What GPU do I need to run QuantTrio/Qwen3.5-9B-AWQ?
9.7B parameters, published in BF16. View on Hugging Face
Set up Qwen3.5-9B-AWQQwen3.5-9B-AWQ is published by QuantTrio on Hugging Face, with 1,219,015 downloads and 26 likes to date. It's a Qwen3_5ForConditionalGeneration 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 | 18.0 GB | 21.6 GB | RTX A5000 | 1 | $0.176/hr |
| FP8 (quantized) | 9.0 GB | 10.8 GB | RTX 4070 Super | 1 | $0.121/hr |
| INT4 (quantized) | 4.5 GB | 5.4 GB | RTX 3060 | 1 | $0.110/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 Qwen3.5-9B-AWQ at its published (BF16) precision: 1× RTX A5000, at $0.176/hr per GPU ($0.176/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Qwen3.5-9B-AWQ: common questions
Does Qwen3.5-9B-AWQ fit on a 24 GB GPU?
Yes. At BF16 it needs 21.6 GB of VRAM, so a 24 GB card holds it with 2.4 GB to spare. A 16 GB card is not enough for it at BF16.
What is the least VRAM Qwen3.5-9B-AWQ can run in?
5.4 GB, at INT4 (quantized), which fits a 6 GB card, against 21.6 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 Qwen3.5-9B-AWQ lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX A5000 at $0.176/hr. At INT4 (quantized) it drops to one RTX 3060 at $0.110/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Qwen3.5 models
- Qwen3.5-9B (9.7B, BF16)
- Qwen3.5-9B-Base (9.7B, BF16)
- Qwen3.5-9B (9.7B, BF16)
- Qwen3.8-9B-Distill (9.7B, BF16)
- Qwen3.5-9B-FP8-dynamic (9.4B, F8_E4M3)
Related reading: RTX A5000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
