Command R 35B v01
By Cohere · Canada
Updated 2026-07-13
Overview
Cohere's original Command R, a 35B optimized for RAG and tool use across 10 languages with 128k context — but locked under CC-BY-NC for non-commercial use only.
When to pick this model
- Research projects exploring early open RAG-native models
- Internal evaluations and prototyping with no commercial intent
- Tool-use experiments needing 128k context
- Multilingual RAG benchmarking across 10 languages
- Comparisons against successor Command R+ 104B
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 20 GB |
| Q5_K_M | 25 GB |
| Q8_0 | 37 GB |
| FP16 (no quantization) | 70 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Command R 35B v01 wants a 24 GB card at Q4_K_M (20 GB). Stepping up to Q8_0 nearly doubles the footprint to 37 GB, and unquantized FP16 weights take 70 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Command R 35B v01 needs roughly 32 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 3 tokens/sec on entry-level GPUs, on the order of 12 tokens/sec on a mid-range card, and up to 28 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Command R 35B v01 to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Command R 35B v01 |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 20 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 20 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 20 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q4_K_M (20 GB used) |
| 32 GB | RTX 5090 | Q5_K_M (25 GB used) |
Which GPU should you buy to run Command R 35B v01?
To run Command R 35B v01 locally at Q4, you need ~20 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
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Strengths
- First open model designed natively for RAG and tool use
- 128k context for long retrieval pipelines
- 10 evaluated languages, 23 in pretraining
- Strong citation and grounding behavior
Limitations
- CC-BY-NC 4.0 license blocks commercial deployment
- Superseded by Command R+ 104B for production quality
- No multimodal capabilities
Typical workloads
In our catalog grid, Command R 35B v01 is filed under RAG, Tool use, 10 Languages — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multilingual workloads.
The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. It ships under the CC-BY-NC 4.0 license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: Dense 35B · optimized for RAG and tool-use · GQA
Training: 10 languages evaluated, 23 trained.
Historically important but commercially off-limits — choose it only for research, and reach for Command R+ everywhere else.
Quick start
ollama run command-r:35bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Command R 35B v01 need?
At the recommended Q4_K_M quantization, Command R 35B v01 needs about 20 GB of VRAM. Q8_0 takes 37 GB, and unquantized FP16 weights take 70 GB.
Can Command R 35B v01 run without a GPU?
Yes — with roughly 32 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Command R 35B v01 support?
Command R 35B v01 supports a 125k-token context window (128,000 tokens).
Can I use Command R 35B v01 commercially?
Command R 35B v01 ships under the CC-BY-NC 4.0 license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Command R 35B v01 on consumer hardware?
Our compatibility engine estimates on the order of 12 tokens/sec on a mid-range GPU and up to 28 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Command R 35B v01 should I download first?
Start with Q4_K_M (20 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q4_K_M.