poolside/Laguna-S-2.1

Overview

Laguna S-2.1 is Poolside's 118B-total / 8B-activated Mixture-of-Experts model — the larger sibling of Laguna XS-2.1, built for agentic coding and long-horizon work. It shares the laguna architecture: mixed sliding-window + global attention (3:1 across 48 layers) with softplus per-head gating and an FP8 KV cache, plus native interleaved reasoning and tool calling.

Key features

• Mixed SWA + global attention: 36 sliding-window layers (window=512) interleaved with 12 global-attention layers (3:1), each with per-layer rotary scaling.
• 256 experts + 1 shared expert with top-10 routing.
• Grouped-query attention: 8 KV heads, head dim 128, per-head softplus output gating.
• Native FP8 KV cache to reduce memory per token.
• Interleaved reasoning: thinking blocks emitted between tool calls; toggled per-request via enable_thinking.
• Tool calling: Poolside's XML-style tool-call protocol, parsed via poolside_v1.

Same architecture as XS-2.1, so vLLM needs no S-specific code — support lands with the shared laguna implementation.

Prerequisites

pip

uv pip install -U 'vllm>=0.25.0'

Docker

docker pull vllm/vllm-openai:latest

Variants and VRAM

Laguna S-2.1 is large: the BF16 weights are ~235GB, so unlike XS-2.1 the default does not fit a single GPU — plan for a multi-GPU node or pick a quantized checkpoint. Select one with the Variant control above:

Variant	Checkpoint	Weights	~Size	Fits on
BF16	Laguna-S-2.1	BF16	235 GB	≥2× H200/B200, or a full HGX node
FP8	Laguna-S-2.1-FP8	Block-FP8	121 GB	TP2 on H200, or one B300; set VLLM_BLOCKSCALE_FP8_GEMM_FLASHINFER=0
NVFP4	Laguna-S-2.1-NVFP4	NVFP4	72 GB	single B200/B300 (Blackwell only)
INT4	Laguna-S-2.1-INT4	INT4 (W4A16)	72 GB	single H200/B200

Quantization is detected automatically from each checkpoint's quantization_config, so the launch command is identical — just swap the model ID. All three quantized checkpoints ship an FP8-quantized KV cache.

Precision-matched DFlash speculators ship for each quantization — DFlash-FP8, DFlash-NVFP4, DFlash-INT4 — to pair speculative decoding with a quantized base at matching precision.

Context length. The BF16 checkpoint supports up to 1,048,576 tokens; the quantized checkpoints are configured for 256K. The commands below use --max-model-len 262144; raise or lower it to trade context for KV memory.

Launch command

Multi-GPU node (H200/B200, BF16)

vllm serve poolside/Laguna-S-2.1 \
  --trust-remote-code \
  --tensor-parallel-size 8 \
  --max-model-len 262144 \
  --enable-auto-tool-choice \
  --tool-call-parser poolside_v1 \
  --reasoning-parser poolside_v1

Single GPU (INT4 on H200, or NVFP4 on B200)

vllm serve poolside/Laguna-S-2.1-INT4 \
  --trust-remote-code \
  --max-model-len 262144 \
  --enable-auto-tool-choice \
  --tool-call-parser poolside_v1 \
  --reasoning-parser poolside_v1

Controlling reasoning

Reasoning is off by default in the chat template. Enable it per-request:

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

resp = client.chat.completions.create(
    model="poolside/Laguna-S-2.1",
    messages=[{"role": "user", "content": "Write a Python retry wrapper with exponential backoff."}],
    extra_body={"chat_template_kwargs": {"enable_thinking": True}},
    temperature=0.7,
    top_p=1.0,
    extra_query={"top_k": 20},
)
print(resp.choices[0].message.reasoning_content)
print(resp.choices[0].message.content)

Or default-on with --default-chat-template-kwargs '{"enable_thinking": true}'.

The model's generation_config.json sets top_k 20 (eval-certified truncation) so clients that send no sampling params still get sane output. Do not add min_p when speculative decoding is on — vLLM rejects min_p/logit_bias with speculation; use top_k/top_p.

Speculative decoding (DFlash)

Enable the Spec Decoding toggle above to attach Poolside's DFlash draft model — a parallel drafter that proposes up to 15 draft tokens per step. Measured mean accepted length is ~3.1 tokens/step on natural text (higher on code), matching Poolside's production deployment.

Requires:

• DFlash drafter support for Laguna, merged in PR #46853 (2026-07-03) — use a vLLM build that includes it (validated on 0.25.1).
• --moe-backend triton — the default DeepGEMM MoE backend is incompatible with the DFlash draft path. Enabling the toggle adds this flag.

vllm serve poolside/Laguna-S-2.1 \
  --trust-remote-code \
  --tensor-parallel-size 8 \
  --max-model-len 16384 \
  --enable-auto-tool-choice \
  --tool-call-parser poolside_v1 \
  --reasoning-parser poolside_v1 \
  --speculative-config '{"model":"poolside/Laguna-S-2.1-DFlash","num_speculative_tokens":15,"method":"dflash"}' \
  --moe-backend triton

DGX Spark (GB10)

The NVFP4 checkpoint is validated on a single DGX Spark (GB10, sm_121a) with vLLM 0.25.1 + a FlashInfer nightly and DFlash — see the Laguna-S-2.1-NVFP4 model card for the full one-box recipe.

References

• Model card
• DFlash draft model
• vLLM DFlash spec-decoding PR #46853
• Laguna XS-2.1 recipe