Laguna S 2.1-FP8 is a 117.6B total parameter Mixture-of-Experts model with 8.5B activated parameters per token designed for agentic coding and long-horizon work on a local machine. It uses Sliding Window Attention with per-head gating in 36 out of 48 layers for fast inference and low KV cache requirements.
Updated release (August 2026). This is a new checkpoint that supersedes the earlier version of this repository. The weights have changed, not only the config, so if you downloaded a previous copy please re-download to pick up the current checkpoint.
Highlights
Mixed SWA and global attention layout: Laguna S 2.1 uses softplus gating with per-layer rotary scales, enabling mixed SWA (Sliding Window Attention) and global attention layers in a 3:1 ratio (across 48 total layers)
KV cache in FP8: KV cache quantized to FP8, reducing memory per token
Native reasoning support: Interleaved thinking between tool calls with support for enabling and disabling thinking per-request
Local-ready: At 117.6B total parameters and 8.5B activated, the FP8 weights are roughly 121 GB. Available on Ollama and llama.cpp (BF16 and Q4_K_M only)
OpenMDW-1.1 license: Use and modify the model and associated materials freely for commercial and non-commercial purposes (learn more about OpenMDW)
Model overview
Training: pre-training, post-training and reinforcement learning stages
Number of parameters: 117.6B total with 8.5B activated per token
Optimizer: Muon
Layers: 48 layers (12 layers with global attention, 36 layers with sliding window attention)
Experts: 256 experts with 1 shared expert
Sliding Window: 512 tokens
Modality: text-to-text
Context window: 1,048,576 tokens
Reasoning support: interleaved thinking with preserved thinking
Benchmark results
benchmarks
Model
Size
Terminal-Bench 2.1
SWE-bench Multilingual
SWE-Bench Pro (Public Dataset)
DeepSWE
SWE Atlas (Codebase QnA)
Toolathlon Verified
Laguna S 2.1
118B-A8B
70.2%
78.5%
59.4%
40.4%
46.2%
49.7%
Tencent Hy3
295B-A21B
71.7%
75.8%
57.9%
-
-
-
Inkling
975B-A41B
63.8%
-
54.3%
-
-
45.5%*
Nemotron 3 Ultra
550B-A55B
56.4%
67.7%
-
-
-
34.3%*
DeepSeek-V4-Pro Max
1.6T-A49B
64.0%*
76.2%
55.4%
9.0%*
27.2%*
55.9%*
Kimi K3
2800B-A50B
88.3%
-
-
69%
-
-
Qwen 3.7 Max
-
74.5%*
78.3%
60.6%
-
-
-
Muse Spark 1.1
-
80%
-
61.5%
53.3%
42.2%*
75.6%
Claude Fable 5
-
88%
-
80.3%
70%
-
-
Benchmarks as of 21 July 2026. Laguna S 2.1 in bold; a dash (-) marks a benchmark a model was not evaluated on. Scores marked * are as reported by third parties: Terminal-Bench 2.1 and DeepSWE via Artificial Analysis, SWE Atlas via Scale AI's official leaderboard, and Toolathlon Verified via its official leaderboard. Full evaluation trajectories: trajectories.poolside.ai.
Usage
Context length
This checkpoint ships configured for a 1,048,576-token (1M) context window. The weights are native 1M checkpoints: training included a long-context extension stage up to 1,048,576 tokens, and quantization was calibrated at the 1M configuration.
If you prefer to cap the context at 262,144 tokens (256K), edit config.json:
At long context you may experience some quality degradation.
Sampling
The sampling defaults in the checkpoint's generation_config.json are authoritative (top_k 20 is eval-certified truncation). Serve with those defaults rather than setting a separate temperature or top_p.
Local deployment
Laguna S 2.1-FP8 is supported in vLLM, SGLang and Transformers, and TRT-LLM thanks to the support of the team at NVIDIA. Use Laguna S 2.1 with Ollama (with MLX support) or Llama.cpp (BF16 and Q4_K_M only) for the best results on your local machine.
vLLM
Serving requires vLLM 0.25.0 or later. See the main Laguna S 2.1 model card for the full recipe.
The full vLLM recipe is on the main Laguna S 2.1 model card and on the vLLM recipes page. Quantization is detected automatically from quantization_config in this checkpoint, so the same command works with poolside/Laguna-S-2.1-FP8 substituted for the model ID. Set VLLM_BLOCKSCALE_FP8_GEMM_FLASHINFER=0 when serving with vLLM.
[!NOTE]
Optional: speculative decoding with DFlash. Pair with the quantization-matched draft model poolside/Laguna-S-2.1-DFlash-FP8 by adding --speculative-config '{"model":"poolside/Laguna-S-2.1-DFlash-FP8","num_speculative_tokens":7,"method":"dflash"}' to the serve command.
The full Transformers recipe is on the main Laguna S 2.1 model card. Substitute poolside/Laguna-S-2.1-FP8 for the model ID; quantization is detected automatically from quantization_config.
TRT-LLM
Laguna S 2.1 support ships in TensorRT-LLM >=1.3.0rc16; see the install recipe on the main Laguna S 2.1 model card. Substitute poolside/Laguna-S-2.1-FP8 for the model ID; quantization is detected automatically from quantization_config, no extra flags required.
Serve the GGUF builds with Poolside's llama.cpp fork, branch laguna. See the main Laguna S 2.1 model card for the build recipe. llama.cpp serves the BF16, Q8_0, and Q4_K_M GGUF builds, not the FP8 safetensors.
Laguna S 2.1-FP8 uses the same reasoning controls (interleaved thinking, preserved reasoning, and the enable_thinking flag) as the base model. See the Controlling reasoning section of the main Laguna S 2.1 model card.
Laguna S 2.1-FP8 is designed for software engineering and agentic coding use cases, and you are responsible for confirming that it is appropriate for your intended application. Laguna S 2.1-FP8 is subject to the OpenMDW-1.1 License, and should be used consistently with Poolside's Acceptable Use Policy. We advise against circumventing Laguna S 2.1-FP8 safety guardrails without implementing substantially equivalent mitigations appropriate for your use case.
Please report security vulnerabilities or safety concerns to security@poolside.ai.
