Mistral Small 3 24B VRAM Requirements

Mistral Small 3 24B at a glance

Made by

Mistral AI, France

Released

January 30, 2025

Parameters

23.6B

Context window

32K tokens

License

Apache 2.0, commercial use allowed

Reads

Text only

Languages

English, French, German, Spanish, Italian, Portuguese and 4 more

Knowledge cutoff

October 2023

OpenRouter

On Hugging Face

989 likes, 84K downloads last month (opens in a new tab)

Runs with

vLLM, SGLang, llama.cpp (Ollama, LM Studio), MLX (Mac)

Checked against

KV cache matches the GGUF header of MaziyarPanahi/Mistral-Small-24B-Instruct-2501-GGUF (opens in a new tab) (what llama.cpp allocates), October 30, 2026

VRAM by precision and context length

Weights plus the KV cache of one request (BF16 cache), in GiB. Add 1 to 3 GiB for the inference engine itself.

Mistral Small 3 24B GGUF files

Exact size of each quantization in MaziyarPanahi/Mistral-Small-24B-Instruct-2501-GGUF (opens in a new tab), and the smallest GPU that runs it with llama.cpp, Ollama or LM Studio and an 8K-token context.

Read every day from Hugging Face. Split files are added together; vision projectors are left out.

How many GPUs to run Mistral Small 3 24B

Fewest GPUs that fit the weights plus one 8K-token request. Data-center GPUs use vLLM (90% of memory usable), the others llama.cpp. Several GPUs split the model with tensor or pipeline parallelism.

8-bit: FP8 with vLLM (INT8 on GPUs without FP8), Q8_0 with llama.cpp. 4-bit: AWQ / GPTQ with vLLM, Q4_K_M with llama.cpp, both counted at the Q4_K_M size.

Cheapest way to run Mistral Small 3 24B today

For each GPU: the fewest cards that fit Mistral Small 3 24B with one 8K-token request, the most faithful weight format at that count, and the cheapest on-demand price (2026-10-30).

On-demand prices from AWS, Google Cloud, Azure, RunPod, Vast.ai, Oracle Cloud, OVHcloud, Scaleway, Verda, Akash and UpCloud, checked every hour. Serving many users needs more KV cache, so more memory: size it in the calculator. Some links are affiliate links: StudioTV may earn a commission, at no extra cost to you.

AWS, Google Cloud and UpCloud prices: Spare Cores (opens in a new tab), CC BY-SA 4.0 (opens in a new tab), shared under the same license.

Recommended settings for Mistral Small 3 24B

Sampling defaults from the official generation_config.json (opens in a new tab) of mistralai/Mistral-Small-24B-Instruct-2501.

vLLM and SGLang apply these defaults unless a request sets its own. With llama.cpp, pass them yourself: --temp 0.15

KV cache: how memory grows with context

Architecture: 40 layers · GQA, 8 KV heads × 128. Every layer keeps keys and values for every token (grouped-query attention), so the cache grows linearly with context.

Mistral Small 3 24B VRAM badge

For a model card or a README: the 4-bit size and the smallest GPU it fits on, linked to this page. Data as JSON: /api/models/mistral-small-3.json.

[![Mistral Small 3 24B VRAM](https://studiotvai.com/badge/mistral-small-3.svg)](https://studiotvai.com/vram-requirements/mistral-small-3)

Other models

Same family (dense): Llama 3.1 8B · Llama 3 8B · Llama 3.2 3B · Nanbeige4.3 33B · Granite 4.2 30B · Granite 4.2 8B · Granite 4.2 3B · MiniCPM5 2B · MiniCPM5 1B · Ministral 3 14B · Ministral 3 8B · Ministral 3 3B · Devstral 2 123B · Devstral Small 2 24B · Mistral Medium 3.5 128B · Magistral Small 1.2 24B · Mistral Large 2411 123B · Pixtral Large 124B · Codestral 22B · Mistral Small 2409 22B · Pixtral 12B · Nemotron 3.5 Parse · LLaVA 1.6 Mistral 7B · Mistral Small 3.2 24B · Qwen3 32B · Llama 3.3 70B · Llama 3.1 405B · Youtu-Parsing-Omni · Mistral 7B v0.3 · Mistral Small 3.1 24B · Mistral Nemo · DeepSeek R1 Qwen3 8B · DeepSeek R1 Distill Qwen 32B · DeepSeek R1 Distill Qwen 14B · DeepSeek R1 Distill Qwen 7B · DeepSeek R1 Distill Llama 8B · Qwen3 4B · Qwen3 8B · Qwen2.5 7B · Qwen3 14B · Qwen2.5 3B · Qwen2.5 32B · Qwen2.5 14B · Qwen3-VL 8B · Qwen2.5-VL 7B · Qwen3-VL 4B · Qwen2.5-VL 3B · Qwen2.5-VL 32B · Qwen2-VL 7B · phi-4 · Phi-3.5-mini · Phi-4-mini · Phi-3-mini-128k · WeVisOne-VL 4B · Granite 4.1 3B

Estimates, not guarantees: computed from the official config.json with the same engine as the LLM VRAM Calculator. Real usage depends on your engine version and settings.