Magistral Small — lokalny model AI od Mistral AI
Autor: Jakub Rusinowski · Ostatnia aktualizacja:
Mistral's reasoning line: Mistral Small with long chain-of-thought added via SFT on Magistral Medium traces plus RL. Apache 2.0, vision-capable, and sized so the whole thing fits a single RTX 4090 or a 32 GB MacBook once quantized — which is the point. Reasoning traces degrade past roughly 40K tokens even though the context window runs to 128K.
Warianty
Najmniejszy wariant Magistral Small potrzebuje około 15 GB VRAM przy Q4_K_M — skwantyzowane wagi plus narzut frameworka, bez pamięci podręcznej KV.
| Model | VRAM przy Q4 | VRAM | Kontekst | Uruchom |
|---|---|---|---|---|
| Magistral Small 24B → 24B | ~15.3 GB | 131,072 | ollama run magistral:24b |
Pamięć to skwantyzowane wagi plus narzut przy Q4_K_M, liczone tym samym silnikiem co sprawdzarka GPU i VRAM.
Jak uruchomić Magistral Small lokalnie
Zainstaluj Ollama, a potem pobierz tag.
ollama run magistral:24bWybierz rozmiar powyżej, aby zobaczyć jego VRAM, szacowaną szybkość i polecenie instalacji.
Licencja
Commercial use permitted. No usage restrictions beyond attribution.
Dotyczy: Magistral Small 24BPolecane GPU
Najtańsze GPU z naszego katalogu, które uruchomi Magistral Small lokalnie (min. 15 GB VRAM), to AMD Radeon RX 7900 XT (20 GB).
Czy uruchomię Magistral Small na moim GPU?
- Magistral Small na AMD Radeon RX 7800 XT
- Magistral Small na AMD Radeon RX 7900 XT
- Magistral Small na AMD Radeon RX 7900 XTX
- Magistral Small na AMD Radeon RX 9060 XT 16GB
- Magistral Small na AMD Radeon RX 9060 XT 8GB
- Magistral Small na AMD Radeon RX 9070
- Magistral Small na AMD Radeon RX 9070 XT
- Magistral Small na Apple M1
- Magistral Small na Apple M1 Pro
- Magistral Small na Apple M2
- Magistral Small na Apple M2 Pro
- Magistral Small na Apple M3
- Magistral Small na Apple M3 Pro
- Magistral Small na Apple M4
- Magistral Small na Apple M4 Pro
- Magistral Small na Apple M5
- Magistral Small na Intel Arc B570
- Magistral Small na Intel Arc B580
- Magistral Small na NVIDIA GeForce RTX 3060 (12GB)
- Magistral Small na NVIDIA GeForce RTX 3070
- Magistral Small na NVIDIA GeForce RTX 3070 Ti
- Magistral Small na NVIDIA GeForce RTX 3080 (10GB)
- Magistral Small na NVIDIA GeForce RTX 3090
- Magistral Small na NVIDIA GeForce RTX 4060
- Magistral Small na NVIDIA GeForce RTX 4060 Ti 16GB
- Magistral Small na NVIDIA GeForce RTX 4070
- Magistral Small na NVIDIA GeForce RTX 4070 Super
- Magistral Small na NVIDIA GeForce RTX 4070 Ti
- Magistral Small na NVIDIA GeForce RTX 4070 Ti Super
- Magistral Small na NVIDIA GeForce RTX 4080
- Magistral Small na NVIDIA GeForce RTX 4080 Super
- Magistral Small na NVIDIA GeForce RTX 4090
- Magistral Small na NVIDIA GeForce RTX 5060
- Magistral Small na NVIDIA GeForce RTX 5060 Ti 16GB
- Magistral Small na NVIDIA GeForce RTX 5060 Ti 8GB
- Magistral Small na NVIDIA GeForce RTX 5070
- Magistral Small na NVIDIA GeForce RTX 5070 Ti
- Magistral Small na NVIDIA GeForce RTX 5080
- Magistral Small na NVIDIA GeForce RTX 5090
Magistral Small — najczęściej zadawane pytania
How much VRAM does Magistral Small need?
Magistral Small needs about 15 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Magistral Small 24B (15 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run Magistral Small on an RTX 4090 (24 GB)?
Yes — Magistral Small runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.
What quantization should I use for Magistral Small?
Q4_K_M is the best balance of quality and VRAM for Magistral Small in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.
How do I run Magistral Small with Ollama?
Install Ollama, then run: ollama run magistral:24b. This downloads Magistral Small and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.