Devstral Small 2 24B — VRAM, Speed & Local Setup

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 11 września 2026

Model libraryDevstral → Devstral Small 2 24B

The second-generation Devstral Small, and the reason this family matters to anyone with one GPU: 68.0% on SWE-bench Verified — up 21 points on the 2505 above — at the same 24B dense, about 15.3 GB at Q4_K_M on a single RTX 4090 or a 32 GB Mac. 256K maximum context and multimodal input, so "read this screenshot and fix the CSS" works. Apache 2.0, with a first-party Ollama tag.

Devstral Small 2 24B needs about 15 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.

Specifications

Parameters24 Billion
Context window262,144
ArchitectureDense Transformer (vision)
ProviderMistral AI
LicenceApache 2.0
Specified atQ4_K_M
System RAM32 GB
Record updated2026-09-11

Licence

Apache-2.0commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB). Weights plus framework overhead only — this model publishes no architecture we can read, so no KV cache is included. A real session needs more; the figure is a floor, not a target. Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantBits/weightWeightsVRAM neededEst. speedFit on 24 GB
Q2_K2.637.9 GB8.7 GB~73 tok/s (est.)Fits comfortably
Q3_K_M3.4110.2 GB11 GB~59 tok/s (est.)Fits comfortably
Q4_K_M4.8314.5 GB15.3 GB~44 tok/s (est.)Fits comfortably
Q5_K_M5.6717 GB17.8 GB~39 tok/s (est.)Fits comfortably
Q6_K6.5619.7 GB20.5 GB~34 tok/s (est.)Fits comfortably
Q8_08.5025.5 GB26.3 GB~4 tok/s (est.)Offloads to system RAM (slow)
F1616.0048 GB48.8 GB~2 tok/s (est.)Offloads to system RAM (slow)

Want to set your own context length and KV-cache quantization? Use the interactive VRAM calculator.

Buy This HardwareAMD Radeon RX 9060 XT 16GB — 16 GB VRAM · 160 W board powerDeploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Recommended GPU

The cheapest catalogued GPU that runs Devstral Small 2 24B is the AMD Radeon RX 9060 XT 16GB (16 GB).

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AMD Radeon RX 9060 XT 16GB
16 GB VRAM · 160 W board power
Ceny w 2026 są niestabilne — sprawdź aktualną ofertę.
Sprawdź cenę na Amazon

How to Run Devstral Small 2 24B

Install Ollama, then run:

ollama run devstral-small-2:24b

Weights on Hugging Face: mistralai/Devstral-Small-2-24B-Instruct-2512.

Published Benchmark Scores

Quality scores as published by the model's authors or an independent evaluator — not throughput, and not measured by us.

BenchmarkScoreProvenance
SWE-bench Verified68 / 100 %reported · https://mistral.ai/news/devstral-2-vibe-cli/

Best for: software engineering, agentic coding, consumer gpu, multimodal.

Can I Run Devstral Small 2 24B on My GPU?

Other Devstral Sizes

Devstral Small 2 24B — Frequently Asked Questions

How much VRAM does Devstral Small 2 24B need?
About 15 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 Small 2 24B run on an RTX 4090 (24 GB)?
Yes. Devstral Small 2 24B needs about 15 GB at Q4_K_M, inside a 24 GB card, at an estimated 44 tokens/sec.
How do I run Devstral Small 2 24B locally?
Install Ollama and run `ollama run devstral-small-2:24b`. 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 (Unverified Listing) (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.

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