LLM
How to deploy Qwen3.5-397B-A17B on a GPU cloud
A 403B (MoE) language model for chat and instruction-following. Full specs, license and use cases.
Qwen3.5-397B-A17B size and hardware requirements
403.4B
Total parameters
~17B active per token (mixture-of-experts; see total parameters above)
Active parameters
BF16
Published precision
901.7 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 751.4 GB | 901.7 GB | No capable live offer found | – | – |
| FP8 (quantized) | 375.7 GB | 450.8 GB | RTX PRO 6000 | 5 | $8.20/hr |
| INT4 (quantized) | 187.8 GB | 225.4 GB | RTX A6000 | 5 | $1.65/hr |
How to run Qwen3.5-397B-A17B
Run Qwen3.5-397B-A17B with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve Qwen/Qwen3.5-397B-A17B --tensor-parallel-size 1Run Qwen3.5-397B-A17B with Ollama
Verified against Ollama's own library listing.
ollama run qwen3.5:397bRun Qwen3.5-397B-A17B with GGUF quantizations
Prebuilt GGUF weights published at unsloth/Qwen3.5-397B-A17B-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/Qwen3.5-397B-A17B-GGUFSource: https://huggingface.co/unsloth/Qwen3.5-397B-A17B-GGUF
Deploy Qwen3.5-397B-A17B on Aquanode
Aquanode has no one-click deploy template for Qwen3.5-397B-A17B; 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 (902 GB VRAM or more).
- 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