LLM
How to deploy Qwen3-235B-A22B-Instruct-2507-FP8 on a GPU cloud
A 235B (MoE) language model for chat and instruction-following. Full specs, license and use cases.
Qwen3-235B-A22B-Instruct-2507-FP8 size and hardware requirements
235.1B
Total parameters
~22B active per token (mixture-of-experts; see total parameters above)
Active parameters
F8_E4M3
Published precision
262.8 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 219.0 GB | 262.8 GB | RTX 6000 Ada | 6 | $3.14/hr |
| INT4 (quantized) | 109.5 GB | 131.4 GB | RTX 3090 | 6 | $0.882/hr |
How to run Qwen3-235B-A22B-Instruct-2507-FP8
Run Qwen3-235B-A22B-Instruct-2507-FP8 with vLLM
From Qwen/Qwen3-235B-A22B-Instruct-2507-FP8's own deployment docs.
vllm serve Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 --tensor-parallel-size 4 --max-model-len 262144Source: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507-FP8/raw/main/README.md
Deploy Qwen3-235B-A22B-Instruct-2507-FP8 on Aquanode
Aquanode has no one-click deploy template for Qwen3-235B-A22B-Instruct-2507-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 (6× RTX 6000 Ada 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