LLM
How to deploy Qwen3-8B on a GPU cloud
A 8.2B language model for chat and instruction-following. Full specs, license and use cases.
Qwen3-8B size and hardware requirements
8.2B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
18.3 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 15.3 GB | 18.3 GB | RTX 3090 | 1 | $0.147/hr |
| FP8 (quantized) | 7.6 GB | 9.2 GB | RTX 4070 Super | 1 | $0.110/hr |
| INT4 (quantized) | 3.8 GB | 4.6 GB | RTX 4070 Super | 1 | $0.110/hr |
How to run Qwen3-8B
Run Qwen3-8B with vLLM
From Qwen/Qwen3-8B's own deployment docs.
vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1Source: https://huggingface.co/Qwen/Qwen3-8B/raw/main/README.md
Run Qwen3-8B with Ollama
Verified against Ollama's own library listing.
ollama run qwen3:8bRun Qwen3-8B with GGUF quantizations
Prebuilt GGUF weights published at unsloth/Qwen3-8B-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/Qwen3-8B-GGUFDeploy Qwen3-8B on Aquanode
Aquanode has no one-click deploy template for Qwen3-8B; 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