This mirror exists to provide a pinned, stable reference for deployment on DGX Spark (GB10). Use the upstream repo if you want to track author updates.

Credits

Base model: mistralai/Devstral-Small-2-24B-Instruct-2512 by Mistral AI
FP8 quantization: by Mistral AI (official release; quant_method: fp8 in config.json)
License: Apache-2.0 (inherited)

Why FP8 (not NVFP4) on GB10

GB10 Blackwell (SM12.1) has native FP8 tensor cores — FP8 GEMM is compute-native
NVFP4 on GB10 runs as W4A16 via Marlin (no CUTLASS FP4 kernel)
Mistral ships official FP8 in this repo → no custom quantization needed
Trade-off vs NVFP4: ~25 GB vs ~12 GB weights; decode ~30% slower (memory-bound)

Model details

Architecture: Mistral3ForConditionalGeneration (multimodal: text + Pixtral vision)
Text backbone: ministral3, 40 layers dense, 24B params
Vision: Pixtral ViT, 24 layers (image-aware; ignored for pure-text usage)
Quantization: FP8 (per-tensor, static activation scheme)
Kept in BF16: vision_tower, multi_modal_projector, lm_head
Max context: 393,216 tokens (YaRN scaling)
SWE-bench Verified: ~55-58% (state-of-art for 24B open-weights SWE agents at release)

Usage

bash

docker run --rm --runtime=nvidia --gpus all \
  -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm/vllm-openai:cu130-nightly \
  gdubicki/Devstral-Small-2-24B-Instruct-2512-FP8 \
  --dtype auto \
  --kv-cache-dtype fp8 \
  --gpu-memory-utilization 0.30 \
  --max-model-len 131072 \
  --tool-call-parser mistral \
  --enable-auto-tool-choice \
  --enable-chunked-prefill \
  --enable-prefix-caching

Agentic / SWE use case

Devstral is tuned for agentic coding workflows (multi-file edits, tool use, long SWE trajectories) — not raw code-gen from prompt. Pair with Cline / aider / OpenHands.