LLMs · Mistral AI

Devstral 2 (123B)

Name: Devstral 2 (123B)
Author: Mistral AI

Parameters: 125B
Architecture: Dense
Max context: 256K tokens
License: modified-mit
Released: 2025-12

How to run it

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run devstral-2:123b

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/unsloth/Devstral-2-123B-Instruct-2512-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Devstral-2-123B-Instruct-2512-GGUF

Original weights · Hugging Face

Full-precision files from Mistral AI, for fine-tuning, vLLM, Transformers.

huggingface.co/mistralai/Devstral-2-123B-Instruct-2512 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Mistral AI. The weights themselves download from Hugging Face.

github.com/mistralai/mistral-inference (opens in a new tab)

Mistral AI’s official inference code.

git clone https://github.com/mistralai/mistral-inference

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

Apps that run Devstral 2 (123B)

Our pick for each kind of computer, assuming the model fits in its memory. Pick your own GPU or device in the Model Finder for an exact answer.

Mac (Apple silicon)

LM Studio

Runs the MLX build, typically up to 10% faster than GGUF for a model with 125B active parameters on Apple silicon.

Also good:

Windows PC, NVIDIA GPU

Ollama

One command with Ollama’s own build of this model.

Also good:

Windows PC, AMD Radeon

Lemonade

AMD’s own app: llama.cpp tuned for Radeon on Vulkan and ROCm.

Also good:

Linux, NVIDIA GPU

Ollama

One command with Ollama’s own build of this model.

Also good:

Linux server, 80 GB+ GPUs

vLLM

The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.

Also good:

App	Type	Opens it as	Systems	Engine	Start
LM Studio	Desktop app	GGUF or MLX	macOSWindowsLinux	llama.cpp, MLX	Open in LM Studio
Ollama	CLI + API	Ollama library	macOSWindowsLinux	llama.cpp, MLX	`ollama run devstral-2:123b`
llama.cpp	CLI + API	GGUF	macOSWindowsLinux	llama.cpp	`llama-server -hf unsloth/Devstral-2-123B-Instruct-2512-GGUF:Q4_K_M`
Jan	Desktop app	GGUF or MLX	macOSWindowsLinux	llama.cpp, MLX	Open in Jan
MLX LM	CLI + API	MLX	macOS	MLX	`mlx_lm.chat --model mlx-community/Devstral-2-123B-Instruct-2512-4bit`
vLLM	Server	Original weights	WSLWindowsLinux	vLLM	`vllm serve mistralai/Devstral-2-123B-Instruct-2512`
SGLang	Server	Original weights	WSLWindowsLinux	SGLang	`python3 -m sglang.launch_server --model-path mistralai/Devstral-2-123B-Instruct-2512`
Docker Model Runner	CLI + API	GGUF	macOSWindowsLinux	llama.cpp, vLLM	`docker model run hf.co/unsloth/Devstral-2-123B-Instruct-2512-GGUF:Q4_K_M`
Lemonade	Desktop app	GGUF	macOSWindowsLinux	llama.cpp, ONNX Runtime GenAI	`lemonade pull unsloth/Devstral-2-123B-Instruct-2512-GGUF:Q4_K_M`
Unsloth Desktop	Desktop app	GGUF or MLX	macOSWindowsLinux	llama.cpp, MLX, stable-diffusion.cpp	Open in Unsloth Desktop
Atomic Chat	Desktop app	GGUF or MLX	macOSWindowsLinux	llama.cpp, MLX-VLM	Open in Atomic Chat
Pi	Coding agent	GGUF or MLX	macOSWindowsLinux	uses llama.cpp or MLX LM	Point it at a llama.cpp or MLX LM server
Hermes Agent	Assistant	GGUF or MLX	macOSWSLWindowsLinux	uses llama.cpp or MLX LM	Point it at a llama.cpp or MLX LM server
OpenClaw	Assistant	GGUF or MLX	macOSWSLWindowsLinux	uses llama.cpp or MLX LM	Point it at a llama.cpp or MLX LM server
OpenCode	Coding agent	GGUF or MLX	macOSWindowsLinux	uses llama.cpp, Ollama or LM Studio	Point it at a llama.cpp or MLX LM server
Codex CLI	Coding agent	GGUF or MLX	macOSWSLWindowsLinux	uses Ollama or any OpenAI-compatible server	Point it at a llama.cpp or MLX LM server
OpenHands	Coding agent	GGUF or MLX	macOSWSLWindowsLinux	uses llama.cpp, Ollama or LM Studio	Point it at a llama.cpp or MLX LM server
Zed	Coding agent	GGUF or MLX	macOSWindowsLinux	uses llama.cpp, Ollama or LM Studio	Point it at a llama.cpp or MLX LM server
Cline	Coding agent	GGUF or MLX	macOSWindowsLinux	uses llama.cpp, Ollama or LM Studio	Point it at a llama.cpp or MLX LM server
Open Interpreter	Coding agent	GGUF or MLX	macOSWindowsLinux	uses llama.cpp, Ollama or LM Studio	Point it at a llama.cpp or MLX LM server
Goose	Coding agent	GGUF or MLX	macOSWindowsLinux	uses llama.cpp, Ollama or LM Studio	Point it at a llama.cpp or MLX LM server
Aider	Coding agent	GGUF or MLX	macOSWindowsLinux	uses llama.cpp, Ollama or LM Studio	Point it at a llama.cpp or MLX LM server
Crush	Coding agent	GGUF or MLX	macOSWindowsLinux	uses llama.cpp, Ollama or LM Studio	Point it at a llama.cpp or MLX LM server
Qwen Code	Coding agent	GGUF or MLX	macOSWindowsLinux	uses llama.cpp, Ollama or LM Studio	Point it at a llama.cpp or MLX LM server
Kilo Code	Coding agent	GGUF or MLX	macOSWindowsLinux	uses llama.cpp, Ollama or LM Studio	Point it at a llama.cpp or MLX LM server
Open WebUI	Assistant	GGUF or MLX	macOSWindowsLinux	uses llama.cpp, Ollama or LM Studio	Point it at a llama.cpp or MLX LM server
Khoj	Assistant	GGUF or MLX	macOSWindowsLinux	uses llama.cpp, Ollama or LM Studio	Point it at a llama.cpp or MLX LM server
Letta	Assistant	GGUF or MLX	macOSWindowsLinux	uses llama.cpp, Ollama or LM Studio	Point it at a llama.cpp or MLX LM server
GhidrAssist	RE plugin	GGUF or MLX	macOSWindowsLinux	uses Ollama, LM Studio or any OpenAI-compatible server	Add it to Ghidra, then point it at a local model server
ReVa	RE plugin	GGUF or MLX	macOSWindowsLinux	uses your MCP client’s model server	Add it to Ghidra, then point it at a local model server
GhidraMCP	RE plugin	GGUF or MLX	macOSWindowsLinux	uses your MCP client’s model server	Add it to Ghidra, then point it at a local model server
OGhidra	RE plugin	GGUF or MLX	macOSWindowsLinux	uses Ollama	Add it to Ghidra, then point it at a local model server
Gepetto	RE plugin	GGUF or MLX	macOSWindowsLinux	uses Ollama or an OpenAI-compatible server	Add it to IDA Pro, then point it at a local model server
IDA Pro MCP	RE plugin	GGUF or MLX	macOSWindowsLinux	uses your MCP client’s model server	Add it to IDA Pro, then point it at a local model server
BinAssist	RE plugin	GGUF or MLX	macOSWindowsLinux	uses Ollama, LM Studio or any OpenAI-compatible server	Add it to Binary Ninja, then point it at a local model server
r2ai and decai	RE plugin	GGUF or MLX	macOSWindowsLinux	uses Ollama, llama.cpp or LM Studio	Add it to radare2, then point it at a local model server
JADX AI MCP	RE plugin	GGUF or MLX	macOSWindowsLinux	uses your MCP client’s model server	Add it to JADX, then point it at a local model server
KoboldCpp	Desktop app	GGUF	macOSWindowsLinux	llama.cpp	Get KoboldCpp
Text Generation WebUI	Desktop app	GGUF	macOSWindowsLinux	llama.cpp, Transformers, ExLlamaV3	Get Text Generation WebUI
llamafile	CLI + API	GGUF	macOSWindowsLinux	llama.cpp	Get llamafile
Msty Studio	Desktop app	GGUF or MLX	macOSWindowsLinux	Ollama, llama.cpp, MLX	Open in Msty Studio
Llama (macOS)	Desktop app	GGUF	macOS	llama.cpp	Open in Llama (macOS)
RecurseChat	Desktop app	GGUF	macOS	llama.cpp	Open in RecurseChat
Inferencer	Desktop app	MLX	macOS	MLX	Get Inferencer
node-llama-cpp	Library	GGUF	macOSWindowsLinux	llama.cpp	Get node-llama-cpp
exo	Server	MLX	macOSLinux	MLX	Get exo
Continue	Coding agent	GGUF or MLX	macOSWindowsLinux	uses Ollama, LM Studio or llama.cpp	Point it at a llama.cpp or MLX LM server
SillyTavern	Assistant	GGUF or MLX	macOSWindowsLinux	uses Ollama, LM Studio or llama.cpp	Point it at a llama.cpp or MLX LM server
LibreChat	Assistant	GGUF or MLX	macOSWindowsLinux	uses Ollama, LM Studio or llama.cpp	Point it at a llama.cpp or MLX LM server
Cherry Studio	Assistant	GGUF or MLX	macOSWindowsLinux	uses Ollama, LM Studio or llama.cpp	Point it at a llama.cpp or MLX LM server
Chatbox	Assistant	GGUF or MLX	macOSWindowsLinux	uses Ollama, LM Studio or llama.cpp	Point it at a llama.cpp or MLX LM server
PrivateGPT	Assistant	GGUF or MLX	macOSWindowsLinux	uses Ollama, LM Studio or llama.cpp	Point it at a llama.cpp or MLX LM server
PocketPal AI	Mobile app	GGUF	iOSAndroid	llama.cpp	App Store Google Play
ChatterUI	Mobile app	GGUF	Android	llama.cpp	Download the APK
LLM Farm	Mobile app	GGUF	iOS	llama.cpp	Not on the App Store or TestFlight for now (checked 2026-11-09).

Which apps can open a model depends on the files published for it: GGUF for llama.cpp-based apps, MLX for Apple’s engine, the original weights for vLLM and SGLang. Compare every app.

Memory needed

Weights plus KV cache and runtime overhead. Leave a little extra free for the operating system and other apps. Try another context length.

Quantization	Quality	Weights	Total at 8K context	Total at 32K context	Download
`Q2_K`	Low	50.0 GB	53.6 GB	62.4 GB	GGUF (opens in a new tab)
`Q3_K_M`	Low	61.3 GB	64.8 GB	73.7 GB	GGUF · folder (opens in a new tab)
`Q4_K_M`	Good	76.3 GB	79.8 GB	88.7 GB	GGUF · folder (opens in a new tab)
`Q5_K_M`	High	88.8 GB	92.3 GB	101 GB	GGUF · folder (opens in a new tab)
`Q6_K`	High	103 GB	106 GB	115 GB	GGUF · folder (opens in a new tab)
`Q8_0`	Very high	133 GB	136 GB	145 GB	GGUF · folder (opens in a new tab)
`F16`	Full	250 GB	254 GB	262 GB	GGUF · folder (opens in a new tab) Original weights (opens in a new tab)

Best hardware for Devstral 2 (123B)

The least expensive GPU in our catalog that runs it without offloading is the NVIDIA RTX PRO 6000 Blackwell (launch price $8,565) at Q4_K_M.

Quality	Hardware	Quant	Memory	Speed	Runs
81Great quality (A) · 81 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA RTX PRO 6000 Blackwell	`Q4_K_M`	84.6 GB / 96.0 GB	17.6 tok/s	Runs welltight fit
60Good quality (B) · 60 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking.	Apple M2 Ultra (192 GB)	`Q2_K`	65.1 GB / 144 GB	11.1 tok/s	Runs well
60Good quality (B) · 60 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking.	Apple M3 Ultra (256 GB)	`Q2_K`	68.9 GB / 192 GB	11.4 tok/s	Runs well
60Good quality (B) · 60 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking.	Apple M3 Ultra (512 GB)	`Q2_K`	84.3 GB / 384 GB	11.4 tok/s	Runs well
86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA B200	`Q8_0`	145 GB / 180 GB	40.0 tok/s	Runs great
83Great quality (A) · 83 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA H200 141GB SXM	`Q5_K_M`	99.4 GB / 141 GB	38.9 tok/s	Runs great
83Great quality (A) · 83 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA H200 NVL	`Q5_K_M`	99.4 GB / 141 GB	38.9 tok/s	Runs great
81Great quality (A) · 81 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GH200 Grace Hopper (96 GB GPU)	`Q4_K_M`	84.6 GB / 96.0 GB	39.3 tok/s	Runs welltight fit
81Great quality (A) · 81 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA H20	`Q4_K_M`	84.6 GB / 96.0 GB	39.3 tok/s	Runs welltight fit
81Great quality (A) · 81 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA H100 NVL	`Q4_K_M`	84.5 GB / 94.0 GB	38.3 tok/s	Runs welltight fit

Check it on your own hardware

Run it on a cluster

Devices that can’t run Devstral 2 (123B) alone can run it together. These example clusters do:

Try it with your own devices

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.