What GPU do I need to run Qwen/Qwen3-30B-A3B-Base?

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

30.5B
Parameters
BF16
Native precision
Qwen3MoeForCausalLM
Architecture
text-generation
Pipeline

Qwen3-30B-A3B-Base is published by Qwen on Hugging Face, with 133,672 downloads and 77 likes to date. It's a Qwen3MoeForCausalLM 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
56.9 GB
68.2 GB
A100 (vastai)
1
$1.15/hr
cheaper alt.
RTX 3060 (simplepod)
6
$0.420/hr
FP8 (quantized)
28.4 GB
34.1 GB
RTX 6000 Ada (vastai)
1
$0.640/hr
cheaper alt.
RTX 4070 Super (simplepod)
3
$0.270/hr
INT4 (quantized)
14.2 GB
17.1 GB
RTX 6000 (akash)
1
$0.116/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 Qwen3-30B-A3B-Base 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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