LLM
How to deploy Qwen3.5-9B on a GPU cloud
A 9.7B language model for chat and instruction-following. Full specs, license and use cases.
Qwen3.5-9B size and hardware requirements
9.7B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
21.6 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 18.0 GB | 21.6 GB | RTX 3090 | 1 | $0.147/hr |
| FP8 (quantized) | 9.0 GB | 10.8 GB | RTX 4070 Super | 1 | $0.110/hr |
| INT4 (quantized) | 4.5 GB | 5.4 GB | RTX 4070 Super | 1 | $0.110/hr |
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 3090 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 pod