Qwen

Qwen3.8 27B

27B-parameter dense multimodal model, one of the most downloaded open-weight models in this class, with native 262,144-token context.image text to textapache-2.07.5M downloadsHugging Face

Cheapest GPU in stock that fits

A single GPU with at least 67 GB for inference.

NosanaIn stock · 21 freeStart right away

1× RTX PRO 600096 GB VRAM

Nosana network, hosts worldwide

Details

Stock and price checked 17 min ago.

Self-service: you start it yourself, with no order or sales contact.

Confidential computing: data stays encrypted while the GPU works on it, so the host can't read it.

nvidia-pro-6000-cc

Full price

€2.09

per hour, whole instance, 1 GPU

View offer

GPU memory it needs

From model cards and vendor documentation. Lower precision needs less memory, with slightly lower quality. Estimates are marked.

What you want to do	Precision	Minimum	Setup	Basis
Run the model vLLM recipe minimum, 51.7 GiB of weights plus runtime overhead.	BF16, full	67 GB	1 GPU, per vLLM recipe	Published
Run the model vLLM recipe minimum for separate repo Qwen/Qwen3.8-27B-FP8 (28.7 GiB).	FP8, 8-bit	38 GB	1 GPU, per vLLM recipe	Published
Run the model vLLM recipe minimum for separate repo RedHatAI/Qwen3.8-27B-INT4 (19.5 GB, W4A16).	4-bit	24 GB	1 GPU, Hopper or newer, per vLLM recipe	Published
LoRA fine-tuning Arithmetic from BF16 weight size (51.74 GiB = 27.78B parameters x 2 bytes): weights x 1.3, our standard estimate. Excludes activations beyond the overhead factor; batch size not assumed.	BF16, full	67.3 GB	—	Estimated
Full fine-tuning Arithmetic from BF16 weight size (51.74 GiB = 27.78B parameters x 2 bytes): weights x 4 states x 1.2, our standard estimate. Excludes activations beyond the overhead factor; batch size not assumed.	BF16, full	248.4 GB	—	Estimated