Command R Family — lokalny model AI od Cohere
Autor: Jakub Rusinowski · Ostatnia aktualizacja:
Zoptymalizowany specjalnie pod kątem RAG (Retrieval Augmented Generation) i użycia narzędzi. Modele Command R wyróżniają się cytowaniem źródeł i rozumowaniem nad dostarczonymi dokumentami.
Warianty
Najmniejszy wariant Command R Family potrzebuje około 22 GB VRAM przy Q4_K_M — skwantyzowane wagi plus narzut frameworka, bez pamięci podręcznej KV.
| Model | VRAM przy Q4 | VRAM | Kontekst | Uruchom |
|---|---|---|---|---|
| Command R (35B) → 35B | ~21.9 GB | 128,000 | ollama run command-r | |
| Command R+ (104B) → 104B | ~63.6 GB | 128,000 | ollama run command-r-plus |
Pamięć to skwantyzowane wagi plus narzut przy Q4_K_M, liczone tym samym silnikiem co sprawdzarka GPU i VRAM.
Jak uruchomić Command R Family lokalnie
Zainstaluj Ollama, a potem pobierz tag.
ollama run command-rWybierz rozmiar powyżej, aby zobaczyć jego VRAM, szacowaną szybkość i polecenie instalacji.
Licencja
Research / non-commercial only — this licence does NOT permit shipping a commercial product.
Dotyczy: Command R (35B), Command R+ (104B)Polecane GPU
Najtańsze GPU z naszego katalogu, które uruchomi Command R Family lokalnie (min. 22 GB VRAM), to NVIDIA GeForce RTX 5090 (32 GB).
Czy uruchomię Command R Family na moim GPU?
- Command R Family na AMD Radeon RX 7800 XT
- Command R Family na AMD Radeon RX 7900 XT
- Command R Family na AMD Radeon RX 7900 XTX
- Command R Family na AMD Radeon RX 9060 XT 16GB
- Command R Family na AMD Radeon RX 9070
- Command R Family na AMD Radeon RX 9070 XT
- Command R Family na AMD Ryzen AI Max+ 395
- Command R Family na Apple M1
- Command R Family na Apple M1 Pro
- Command R Family na Apple M1 Ultra
- Command R Family na Apple M2
- Command R Family na Apple M2 Max
- Command R Family na Apple M2 Pro
- Command R Family na Apple M3
- Command R Family na Apple M3 Pro
- Command R Family na Apple M4
- Command R Family na Apple M4 Max
- Command R Family na Apple M4 Pro
- Command R Family na Apple M5
- Command R Family na Apple M5 Max
- Command R Family na Intel Arc B570
- Command R Family na Intel Arc B580
- Command R Family na NVIDIA A100 80GB (PCIe)
- Command R Family na NVIDIA DGX Spark
- Command R Family na NVIDIA GeForce RTX 3060 (12GB)
- Command R Family na NVIDIA GeForce RTX 3080 (10GB)
- Command R Family na NVIDIA GeForce RTX 3090
- Command R Family na NVIDIA GeForce RTX 4060 Ti 16GB
- Command R Family na NVIDIA GeForce RTX 4070
- Command R Family na NVIDIA GeForce RTX 4070 Super
- Command R Family na NVIDIA GeForce RTX 4070 Ti
- Command R Family na NVIDIA GeForce RTX 4070 Ti Super
- Command R Family na NVIDIA GeForce RTX 4080
- Command R Family na NVIDIA GeForce RTX 4080 Super
- Command R Family na NVIDIA GeForce RTX 4090
- Command R Family na NVIDIA GeForce RTX 5060 Ti 16GB
- Command R Family na NVIDIA GeForce RTX 5070
- Command R Family na NVIDIA GeForce RTX 5070 Ti
- Command R Family na NVIDIA GeForce RTX 5080
- Command R Family na NVIDIA GeForce RTX 5090
- Command R Family na NVIDIA H100 80GB (PCIe)
- Command R Family na NVIDIA L40S
- Command R Family na NVIDIA RTX 6000 Ada Generation
- Command R Family na NVIDIA RTX PRO 6000 Blackwell
Command R Family — najczęściej zadawane pytania
How much VRAM does Command R Family need?
Command R Family needs about 22 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Command R (35B) (22 GB, Q4_K_M); Command R+ (104B) (64 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run Command R Family on an RTX 4090 (24 GB)?
Yes — Command R Family 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 Command R Family?
Q4_K_M is the best balance of quality and VRAM for Command R Family 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 Command R Family with Ollama?
Install Ollama, then run: ollama run command-r. This downloads Command R Family and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.