging Face as a mixture-of-experts (MoE) language model aimed at both general text and code generation, released under the permissive Apache-2.0 license.

The model ships with support for two of the most widely used high-throughput inference engines, vLLM and SGLang. That choice signals a focus on practical deployment: teams can run Laguna-M.1 on their own infrastructure without waiting for community patches, which lowers the barrier for evaluation and production testing alike.

Why it matters

Poolside has largely built its reputation behind a closed product, so an openly licensed release is a notable shift. A few reasons it stands out:

  • Permissive licensing: Apache-2.0 allows commercial use and modification, making the model viable for internal tooling.
  • MoE architecture: Mixture-of-experts designs activate only a fraction of parameters per token, a common route to balancing capability and serving cost.
  • Day-one tooling: Built-in vLLM and SGLang compatibility means the model is ready to serve from the start.

Key specifics—including parameter counts, active-expert configuration, and context length—are not detailed in the release record, so prospective users will want to check the model card directly before benchmarking. As an initial release, Laguna-M.1 establishes a baseline for what could become a recurring open-model line from Poolside, and a fresh option for developers weighing open alternatives for coding workloads.

Sources

  • poolside/Laguna-M.1

    Hugging Face

    Visit
OlderMicrosoft's FastContext is a 4B sub-agent for codeMicrosoft · Text / LLM · 5 months agoNewerBoogu-Image 0.1 Edit arrives with Apache licenseBoogu · Image Editing · 5 months ago

Get the model

Hugging Face

Specs

Active params23B active
Context window262K tokens
Size451.6 GB
PrecisionBF16
ArchitectureLagunaForCausalLM
LicenseAPACHE-2.0
Downloads944.3K
Likes149

Can you run it?

Needs a multi-GPU server — about 138.9 GB at 4-bit.

  • BF16 (as published)

    8×H100 80 GB node

    454.9 GB
  • 8-bit

    4×H100 80 GB · 512 GB Mac Studio

    242.9 GB
  • 4-bit

    2×H100 80 GB · 256 GB Mac Studio

    138.9 GB
Your machine
GGUF builds
  • Mixture-of-experts: all 226B parameters must be resident even though only 23B are active per token — active count affects speed, not memory.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

CodeText / LLM

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