What GPU do I need to run Qwen/Qwen3.5-9B?
A 9.7B language model for chat and instruction-following. 9.7B parameters, published in BF16. View on Hugging Face
Qwen3.5-9B is published by Qwen on Hugging Face, with 12,621,674 downloads and 1,883 likes to date. It's a Qwen3_5ForConditionalGeneration model built for image-text-to-text, published natively in BF16.
What Qwen3.5-9B is
Qwen3.5-9B is a 9.7B-parameter language model published by Alibaba (Qwen) on Hugging Face. It is released under Apache 2.0.
License note: fully permissive, no gating and no usage restrictions. Facts in this section are sourced from Qwen3.5-9B's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Chat assistants
- Instruction following
- Synthetic data generation
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 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 4.5 GB | 5.4 GB | RTX 5060 Ti | 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 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: common questions
Does Qwen3.5-9B 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 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 lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX A5000 at $0.176/hr. At FP8 (quantized) it drops to one RTX 5060 Ti at $0.110/hr, provided a quantized checkpoint exists for it.
How to run Qwen3.5-9B
Run Qwen3.5-9B with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve Qwen/Qwen3.5-9B --tensor-parallel-size 1Run Qwen3.5-9B with Ollama
Verified against Ollama's own library listing.
ollama run qwen3.5:9bRun Qwen3.5-9B with GGUF quantizations
Prebuilt GGUF weights published at unsloth/Qwen3.5-9B-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/Qwen3.5-9B-GGUFDeploy Qwen3.5-9B on Aquanode
Aquanode has no one-click deploy template for Qwen3.5-9B; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× RTX A5000 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Qwen3.5 models
- Qwen3.5-9B-AWQ (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.
