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Laguna S 2.1 118B-A8B model runs on DGX Station with NVFP4 and FP8 KV Cache, achieving ~1k tokens/second for 10 parallel agents with 256k context each.
Laguna S 2.1, a 120B-class model, impressed by solving a complex coding problem in Julia with long thinking tokens, outperforming Qwen models on a memory-constrained rearrangement task.
The article reports that infinite thinking loops in Laguna S 2.1 AI model are likely caused by quantization artifacts. Switching to an MoE-aware APEX quant (e.g., Myric/Laguna-S-2.1-APEX-GGUF) and using default sampling settings (temp 0.7, top_p 0.95, top_k 20) resolved the looping for most cases. Additionally, framing prompts around tool calls can prevent overthinking.
Laguna S-2.1 model has been updated with a fix for yarn_attn_factor (corrected to 1.0) and an improved chat template that fixes broken thinking, preserves thinking, and enables tool calling. Users are advised to use the updated GGUF from the official repo.
Poolside released Laguna S 2.1, their most capable model for long-horizon tasks, along with multiple quantized variants (FP8, NVFP4, INT4, DFlash, GGUF) on Hugging Face.
Poolside released Laguna S 2.1, and the user acquired 3x AMD V620 GPUs totaling 96 GB VRAM.
Unsloth released GGUF quantizations of the Laguna S 2.1 Mixture-of-Experts model, a 118B parameter coding model with 8B active parameters, 1M context window, and agentic capabilities. The quantized versions enable efficient local deployment.