Mistral AI24B~15 GB VRAM at Q4_K_M

Magistral Small 24B — VRAM, speed & local setup

Written by Jakub Rusinowski · Last updated

24B dense reasoning model with image understanding, 128K context, Apache 2.0. Emits long reasoning traces before answering, so budget for the extra tokens. 15.3 GB at Q4_K_M leaves comfortable headroom on a 24 GB card — a genuine local reasoning model rather than a distilled approximation of one.

Magistral Small 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.

Logic88
Creative78
Coding82

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 Weights plus overhead only: this model's architecture is unpublished, so no KV cache is added.

QuantVRAMSpeed (est.)Fit
Q2_K
2.63 bpw
8.7 GB~73 tok/sFits
Q3_K_M
3.41 bpw
11 GB~59 tok/sFits
Q4_K_M
4.83 bpw
15.3 GB~44 tok/sFits
Q5_K_M
5.67 bpw
17.8 GB~39 tok/sFits
Q6_K
6.56 bpw
20.5 GB~34 tok/sFits
Q8_0
8.50 bpw
26.3 GB~4 tok/sOffload
F16
16.00 bpw
48.8 GB~2 tok/sOffload

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

Get Magistral Small 24B running

The cheapest catalogued GPU that runs Magistral Small 24B is the AMD Radeon RX 7900 XT (20 GB).

Buy this hardware AMD Radeon RX 7900 XT 20GB — 20 GB VRAM · 315 W board powerDeploy in the cloud now RTX 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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AMD Radeon RX 7900 XT 20GB
20 GB VRAM · 315 W board power
2026 prices are volatile — check the current listing.

How to run Magistral Small 24B

Install Ollama, then run:

ollama run magistral:24b
Weights on Hugging Face: mistralai/Magistral-Small-2509 ↗

Specifications

Verified — Checked against the primary source — the model card or the vendor spec page — and corroborated by a second independent source.

Parameters
24 Billion
Context window
131,072
Architecture
Dense Transformer (reasoning-tuned, vision)
Provider
Mistral AI
Licence
Apache 2.0
Practical deployment
Single consumer GPU
Specified at
Q4_K_M
System RAM
32 GB
Record updated
2026-09-06
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 forreasoningmathcodingmultimodal

Can I run Magistral Small 24B on my GPU?

Magistral Small 24B — frequently asked questions

How much VRAM does Magistral Small 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 Magistral Small 24B run on an RTX 4090 (24 GB)?

Yes. Magistral Small 24B needs about 15 GB at Q4_K_M, inside a 24 GB card, at an estimated 44 tokens/sec.

How do I run Magistral Small 24B locally?

Install Ollama and run `ollama run magistral:24b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.