Family Command · 35B parameters

Command R 35B v01

Original Command R optimized for RAG. 10 languages. ⚠ CC-BY-NC license (non-commercial).

Cohere·License CC-BY-NC 4.0·Context 125k tokens·Output March 2024·Fits within the 128 GB of the GIGABYTE AI TOP ATOM← Catalog

01What it can do

Strengths
  • First open RAG/tool-use
  • 128k ctx
  • 10 languages
Limitations to know
  • —⚠ CC-BY-NC license (non-commercial)
  • —Superseded by Command R+ 104B
Architecture
Dense 35B · optimized for RAG and tool use · GQA
Training
10 languages evaluated, 23 trained.
Ideal for
RAGTool use10 languages

04Install

Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.

$ollama run command-r:35b
⚠
First download: between 2 and 40 GB depending on the selected quantization. Plan for sufficient disk space; a stable connection is recommended. Subsequent launches are instant.

02Required memory

Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.

Q4_K_M
The lightest, ~5% loss
20 GB
Q5_K_M
Good quality/size compromise
25 GB
Q8_0
Nearly indistinguishable from FP16
37 GB
FP16
Full precision — server use
70 GB
Fallback CPU · If you don't have a GPU, allow 32 GB of RAM minimum to run this model at reduced speed.

What hardware do you need for Command R 35B v01?

To run Command R 35B v01 locally with Q4 quantization, you need about 20 GB of VRAM. An option to compare: GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) — leave some headroom for the system and context; check engine compatibility with the GPU.

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On the go: Command R 35B v01 also runs on a RTX laptop PC (24 GB of VRAM) →

This model in your private ChatGPT, without the cloud

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03Expected speed

Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.

Entry-level
~3t/s
GTX 1650, RX 6600, MBA M2 8GB
Mid-range
~12t/s
RTX 4060, 4070, MBP M3 Pro
High-end
~28t/s
RTX 4090, M4 Max, Radeon 7900