Mistral AI123B~75 GB VRAM at Q4_K_M

Devstral-2 123B — VRAM, speed & local setup

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

Written by Jakub Rusinowski · Last updated April 5, 2026

The flagship coding agent, and dense — Mistral deliberately did not build this one sparse, so all 123B stream per token and throughput scales accordingly. Scores 71.6% on SWE-bench Verified, the best open-source coding-agent result at release. Requires about 75 GB at Q4_K_M, so a dual-GPU workstation. Apache 2.0.

Devstral-2 123B needs about 75 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

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VRAM and speed by quantization

Quoted against NVIDIA RTX 4090 (24 GB). Weights plus overhead only: this model's architecture is unpublished, so no KV cache is added.

Quant	VRAM	Speed (est.)	Fit
Q2_K 2.63 bpw	41.2 GB	~3 tok/s	Offload
Q3_K_M 3.41 bpw	53.2 GB	~2 tok/s	Offload
Q4_K_M 4.83 bpw	75.1 GB	—	Too big
Q5_K_M 5.67 bpw	88 GB	—	Too big
Q6_K 6.56 bpw	101.7 GB	—	Too big
Q8_0 8.50 bpw	131.5 GB	—	Too big
F16 16.00 bpw	246.8 GB	—	Too big

Black marker = usable memory on the NVIDIA RTX 4090 (24 GB). Estimates from the memory-bandwidth roofline on the methodology page. Devstral-2 123B VRAM calculator →

Get Devstral-2 123B running

The cheapest catalogued GPU that runs Devstral-2 123B is the NVIDIA DGX Spark (128 GB).

Buy this hardware NVIDIA DGX Spark (128GB) — 128 GB VRAM · 150 W board power Deploy in the cloud now NVIDIA A100 80GB on RunPod — from $1.39/hr · rate checked 2026-07

or compare on Vast.ai from $0.77/hr (typical low · varies)

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NVIDIA DGX Spark (128GB)

128 GB VRAM · 150 W board power

2026 prices are volatile — check the current listing.

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How to run Devstral-2 123B

Install Ollama, then run:

$ollama run devstral:123b

Weights on Hugging Face: mistralai/Devstral-2-123B-Instruct-2512 ↗

Specifications

Preview — The model is released, but these specs are thin or rest on a single source. Individual fields may be wrong.

Parameters: 123 Billion (dense)
Context window: 262,144
Architecture: Dense Transformer
Provider: Mistral AI
Licence: Apache 2.0
Practical deployment: Workstation (large unified memory or pro GPU)
Specified at: Q4_K_M
System RAM: 128 GB
Record updated: 2026-04-05

LicenceApache-2.0Commercial use permitted

Commercial use permitted. No usage restrictions beyond attribution.

Quality and use cases

Scores as published by the model’s authors or an independent evaluator — quality, not throughput, and not measured by us.

Best forsoftware engineeringagentic codingrepo leveltest driven dev

Benchmark	Score	Provenance
SWE-bench Verified	71.6 / 100 %	reported
HumanEval	91.2 / 100 %	reported
LiveCodeBench v6	67.8 / 100 %	reported

Can I run Devstral-2 123B on my GPU?

Other Devstral sizes

Devstral-2 123B — frequently asked questions

How much VRAM does Devstral-2 123B need?

About 75 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.

Does Devstral-2 123B run on an RTX 4090 (24 GB)?

No. Devstral-2 123B needs about 75 GB at Q4_K_M, more than a single RTX 4090's 24 GB. It needs a larger card, several GPUs, or Apple Silicon with enough unified memory — or it runs with part of the weights offloaded to system RAM, which is much slower.

How do I run Devstral-2 123B locally?

Install Ollama and run `ollama run devstral:123b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does Devstral come in?

Devstral-2 123B (75 GB), Devstral 2 22B (14 GB), Devstral Small 2505 24B (15 GB), Devstral Small 2 24B (15 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.