How much VRAM does Magistral Small 1.2 24B need?

About 15.6 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 20.4 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.

Will Magistral Small 1.2 24B run on a 20GB GPU?

Yes — at Q4_K_M and 4K context it needs about 15.6 GB, which leaves 4.4 GB spare on a Radeon RX 7900 XT. That is the smallest card in this index that clears it comfortably; 32 of 76 do.

Which quantization of Magistral Small 1.2 24B should I use?

Q6_K has the lowest published quality loss (0.6%), and Q4_K_M is the level most people run. All 5 levels in the index are Q4_K_M, Q5_K_M, Q6_K, AWQ INT4, EXL2 4.65bpw.

Magistral Small 1.2 24B

24B

Mistral Magistral

Mistral's reasoning model on a Mistral Small 3.2 base, with reasoning wrapped in [THINK] tags. Q4 ~14GB puts explicit chain-of-thought on a 16GB card — the level below the 70B-class reasoners most guides assume.

⬇ 6.2K HF downloads♥ 306 likesmistralai/Magistral-Small-2509· stats from 10/6/2026

Consumer GPUMac / Apple Silicon

131K

Max Context

Quant Variants

GGUF Q6_K

Best Quality

99.4%

Accuracy Retained

Calculate VRAM Hugging Face Compare

Quantization Variants

Per-quant VRAM, quality loss, and inference speed on RTX 4090

Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports

Format	Level	BPW	VRAM	PPL Loss	Speed	Source	Actions
GGUF	Q4_K_M	4.85	14.3 GB	2.9%	60 tok/s	Community	Calc HF
GGUF	Q5_K_M	5.68	16.8 GB	1.4%	54 tok/s	Estimated	Calc HF
GGUF	Q6_K	6.56	19.4 GB	0.6%	47 tok/s	Estimated	Calc HF
AWQ	INT4	4	13.0 GB	4.0%	76 tok/s	Estimated	Calc HF
EXL2	4.65bpw	4.65	13.9 GB	2.5%	—	Estimated	Calc HF

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Running Magistral Small 1.2 24B locally

At Q4_K_M and 4K of context, Magistral Small 1.2 24B needs about 15.6 GB — 13.6 GB of weights, 0.63 GB of KV cache and a 1.4 GB activation buffer. The smallest card in this index that clears that comfortably is the Radeon RX 7900 XT at 20 GB, and 32 of the 76 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.

What longer context costs

Going from 4K to 32K adds about 4.37 GB, taking the total to 20.4 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 128K; holding all of it at Q4_K_M would need about 37 GB.

Which build to download

This index tracks 3 formats for it — GGUF, AWQ, EXL2 — across 5 levels. Q6_K carries the lowest published perplexity loss at 0.6%. The fastest level listed is AWQ INT4 at about 76 tok/s on an RTX 4090, batch 1 — an estimate, not a run on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.

Common questions

How much VRAM does Magistral Small 1.2 24B need?: About 15.6 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 20.4 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
Will Magistral Small 1.2 24B run on a 20GB GPU?: Yes — at Q4_K_M and 4K context it needs about 15.6 GB, which leaves 4.4 GB spare on a Radeon RX 7900 XT. That is the smallest card in this index that clears it comfortably; 32 of 76 do.
Which quantization of Magistral Small 1.2 24B should I use?: Q6_K has the lowest published quality loss (0.6%), and Q4_K_M is the level most people run. All 5 levels in the index are Q4_K_M, Q5_K_M, Q6_K, AWQ INT4, EXL2 4.65bpw.

Where this model fits

Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.

Runs comfortably on: Radeon RX 7900 XT20GB · +4.4 Mac M4 24G24GB · +2.4 Mac M4 Pro 24G24GB · +2.4 RTX 309024GB · +8.4 Radeon RX 7900 XTX24GB · +8.4
Tight but possible: RTX 4060 Ti 16G16GB Radeon RX 7600 XT16GB Radeon RX 9060 XT 16G16GB
Just misses: Mac M3 Pro 18G+2.1GB over RTX 3060 12G+3.6GB over
Ships in: GGUF AWQ EXL2
Compared here: GGUF vs AWQ GGUF vs EXL2 AWQ vs EXL2
A top pick for: Best local LLM for 16GB
Try it yourself: Size it in the calculator Generate the run command

How to actually run this

Deployment guides for this model and this class of hardware.

This page's figures change when the model or the runtime does.Last updated 2026-10-06 RSS → /feed.xml