Laguna XS.2
By Poolside · United States
Updated 2026-07-13
Overview
Poolside's first open-weight release: a 33B MoE (3B active) under Apache 2.0 built specifically for agentic coding. Scores 68.2% on SWE-Bench Verified and runs on a 36 GB Mac.
When to pick this model
- Local coding agents on developer laptops (Mac 36 GB or similar)
- Apache 2.0 commercial coding assistants
- Agentic workflows needing native tool calls and streaming
- Frontier-grade SWE-Bench scores without datacenter hardware
- Replacing closed coding APIs with a self-hosted alternative
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 19 GB |
| Q5_K_M | 23 GB |
| Q8_0 | 35 GB |
| FP16 (no quantization) | 66 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Laguna XS.2 wants a 24 GB card at Q4_K_M (19 GB). Stepping up to Q8_0 nearly doubles the footprint to 35 GB, and unquantized FP16 weights take 66 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Laguna XS.2 needs roughly 36 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 15 tokens/sec on entry-level GPUs, on the order of 40 tokens/sec on a mid-range card, and up to 100 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Laguna XS.2 to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Laguna XS.2 |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 19 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 19 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 19 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q5_K_M (23 GB used) |
| 32 GB | RTX 5090 | Q5_K_M (23 GB used) |
Which GPU should you buy to run Laguna XS.2?
To run Laguna XS.2 locally at Q4, you need ~19 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| SWE-Bench Verified | 68.2 |
| SWE-Bench Multilingual | 62.4 |
| SWE-Bench Pro | 44.5 |
| Terminal-Bench 2.0 | 30.1 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- 68.2% SWE-Bench Verified — top-tier among open models
- Runs on a 36 GB Mac
- Apache 2.0 with no commercial restrictions
- Native tool calls and streaming
- Official Ollama tag with multiple quantizations
Limitations
- Coding-specialized — not a general chat model
- MoE + SWA architecture needs transformers v5.6.2 or newer
- Interleaved thinking can slow first-token latency
Typical workloads
In our catalog grid, Laguna XS.2 is filed under Local Agentic Coding, Multi-File Refactor, Apple Silicon Copilot — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline).
The 128k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: MoE 33B/3B active · 256 experts + 1 shared · 40 layers (10 global attention + 30 sliding-window 512) · FP8 KV-cache · 128k ctx
Training: Poolside's first open-weight model, optimized for local agentic coding. Muon optimizer, BF16, native reasoning with interleaved thinking.
The strongest open coding model that actually fits on a developer laptop — Apache 2.0 to boot.
Quick start
ollama run laguna-xs.2Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Laguna XS.2 need?
At the recommended Q4_K_M quantization, Laguna XS.2 needs about 19 GB of VRAM. Q8_0 takes 35 GB, and unquantized FP16 weights take 66 GB.
Can Laguna XS.2 run without a GPU?
Yes — with roughly 36 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Laguna XS.2 support?
Laguna XS.2 supports a 128k-token context window (131,072 tokens).
Can I use Laguna XS.2 commercially?
Yes. Laguna XS.2 is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Laguna XS.2 on consumer hardware?
Our compatibility engine estimates on the order of 40 tokens/sec on a mid-range GPU and up to 100 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Laguna XS.2 should I download first?
Start with Q4_K_M (19 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.