What GPU do I need to run Qwen/Qwen3.5-35B-A3B-Base?

36.0B parameters, published in BF16. View on Hugging Face

36.0B
Parameters
BF16
Native precision
Qwen3_5MoeForConditionalGeneration
Architecture
image-text-to-text
Pipeline

Qwen3.5-35B-A3B-Base is published by Qwen on Hugging Face, with 97,728 downloads and 144 likes to date. It's a Qwen3_5MoeForConditionalGeneration 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
67.0 GB
80.4 GB
RTX PRO 6000 (runpod)
1
$1.69/hr
cheaper alt.
RTX 3060 (simplepod)
7
$0.560/hr
FP8 (quantized)
33.5 GB
40.2 GB
L40 (massecompute)
1
$0.772/hr
cheaper alt.
RTX 5060 Ti (simplepod)
3
$0.300/hr
INT4 (quantized)
16.7 GB
20.1 GB
RTX 3090 (simplepod)
1
$0.160/hr
cheaper alt.
RTX 3070 (simplepod)
3
$0.150/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 Qwen3.5-35B-A3B-Base at its published (BF16) precision: 1× RTX PRO 6000 on runpod, at $1.69/hr per GPU ($1.69/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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