Command R
Cohere's Command R. Strong RAG and tool use; 10+ language support. Best when you need grounded generation.
Best for RAG with strong tool use
Sizes 35B
Context 128K
License CC-BY-NC (research) / Cohere License (commercial)
Min VRAM (default size, Q4) 22 GB
Rec VRAM 24 GB
What is Command R?
Command R is Cohere's open-weights language model. Released in 2024-03, it ships in 1 size (35B) and is licensed CC-BY-NC (research) / Cohere License (commercial). The most popular use case is RAG with strong tool use.
VRAM and hardware
The 35B size fits in 22 GB of VRAM.
| Size | Min VRAM (Q4_K_M) | Recommended VRAM | Tokens/sec on 3090 |
|---|---|---|---|
| 35B | 22 GB | 33 GB | ~17 tok/s |
How to run Command R locally
Option 1: Ollama (simplest)
ollama pull command-r
ollama run command-r Option 2: Mullama (production)
mullama pull command-r
mullama run command-r Option 3: llama.cpp (CLI)
# Download a GGUF from Hugging Face (search "command-r gguf")
./llama-cli -m command-r.Q4_K_M.gguf -p "Hello, AI!" Option 4: Python with Mullama or llama-cpp-python
from mullama import Model, Context
model = Model.load("command-r.Q4_K_M.gguf", n_gpu_layers=99)
ctx = Context(model, n_ctx=4096)
print(ctx.generate("Hello, AI!", 256)) What you can build with Command R
- RAG
- tools
- multilingual
Hardware it fits on
- Apple Silicon (8GB): 35B in MLX
- Apple Silicon (16GB): 35B in MLX
- Apple Silicon (32GB): 35B in MLX or GGUF
- Apple Silicon (64GB+): 35B in MLX
- RTX 3090 (24GB): 35B at Q4_K_M
- RTX 4090 (24GB): same as 3090, ~30% faster
- RTX 5090 (32GB): 35B at Q8_0
- 2× RTX 6000 Ada (96GB total): 35B at Q4_K_M
Related models in the Command family
This is the only Command-family model in the catalog right now.
Other models you might consider
- Llama 3.1 (Meta) — 8B, general-purpose chat, agentic workflows, code
- Llama 3.2 (Meta) — 1B, low-end hardware, mobile, edge deployment
- Llama 3.2 Vision (Meta) — 11B, vision + chat on desktop GPUs
- Llama 3.3 (Meta) — 70B, best-in-class 70B for desktops
- Llama 4 (Meta) — Scout 17B (109B MoE), vision + long context (10M tokens)
- Qwen 2.5 (Alibaba) — 0.5B, strongest 7B-32B on consumer hardware