What GPU do I need to run CohereLabs/c4ai-command-r-v01?
35.0B parameters, published in F16. View on Hugging FaceGated
Set up c4ai-command-r-v01c4ai-command-r-v01 is published by CohereLabs on Hugging Face, with 33,570 downloads and 1,116 likes to date. It's a CohereForCausalLM model built for text-generation, published natively in F16, and gated: you'll need to accept the model's terms on Hugging Face before downloading weights.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP16 | 65.2 GB | 78.2 GB | A100 | 1 | $1.31/hr |
| cheaper alt. | V100 | 5 | $0.440/hr | ||
| FP8 (quantized) | 32.6 GB | 39.1 GB | RTX 4090 | 1 | $0.441/hr |
| cheaper alt. | RTX 5060 Ti | 3 | $0.330/hr | ||
| INT4 (quantized) | 16.3 GB | 19.5 GB | RTX A5000 | 1 | $0.176/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run c4ai-command-r-v01 at its published (F16) precision: 1× A100, at $1.31/hr per GPU ($1.31/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
c4ai-command-r-v01: common questions
Can c4ai-command-r-v01 run on a single GPU?
Yes, but not on a desktop card. At FP16 it needs 78.2 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 80.0 GB A100 at $1.31/hr.
Do I need approval to download c4ai-command-r-v01?
Yes. CohereLabs gates this repository on Hugging Face, so you have to accept its terms with a signed-in account before the weights will download. It does not change the 78.2 GB the model needs once you have them.
What is the least VRAM c4ai-command-r-v01 can run in?
19.5 GB, at INT4 (quantized), which fits a 24 GB card, against 78.2 GB at FP16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing c4ai-command-r-v01 lower the GPU bill?
Yes. At FP16 the cheapest live fit is one A100 at $1.31/hr. At INT4 (quantized) it drops to one RTX A5000 at $0.176/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Command R models
- c4ai-command-r7b-12-2024 (8.0B, BF16)
Related reading: A100 pricing and specs, and The best GPUs for AI, ranked.
