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

8.2B parameters, published in BF16. View on Hugging Face

Set up Qwen3-Reranker-8B
8.2B
Parameters
BF16
Native precision
Qwen3ForCausalLM
Architecture
text-ranking
Pipeline

Qwen3-Reranker-8B is published by Qwen on Hugging Face, with 111,566 downloads and 261 likes to date. It's a Qwen3ForCausalLM model built for text-ranking, 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
15.3 GB
18.3 GB
RTX A5000 (runpod)
1
$0.160/hr
FP8 (quantized)
7.6 GB
9.2 GB
RTX 4070 Super (simplepod)
1
$0.100/hr
INT4 (quantized)
3.8 GB
4.6 GB
RTX 3060 (simplepod)
1
$0.080/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 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.

Cheapest way to run Qwen3-Reranker-8B at its published (BF16) precision: 1× RTX A5000 on runpod, at $0.160/hr per GPU ($0.160/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.

Qwen3-Reranker-8B: common questions

Does Qwen3-Reranker-8B fit on a 24 GB GPU?

Yes. At BF16 it needs 18.3 GB of VRAM, so a 24 GB card holds it with 5.7 GB to spare. A 16 GB card is not enough for it at BF16.

What is the least VRAM Qwen3-Reranker-8B can run in?

4.6 GB, at INT4 (quantized), which fits a 6 GB card, against 18.3 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.

Does quantizing Qwen3-Reranker-8B lower the GPU bill?

Yes. At BF16 the cheapest live fit is one RTX A5000 on runpod at $0.160/hr. At INT4 (quantized) it drops to one RTX 3060 on simplepod at $0.080/hr, provided a quantized checkpoint exists for it.

Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.

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