LLM
How to deploy Qwen3.8-27B-FP8 on a GPU cloud
A 27.8B language model for chat and instruction-following. Full specs, license and use cases.
Qwen3.8-27B-FP8 size and hardware requirements
27.8B
Total parameters
Dense (no MoE)
Architecture
F8_E4M3
Published precision
31.0 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 25.9 GB | 31.0 GB | RTX 4080 Super | 1 | $0.383/hr |
| INT4 (quantized) | 12.9 GB | 15.5 GB | RTX A4000 | 1 | $0.113/hr |
How to run Qwen3.8-27B-FP8
Run Qwen3.8-27B-FP8 with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve Qwen/Qwen3.8-27B-FP8 --tensor-parallel-size 1Run Qwen3.8-27B-FP8 with Ollama
Verified against Ollama's own library listing.
ollama run qwen3.8:27bDeploy Qwen3.8-27B-FP8 on Aquanode
Aquanode has no one-click deploy template for Qwen3.8-27B-FP8; 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 4080 Super 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