What GPU do I need to run CohereLabs/c4ai-command-r-v01?

35.0B parameters, published in F16. View on Hugging FaceGated

35.0B
Parameters
F16
Native precision
CohereForCausalLM
Architecture
text-generation
Pipeline

c4ai-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 (runpod)
1
$1.19/hr
cheaper alt.
RTX 8000 (akash)
2
$0.441/hr
FP8 (quantized)
32.6 GB
39.1 GB
L40 (massecompute)
1
$0.772/hr
cheaper alt.
RTX 5060 Ti (simplepod)
3
$0.300/hr
INT4 (quantized)
16.3 GB
19.5 GB
RTX 3090 (simplepod)
1
$0.160/hr
cheaper alt.
RTX 3080 (simplepod)
2
$0.140/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 caveat: 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 on runpod, at $1.19/hr per GPU ($1.19/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.

Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.

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