Poolside

Laguna S 2.1

Name: Laguna S 2.1
Rating: 88 (29 reviews)
Author: Poolside

前沿

HuggingFace

45.3K下载量627点赞Jul 2026发布日期1.0M tokens上下文OpenMDW 1.1许可证83 优秀质量

Laguna S 2.1 (117.5999984741211B parameters) requires approximately 74.0 GB of VRAM with Q4_K_M quantization. As a Mixture of Experts model with 8B active parameters, it uses less memory than its total parameter count suggests. For the best balance of quality and speed, we recommend hardware with at least 86 GB of VRAM.

快速开始

— 复制粘贴即可本地运行

Copy-paste commands to run Laguna S 2.1 on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "poolside/Laguna-S-2.1" \
  --hf-file "Laguna-S-2.1-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Quick specs

Parameters117.6B (8B active)

Architecturemoe (MoE)

Context1.0M tokens

Modalitytext

Min RAM45.9 GB

Rec. RAM71.7 GB (Q4_K_M)

LicenseOpenMDW 1.1

FamilyLaguna

✓ Code✓ Reasoning

About this model

Laguna S 2.1 is Poolside's 118B-total Mixture-of-Experts model with ~8B parameters activated per token, built for agentic coding and long-horizon work. It uses 256 routed experts plus one shared expert with a softplus token-choice router, grouped-query attention, and a 1:3 global-to-sliding-window attention layout across 48 layers, with a 1M-token context.

•118B total / ~8B active per token — 256 routed experts plus 1 shared expert.
•1,048,576-token context window for long-horizon agentic coding.
•Hybrid attention: 12 global layers + 36 sliding-window layers (window 512), cutting KV-cache cost.
•Native interleaved reasoning between tool calls, plus a DFlash draft model for speculative decoding.
•Official FP8, NVFP4, INT4 and GGUF variants published by Poolside.

Laguna S 2.1 的最佳选择

AMD Instinct MI250X 128GBS

128 GB

AMD Instinct MI300A 128GBS

128 GB

AMD Instinct MI250 128GBS

128 GB

Gaudi 3 128GBS

128 GB

Intel Data Center GPU Max 1550 128GBS

128 GB

运行此模型

Laguna S 2.1 on AMD Instinct MI250X 128GB Laguna S 2.1 on AMD Instinct MI300A 128GB Laguna S 2.1 on AMD Instinct MI250 128GB

量化选项

各量化级别的 VRAM 估算

No hardware detected — fit column shows raw VRAM estimates

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	16.9 GB	Very Low	—
Q2_0_G128	1.71	31.4 GB	Low	—
Q2_K	2	45.9 GB	Low	—
Q3_K_S	3	57.6 GB	Low	—
NVFP4	4	65.9 GB	Medium	—
Q4_K_M	4	71.7 GB	Medium	—
Q5_K_M	5	84.7 GB	High	—
Q6_K	6	96.4 GB	High	—
Q8_0	8	125.8 GB	Very High	—
F16	16	241.1 GB	Maximum	—

硬件兼容性

全部硬件的适配估算

打开计算器

Computing compatibility...

内存详细分析

Reference: RTX 2060 6GB

Weights71.7 GB

KV Cache0.7 GB

Runtime0.9 GB

Headroom0.6 GB

常见问题

FAQ — Laguna S 2.1

另请参阅

量化指南评分方法打开计算器