LLM
How to deploy Qwen3-32B on a GPU cloud
A 32.8B language model for chat and instruction-following. Full specs, license and use cases.
Qwen3-32B size and hardware requirements
32.8B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
73.2 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 61.0 GB | 73.2 GB | A100 | 1 | $0.851/hr |
| FP8 (quantized) | 30.5 GB | 36.6 GB | RTX 6000 Ada | 1 | $0.524/hr |
| INT4 (quantized) | 15.3 GB | 18.3 GB | RTX 3090 | 1 | $0.147/hr |
How to run Qwen3-32B
Run Qwen3-32B with vLLM
From Qwen/Qwen3-32B's own deployment docs.
vllm serve Qwen/Qwen3-32B --enable-reasoning --reasoning-parser deepseek_r1Source: https://huggingface.co/Qwen/Qwen3-32B/raw/main/README.md
Run Qwen3-32B with Ollama
Verified against Ollama's own library listing.
ollama run qwen3:32bDeploy Qwen3-32B on Aquanode
Aquanode has no one-click deploy template for Qwen3-32B; 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× A100 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