Arrakis / Models / Qwen3-Coder-30B-A3B-Instruct

Qwen3-Coder-30B-A3B-Instruct GPU requirements

Qwen3-Coder-30B-A3B-Instruct has 31B parameters. To serve it, plan for about 73 GB of GPU memory in 16-bit, 37 GB in 8-bit or 18 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 31B 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	73 GB: one H100 or A100, H100 from $2.07 per GPU-hour
8-bit	37 GB: one L40S, RTX 6000 Ada or RTX A6000, L40S from $0.44 per GPU-hour
4-bit	18 GB: one RTX 4090 or RTX 3090, RTX 3090 from $0.19 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	21 GB: one RTX 4090 or RTX 3090, RTX 3090 from $0.19 per GPU-hour
LoRA	73 GB: one H100 or A100, H100 from $2.07 per GPU-hour
Full fine-tuning	489 GB: 2 × B300, from $9.20 per hour for all 2

About the model

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

Parameters: 31B
Active for each token: 3B
Experts: 128
Experts for each token: 8
Context window (tokens): 262,144
Published in: BF16
License: apache-2.0
Released: 2025-07-31
Weights: Qwen/Qwen3-Coder-30B-A3B-Instruct

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-Coder-30B-A3B-Instruct --tensor-parallel-size 1

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Questions

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

How much GPU memory does Qwen3-Coder-30B-A3B-Instruct need?

To serve it, about 73 GB in 16-bit, 37 GB in 8-bit, 18 GB in 4-bit, including about 20% for the KV cache and activations. To fine-tune it, about 21 GB with QLoRA, 73 GB with LoRA and 489 GB for full fine-tuning with Adam, before activations.

Can I run Qwen3-Coder-30B-A3B-Instruct on one GPU?

Yes. In 16-bit it needs about 73 GB, which fits on one H100 or A100.

What does it cost to run Qwen3-Coder-30B-A3B-Instruct?

In 16-bit it needs one H100 or A100; in 4-bit, one RTX 4090 or RTX 3090. 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-Coder-30B-A3B-Instruct on Arrakis?

Open Models in the Arrakis console, paste Qwen/Qwen3-Coder-30B-A3B-Instruct, 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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