What GPU do I need to run Qwen/Qwen3-235B-A22B-Instruct-2507-FP8?
A 235B (MoE) language model for chat and instruction-following. 235.1B parameters, published in F8_E4M3. View on Hugging Face
Qwen3-235B-A22B-Instruct-2507-FP8 is published by Qwen on Hugging Face, with 176,542 downloads and 149 likes to date. It's a Qwen3MoeForCausalLM model built for text-generation, published natively in F8_E4M3.
What Qwen3-235B-A22B-Instruct-2507-FP8 is
Qwen3-235B-A22B-Instruct-2507-FP8 is a 235B-parameter mixture-of-experts language model published by Alibaba (Qwen) on Hugging Face. It is released under Apache 2.0.
License note: fully permissive, no gating and no usage restrictions. Facts in this section are sourced from Qwen3-235B-A22B-Instruct-2507-FP8's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Chat assistants
- Instruction following
- Synthetic data generation
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 219.0 GB | 262.8 GB | RTX PRO 6000 | 3 | $4.13/hr |
| INT4 (quantized) | 109.5 GB | 131.4 GB | RTX A5000 | 6 | $1.06/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run Qwen3-235B-A22B-Instruct-2507-FP8 at its published (F8_E4M3) precision: 3× RTX PRO 6000, at $1.38/hr per GPU ($4.13/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Qwen3-235B-A22B-Instruct-2507-FP8: common questions
Can Qwen3-235B-A22B-Instruct-2507-FP8 run on a single GPU?
No. At FP8 (native) it needs 262.8 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 95.0 GB RTX PRO 6000, and it takes 3 of them.
Is Qwen3-235B-A22B-Instruct-2507-FP8 already quantized?
Yes. It is published in FP8, one byte per parameter, so the 262.8 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 131.4 GB. A GPU without FP8 tensor cores cannot run it as published, which is why cards here are matched on precision support and not on VRAM alone.
How many GPUs do I need to run Qwen3-235B-A22B-Instruct-2507-FP8?
3 at FP8 (native). It needs 262.8 GB of VRAM and the cheapest capable live offer is a 95.0 GB RTX PRO 6000, so 3 of them come to $4.13/hr in total.
Does quantizing Qwen3-235B-A22B-Instruct-2507-FP8 lower the GPU bill?
Yes. At FP8 (native) the cheapest live fit is 3 RTX PRO 6000 cards at $4.13/hr. At INT4 (quantized) it drops to 6 RTX A5000 cards at $1.06/hr, provided a quantized checkpoint exists for it.
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 (3× RTX PRO 6000 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 podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Qwen3 models
- Qwen3-235B-A22B-Thinking-2507-FP8 (235.1B, F8_E4M3)
- Qwen3-235B-A22B-FP8 (235.1B, F8_E4M3)
- Qwen3-235B-A22B (235.1B, BF16)
- Qwen3-235B-A22B-Instruct-2507 (235.1B, BF16)
- Qwen3-235B-A22B-Thinking-2507 (235.1B, BF16)
Related reading: RTX PRO 6000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
