Arrakis / Models / Qwen3-235B-A22B

Qwen3-235B-A22B GPU requirements

Qwen3-235B-A22B has 235B parameters. To serve it, plan for about 564 GB of GPU memory in 16-bit, 282 GB in 8-bit or 141 GB in 4-bit, including about 20% for the KV cache and activations.

It is a mixture of experts: 128 experts, 8 of them active for each token. All of them stay in GPU memory, so plan memory for all 235B parameters.

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What it takes to serve it

GPU memory to run it for inference, with the smallest GPU setup that holds it and the lowest price on Arrakis right now.

Precision	Memory and the smallest setup
16-bit	564 GB: 8 × H100, from $16.56 per hour for all 8
8-bit	282 GB: 4 × H100, from $8.28 per hour for all 4
4-bit	141 GB: one B200, from $6.33 per GPU-hour

What it takes to fine-tune it

QLoRA trains adapters on a 4-bit frozen model, LoRA on a 16-bit one, and full fine-tuning updates every weight. Figures are before activations, which grow with batch size and sequence length.

Method	Memory and the smallest setup
QLoRA	165 GB: one B200, from $6.33 per GPU-hour
LoRA	564 GB: 8 × H100, from $16.56 per hour for all 8
Full fine-tuning	3,761 GB: more than one 8-GPU machine

About the model

From its card and files on Hugging Face. Memory figures here follow the GPU memory guide.

Parameters: 235B
Active for each token: 22B
Experts: 128
Experts for each token: 8
Context window (tokens): 40,960
Published in: BF16
License: apache-2.0
Released: 2025-04-27
Weights: Qwen/Qwen3-235B-A22B

Deploy it on Arrakis

Open Models in the console, choose vLLM, SGLang or llama.cpp, and rent a GPU it fits on. You get an OpenAI-compatible endpoint with its own API key, and root SSH to the machine. Or run it yourself on any machine with enough GPU memory:

vLLM

vllm serve Qwen/Qwen3-235B-A22B --tensor-parallel-size 8

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Questions

Live prices for every GPU are on the GPU prices page.

How much GPU memory does Qwen3-235B-A22B need?

To serve it, about 564 GB in 16-bit, 282 GB in 8-bit, 141 GB in 4-bit, including about 20% for the KV cache and activations. To fine-tune it, about 165 GB with QLoRA, 564 GB with LoRA and 3,761 GB for full fine-tuning with Adam, before activations.

Can I run Qwen3-235B-A22B on one GPU?

Yes, quantized. In 4-bit it needs about 141 GB, which fits on one B200. In 16-bit it needs 8 H100 GPUs.

What does it cost to run Qwen3-235B-A22B?

In 16-bit it needs 8 H100 GPUs; in 4-bit, one B200. Each setup on this page shows the lowest price per GPU-hour on Arrakis right now, and you pay by the hour from a prepaid balance.

How do I deploy Qwen3-235B-A22B on Arrakis?

Open Models in the Arrakis console, paste Qwen/Qwen3-235B-A22B, choose vLLM, SGLang or llama.cpp, and rent a GPU it fits on. You get an OpenAI-compatible endpoint with its own API key, and root SSH to the machine.

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