👁 Mistral
Mistral
Magistral Small 2507
Legacy
1.0KDownloads104LikesJul 2025Released131K tokensContextApache 2.0License96 ExceptionalQuality
Magistral Small 2507 (24B parameters) requires approximately 18.9 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 22 GB of VRAM.
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— copy & paste to run locallyCopy-paste commands to run Magistral Small 2507 on your machine.
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ollama run magistralQuick specs
Parameters24B
Architecturedense
Context131K tokens
Modalitytext
Min RAM9.4 GB
Rec. RAM14.6 GB (Q4_K_M)
LicenseApache 2.0
FamilyMagistral
✓ Chat✓ Reasoning
About this model
- •Reasoning:: Capable of long chains of reasoning traces before providing an answer
- •Multilingual:: Supports dozens of languages, including English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Malay,...
- •Apache 2.0 License:: Open license allowing usage and modification for both commercial and non-commercial purposes
- •Context Window:: A 128k context window, but performance might degrade past 40k. Hence we recommend setting the maximum model length to 40k
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Quantization options
VRAM estimates by quant level
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | — |
Q3_K_S | 3 | 11.8 GB | Low | — |
NVFP4 | 4 | 13.4 GB | Medium | — |
Q4_K_M | 4 | 14.6 GB | Medium | — |
Q5_K_M | 5 | 17.3 GB | High | — |
Q6_K | 6 | 19.7 GB | High | — |
Q8_0 | 8 | 25.7 GB | Very High | — |
F16 | 16 | 49.2 GB | Maximum | — |
Quality benchmarks
Magistral Small 2507 benchmark scores
Reasoning
MMLU-Pro—
GPQA Diamond68.2%
MATH-50095.9%
ARC Challenge—
General
Chatbot Arena—
IFEval87.4%
Source: official · 2025-07-01
Hardware compatibility
Fit estimates across all hardware
Computing compatibility...
Memory breakdown
Reference: RTX 2060 6GB
Weights14.6 GB
KV Cache2.4 GB
Runtime1.2 GB
Headroom0.6 GB
Frequently asked questions
FAQ — Magistral Small 2507
See also