What GPU do I need to run Qwen/Qwen2.5-Coder-32B-Instruct?

32.8B parameters, published in BF16. View on Hugging Face

Set up Qwen2.5-Coder-32B-Instruct
32.8B
Parameters
BF16
Native precision
Qwen2ForCausalLM
Architecture
text-generation
Pipeline

Qwen2.5-Coder-32B-Instruct is published by Qwen on Hugging Face, with 1,725,084 downloads and 2,117 likes to date. It's a Qwen2ForCausalLM 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
61.0 GB
73.2 GB
RTX PRO 6000 (simplepod)
1
$1.00/hr
cheaper alt.
RTX 3060 (simplepod)
7
$0.560/hr
FP8 (quantized)
30.5 GB
36.6 GB
RTX 5880 Ada (vastai)
1
$0.594/hr
cheaper alt.
2
$0.360/hr
INT4 (quantized)
15.3 GB
18.3 GB
RTX 3090 (akash)
1
$0.147/hr
cheaper alt.
RTX 3080 (simplepod)
2
$0.140/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 Qwen2.5-Coder-32B-Instruct at its published (BF16) precision: 1× RTX PRO 6000 on simplepod, at $1.00/hr per GPU ($1.00/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.

Qwen2.5-Coder-32B-Instruct: common questions

Can Qwen2.5-Coder-32B-Instruct run on a single GPU?

Yes, but not on a desktop card. At BF16 it needs 73.2 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 95.0 GB RTX PRO 6000 on simplepod at $1.00/hr.

What is the least VRAM Qwen2.5-Coder-32B-Instruct can run in?

18.3 GB, at INT4 (quantized), which fits a 24 GB card, against 73.2 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 Qwen2.5-Coder-32B-Instruct lower the GPU bill?

Yes. At BF16 the cheapest live fit is one RTX PRO 6000 on simplepod at $1.00/hr. At INT4 (quantized) it drops to one RTX 3090 on akash at $0.147/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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