What GPU do I need to run allenai/OLMoE-1B-7B-0125-Instruct?

6.9B parameters, published in BF16. View on Hugging Face

6.9B
Parameters
BF16
Native precision
OlmoeForCausalLM
Architecture
text-generation
Pipeline

OLMoE-1B-7B-0125-Instruct is published by allenai on Hugging Face, with 286,999 downloads and 68 likes to date. It's a OlmoeForCausalLM 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
12.9 GB
15.5 GB
RTX 5060 Ti (simplepod)
1
$0.100/hr
cheaper alt.
RTX 3070 (simplepod)
2
$0.100/hr
FP8 (quantized)
6.4 GB
7.7 GB
RTX 4070 Super (simplepod)
1
$0.090/hr
INT4 (quantized)
3.2 GB
3.9 GB
RTX 3070 (simplepod)
1
$0.050/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 OLMoE-1B-7B-0125-Instruct at its published (BF16) precision: 1× RTX 5060 Ti on simplepod, at $0.100/hr per GPU ($0.100/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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