What GPU do I need to run Qwen/Qwen3.8-27B?

27.8B parameters, published in BF16. View on Hugging Face

27.8B
Parameters
BF16
Native precision
Qwen3_5ForConditionalGeneration
Architecture
image-text-to-text
Pipeline

Qwen3.8-27B is published by Qwen on Hugging Face, with 2,090,699 downloads and 12,126 likes to date. It's a Qwen3_5ForConditionalGeneration model built for image-text-to-text, 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
51.7 GB
62.1 GB
A100 (vastai)
1
$1.15/hr
cheaper alt.
RTX 3070 (simplepod)
8
$0.400/hr
FP8 (quantized)
25.9 GB
31.0 GB
RTX 5090 (vastai)
1
$0.444/hr
cheaper alt.
RTX 5060 Ti (simplepod)
2
$0.200/hr
INT4 (quantized)
12.9 GB
15.5 GB
RTX 5060 Ti (simplepod)
1
$0.100/hr
cheaper alt.
RTX 3070 (simplepod)
2
$0.100/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 Qwen3.8-27B at its published (BF16) precision: 1× A100 on vastai, at $1.15/hr per GPU ($1.15/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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