Magistral Small 24B
By Mistral AI · France
Updated 2026-07-13
Overview
Mistral AI's first open reasoning model, built on Small 3.1 with RL-trained chain-of-thought. Hits 70.7% on AIME24 under Apache 2.0.
When to pick this model
- Math, science, and competition-style problem solving on local hardware
- Transparent reasoning where visible CoT helps debugging
- Reasoning workloads requiring a permissively licensed alternative to DeepSeek R1
- Multi-step planning agents with reasoning budgets under 40k tokens
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 14 GB |
| Q5_K_M | 17 GB |
| Q8_0 | 26 GB |
| FP16 (no quantization) | 48 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Magistral Small 24B needs a 16 GB card at Q4_K_M (14 GB). Stepping up to Q8_0 nearly doubles the footprint to 26 GB, and unquantized FP16 weights take 48 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Magistral Small 24B needs roughly 24 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 4 tokens/sec on entry-level GPUs, on the order of 15 tokens/sec on a mid-range card, and up to 40 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Magistral Small 24B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Magistral Small 24B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 14 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 14 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Q4_K_M (14 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q5_K_M (17 GB used) |
| 32 GB | RTX 5090 | Q8_0 (26 GB used) |
Which GPU should you buy to run Magistral Small 24B?
To run Magistral Small 24B locally at Q4, you need ~14 GB of VRAM. The best value for this is a RTX 5070 Ti (16 GB VRAM).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| AIME 2024 | 70.7 |
| MATH-500 | 90 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
To put Magistral Small 24B in context: its AIME 2024 score of 70.7 ranks #5 of the 8 catalog models with a published AIME 2024 result (catalog median 71.7); its MATH-500 score of 90 ranks #7 of the 8 catalog models with a published MATH-500 result (catalog median 93.3). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.
Strengths
- First open Mistral reasoner with a real RL training pipeline
- AIME24 70.7% — competitive with much larger reasoners
- Apache 2.0 license
- Runs on a single 24GB GPU at Q4
Limitations
- Highly verbose in thinking mode — token costs add up
- Recommended effective context capped around 40k
- Trails DeepSeek R1 distills on hardest math benchmarks
Typical workloads
In our catalog grid, Magistral Small 24B is filed under FR Reasoning, Math, Science — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks; French-language output where quality matters.
The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense 24B · CoT reasoning · Small 3.1 base
Training: RL on reasoning.
Mistral's first credible reasoning model — solid math chops under Apache 2.0, if you can stomach the verbose CoT.
Quick start
ollama run magistral:24bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Magistral Small 24B need?
At the recommended Q4_K_M quantization, Magistral Small 24B needs about 14 GB of VRAM. Q8_0 takes 26 GB, and unquantized FP16 weights take 48 GB.
Can Magistral Small 24B run without a GPU?
Yes — with roughly 24 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Magistral Small 24B support?
Magistral Small 24B supports a 125k-token context window (128,000 tokens).
Can I use Magistral Small 24B commercially?
Yes. Magistral Small 24B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Magistral Small 24B on consumer hardware?
Our compatibility engine estimates on the order of 15 tokens/sec on a mid-range GPU and up to 40 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Magistral Small 24B should I download first?
Start with Q4_K_M (14 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.