{ "hf_id": "poolside/Laguna-XS.2", "meta": { "title": "Laguna XS.2", "slug": "laguna-xs.2", "provider": "Poolside", "description": "Poolside's 33B total / 3B activated MoE coding model with mixed sliding-window + global attention, native interleaved reasoning, and 256K context — designed for agentic coding.", "date_added": "2026-04-28", "date_updated": "2026-04-29", "tasks": [ "text" ], "performance_headline": "33B/3B-A MoE for agentic coding with interleaved thinking and tool use", "related_recipes": [], "hardware": { "h200": "verified", "mi300x": "verified", "mi325x": "verified", "mi355x": "verified" } }, "recommended_command": { "hardware": "h200", "strategy": "single_node_tp", "variant": "default", "node_count": 1, "deploy_type": "single_node", "env": {}, "docker_image": "vllm/vllm-openai:latest", "command": "vllm serve poolside/Laguna-XS.2 \\\n --trust-remote-code \\\n --tensor-parallel-size 1 \\\n --enable-auto-tool-choice \\\n --tool-call-parser poolside_v1 \\\n --reasoning-parser poolside_v1", "argv": [ "vllm", "serve", "poolside/Laguna-XS.2", "--trust-remote-code", "--tensor-parallel-size", "1", "--enable-auto-tool-choice", "--tool-call-parser", "poolside_v1", "--reasoning-parser", "poolside_v1" ], "docker_command": "docker run --gpus all \\\n --privileged --ipc=host -p 8000:8000 \\\n -v ~/.cache/huggingface:/root/.cache/huggingface \\\n vllm/vllm-openai:latest poolside/Laguna-XS.2 \\\n --trust-remote-code \\\n --tensor-parallel-size 1 \\\n --enable-auto-tool-choice \\\n --tool-call-parser poolside_v1 \\\n --reasoning-parser poolside_v1", "docker_argv": [ "docker", "run", "--gpus", "all", "--privileged", "--ipc=host", "-p", "8000:8000", "-v", "~/.cache/huggingface:/root/.cache/huggingface", "vllm/vllm-openai:latest", "poolside/Laguna-XS.2", "--trust-remote-code", "--tensor-parallel-size", "1", "--enable-auto-tool-choice", "--tool-call-parser", "poolside_v1", "--reasoning-parser", "poolside_v1" ], "strategy_spec": { "name": "single_node_tp", "deploy_type": "single_node", "display_name": "Tensor Parallel", "short_label": "TP", "orientation": "latency", "description": "Single-node tensor parallel. Splits the model across all local GPUs. TP size is set to the GPU count at deploy time. The simplest multi-GPU strategy — works for all model architectures.\n", "hardware_match": { "min_gpus": 1, "max_gpus": 8, "multi_node": false }, "vllm_args": [], "parallel_flag": "--tensor-parallel-size" }, "hardware_profile": { "brand": "NVIDIA", "generation": "hopper", "compute_arch": "sm90", "display_name": "H200", "description": "NVIDIA H200 SXM 141 GB HBM3e · 8-GPU HGX node", "gpu_count": 8, "vram_gb": 1128, "multi_node": false }, "alternatives": { "single_node_tep": "/poolside/Laguna-XS.2/strategies/single_node_tep.json", "single_node_dep": "/poolside/Laguna-XS.2/strategies/single_node_dep.json", "kv_store_centralized_mooncake": "/poolside/Laguna-XS.2/strategies/kv_store_centralized_mooncake.json", "kv_store_distributed_mooncake": "/poolside/Laguna-XS.2/strategies/kv_store_distributed_mooncake.json" }, "by_hardware": { "h200": "/poolside/Laguna-XS.2/hw/h200.json", "h100": "/poolside/Laguna-XS.2/hw/h100.json", "b200": "/poolside/Laguna-XS.2/hw/b200.json", "gb200": "/poolside/Laguna-XS.2/hw/gb200.json", "b300": "/poolside/Laguna-XS.2/hw/b300.json", "gb300": "/poolside/Laguna-XS.2/hw/gb300.json", "mi300x": "/poolside/Laguna-XS.2/hw/mi300x.json", "mi325x": "/poolside/Laguna-XS.2/hw/mi325x.json", "mi350x": "/poolside/Laguna-XS.2/hw/mi350x.json", "mi355x": "/poolside/Laguna-XS.2/hw/mi355x.json" } }, "model": { "model_id": "poolside/Laguna-XS.2", "min_vllm_version": "0.21.0", "architecture": "moe", "parameter_count": "33B", "active_parameters": "3B", "context_length": 262144, "base_args": [ "--trust-remote-code" ], "base_env": {}, "install": { "pip": { "command": "uv venv\nsource .venv/bin/activate\nuv pip install -U vllm --torch-backend auto" }, "docker": { "command": "docker pull vllm/vllm-openai:latest" } } }, "features": { "tool_calling": { "description": "Enable automatic tool choice with Poolside's tool-call parser", "args": [ "--enable-auto-tool-choice", "--tool-call-parser", "poolside_v1" ] }, "reasoning": { "description": "Enable interleaved thinking with Poolside's reasoning parser", "args": [ "--reasoning-parser", "poolside_v1" ] }, "spec_decoding": { "description": "DFlash speculative decoding with the Laguna-XS.2 draft model (7 tokens, greedy)", "args": [ "--speculative-config", "{\"model\":\"poolside/Laguna-XS.2-speculator.dflash\",\"num_speculative_tokens\":7,\"method\":\"dflash\"}" ] } }, "opt_in_features": [ "spec_decoding" ], "variants": { "default": { "precision": "bf16", "vram_minimum_gb": 80, "description": "BF16 weights — fits on a single 80GB+ GPU (H100/H200/B200)" }, "fp8": { "model_id": "poolside/Laguna-XS.2-FP8", "precision": "fp8", "vram_minimum_gb": 40, "min_vllm_version": "0.22.0", "description": "Block-FP8 weights + FP8 KV cache — halves VRAM, fits on a single 40GB+ GPU", "extra_env": { "VLLM_BLOCKSCALE_FP8_GEMM_FLASHINFER": "0" }, "json": "/poolside/Laguna-XS.2-FP8.json" }, "nvfp4": { "model_id": "poolside/Laguna-XS.2-NVFP4", "precision": "nvfp4", "vram_minimum_gb": 26, "min_vllm_version": "0.22.0", "description": "NVFP4 weights + FP8 KV cache (Blackwell only) — the most compact variant at ~26GB", "json": "/poolside/Laguna-XS.2-NVFP4.json" }, "int4": { "model_id": "poolside/Laguna-XS.2-INT4", "precision": "int4", "vram_minimum_gb": 29, "min_vllm_version": "0.22.0", "description": "INT4 (W4A16) weights + FP8 KV cache — fits on a single 32GB+ GPU", "json": "/poolside/Laguna-XS.2-INT4.json" }, "redhat_fp8": { "model_id": "RedHatAI/Laguna-XS.2-FP8", "precision": "fp8", "label": "FP8 (RedHat)", "vram_minimum_gb": 40, "description": "RedHatAI FP8 quantization", "extra_args": [ "--kv-cache-dtype", "fp8" ], "json": "/RedHatAI/Laguna-XS.2-FP8.json" }, "redhat_nvfp4": { "model_id": "RedHatAI/Laguna-XS.2-NVFP4", "precision": "nvfp4", "label": "NVFP4 (RedHat)", "vram_minimum_gb": 22, "description": "RedHatAI NVFP4 quantization", "json": "/RedHatAI/Laguna-XS.2-NVFP4.json" } }, "hardware_overrides": {}, "guide": "## Overview\n\n[Laguna XS.2](https://huggingface.co/poolside/Laguna-XS.2) is Poolside's 33B-total / 3B-activated Mixture-of-Experts model purpose-built for agentic coding and long-horizon work. It combines mixed sliding-window + global attention (3:1 across 40 layers) with sigmoid per-head gating and FP8 KV cache, so it stays compact enough to run locally while supporting a 256K-token context.\n\n### Key features\n- **Mixed SWA + global attention**: 30 sliding-window layers (window=512) interleaved with 10 global-attention layers, each with per-layer rotary scaling.\n- **Native FP8 KV cache**: KV cache is quantized to FP8 to reduce memory per token.\n- **Interleaved reasoning**: thinking blocks emitted between tool calls; toggled per-request via `enable_thinking`.\n- **Tool calling**: Poolside-specific XML-style tool-call protocol, parsed via `poolside_v1`.\n- **256 experts + 1 shared expert** with top-8 routing.\n\n## Prerequisites\n\nLaguna XS.2 support is on the open vLLM PR ([vllm-project/vllm#41129](https://github.com/vllm-project/vllm/pull/41129)) — install from a nightly wheel or the pinned Docker image below until the PR lands in a stable release.\n\n### Docker (recommended)\n```bash\ndocker pull vllm/vllm-openai:laguna\n```\n\n### pip (nightly)\n```bash\nuv venv\nsource .venv/bin/activate\nuv pip install -U vllm --pre \\\n --extra-index-url https://wheels.vllm.ai/nightly/cu130 \\\n --extra-index-url https://download.pytorch.org/whl/cu130 \\\n --index-strategy unsafe-best-match\n```\n\n## Quantized variants\n\nPre-quantized checkpoints are published alongside the BF16 weights — select one with the **Variant** control above:\n\n| Variant | Checkpoint | Weights | ~VRAM | Notes |\n|---------|-----------|---------|-------|-------|\n| BF16 | [Laguna-XS.2](https://huggingface.co/poolside/Laguna-XS.2) | BF16 | 80 GB | Default |\n| FP8 | [Laguna-XS.2-FP8](https://huggingface.co/poolside/Laguna-XS.2-FP8) | Block-FP8 | 40 GB | Set `VLLM_BLOCKSCALE_FP8_GEMM_FLASHINFER=0` |\n| NVFP4 | [Laguna-XS.2-NVFP4](https://huggingface.co/poolside/Laguna-XS.2-NVFP4) | NVFP4 | 26 GB | Blackwell only |\n| INT4 | [Laguna-XS.2-INT4](https://huggingface.co/poolside/Laguna-XS.2-INT4) | INT4 (W4A16) | 29 GB | Hopper or Blackwell |\n\nQuantization is detected automatically from each checkpoint's `quantization_config`, so the launch command is identical — just swap the model ID (select the variant above). All three quantized checkpoints ship an **FP8-quantized KV cache**.\n\n> **vLLM ≥ 0.22.0 required for the quantized variants.** The FP8 KV cache needs the per-layer attention-head fix in [vllm#42650](https://github.com/vllm-project/vllm/pull/42650); earlier versions produce scrambled output on non-Hopper GPUs. On older vLLM you can disable the FP8 KV cache with `--kv-cache-dtype-skip-layers $(seq 0 39)`.\n\n## Deployment Configurations\n\n### Single GPU (H100/H200/B200, BF16)\n```bash\nvllm serve poolside/Laguna-XS.2 \\\n --trust-remote-code \\\n --max-model-len 262144 \\\n --enable-auto-tool-choice \\\n --tool-call-parser poolside_v1 \\\n --reasoning-parser poolside_v1\n```\n\n### Docker\n```bash\ndocker run -itd --name laguna-xs2 \\\n --ipc=host --network host --shm-size 16G --gpus all \\\n -v ~/.cache/huggingface:/root/.cache/huggingface \\\n vllm/vllm-openai:laguna \\\n --model poolside/Laguna-XS.2 \\\n --trust-remote-code \\\n --max-model-len 262144 \\\n --enable-auto-tool-choice \\\n --tool-call-parser poolside_v1 \\\n --reasoning-parser poolside_v1 \\\n --host 0.0.0.0 --port 8000\n```\n\n### AMD MI300X / MI325X / MI355X\n\n```bash\nvllm serve poolside/Laguna-XS.2 \\\n --trust-remote-code \\\n --max-model-len 262144 \\\n --enable-auto-tool-choice \\\n --tool-call-parser poolside_v1 \\\n --reasoning-parser poolside_v1\n```\n\n### Docker (AMD MI300X / MI325X / MI355X)\n\n```bash\ndocker run --rm -it \\\n --cap-add=SYS_PTRACE \\\n --ipc=host \\\n --privileged=true \\\n --shm-size=128GB \\\n --network=host \\\n --device=/dev/kfd \\\n --device=/dev/dri \\\n --group-add video \\\n vllm/vllm-openai-rocm:latest \\\n --model poolside/Laguna-XS.2 \\\n --trust-remote-code \\\n --max-model-len 262144 \\\n --enable-auto-tool-choice \\\n --tool-call-parser poolside_v1 \\\n --reasoning-parser poolside_v1 \n```\n\n## Controlling reasoning\n\nReasoning is **off by default** in the chat template. Enable it per-request:\n\n```python\nfrom openai import OpenAI\nclient = OpenAI(base_url=\"http://localhost:8000/v1\", api_key=\"EMPTY\")\n\nresp = client.chat.completions.create(\n model=\"poolside/Laguna-XS.2\",\n messages=[{\"role\": \"user\", \"content\": \"Write a Python retry wrapper with exponential backoff.\"}],\n extra_body={\"chat_template_kwargs\": {\"enable_thinking\": True}},\n temperature=0.7,\n top_p=1.0,\n extra_query={\"top_k\": 20},\n)\nprint(resp.choices[0].message.reasoning)\nprint(resp.choices[0].message.content)\n```\n\nOr default-on with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`.\n\n## Speculative decoding (DFlash)\n\nEnable the **Spec Decoding** toggle above to attach Poolside's [DFlash draft model](https://huggingface.co/poolside/Laguna-XS.2-speculator.dflash) — a 5-layer Llama-style speculator that proposes up to 7 tokens per step. Reported per-position acceptance with reasoning enabled is ~70% at position 1 across coding, math, QA, and writing workloads.\n\nRequires:\n- vLLM built from [PR #41880](https://github.com/vllm-project/vllm/pull/41880) (extends the base Laguna PR with DFlash support).\n- `VLLM_USE_DEEP_GEMM=0` in the launch environment — DeepGEMM is currently incompatible with the DFlash draft path.\n\nExample:\n\n```bash\nVLLM_USE_DEEP_GEMM=0 vllm serve poolside/Laguna-XS.2 \\\n --trust-remote-code \\\n --max-model-len 16384 \\\n --enable-auto-tool-choice \\\n --tool-call-parser poolside_v1 \\\n --reasoning-parser poolside_v1 \\\n --speculative-config '{\"model\":\"poolside/Laguna-XS.2-speculator.dflash\",\"num_speculative_tokens\":7,\"method\":\"dflash\"}'\n```\n\n## References\n\n- [Model card](https://huggingface.co/poolside/Laguna-XS.2)\n- [Release blog post](https://poolside.ai/blog/laguna-a-deeper-dive)\n- [vLLM support PR #41129](https://github.com/vllm-project/vllm/pull/41129)\n- [vLLM DFlash spec-decoding PR #41880](https://github.com/vllm-project/vllm/pull/41880)\n- [DFlash draft model](https://huggingface.co/poolside/Laguna-XS.2-speculator.dflash)\n", "source": { "source": "vllm", "repo": "vllm-project/vllm", "ref": "main", "sha": "389ffebe555705c85337025d5df108d0384d365e", "path": "recipes/models/poolside/Laguna-XS.2.yaml", "edit_url": "https://github.com/vllm-project/vllm/edit/main/recipes/models/poolside/Laguna-XS.2.yaml", "overlays": [] } }