What GPU do I need to run CohereLabs/North-Mini-Code-1.0?

30.5B parameters, published in BF16. View on Hugging Face

30.5B
Parameters
BF16
Native precision
Cohere2MoeForCausalLM
Architecture
text-generation
Pipeline

North-Mini-Code-1.0 is published by CohereLabs on Hugging Face, with 20,274 downloads and 564 likes to date. It's a Cohere2MoeForCausalLM model built for text-generation, published natively in BF16.

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)
BF16
56.8 GB
68.1 GB
A100 (runpod)
1
$1.19/hr
cheaper alt.
RTX 4070 (simplepod)
6
$0.480/hr
FP8 (quantized)
28.4 GB
34.1 GB
L40 (massecompute)
1
$0.772/hr
cheaper alt.
RTX 4070 (simplepod)
3
$0.240/hr
INT4 (quantized)
14.2 GB
17.0 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 North-Mini-Code-1.0 at its published (BF16) 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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