What GPU do I need to run Qwen/Qwen3-Next-80B-A3B-Thinking?
A 81.3B (MoE) model tuned to reason step by step before answering. 81.3B parameters, published in BF16. View on Hugging Face
Qwen3-Next-80B-A3B-Thinking is published by Qwen on Hugging Face, with 43,899 downloads and 495 likes to date. It's a Qwen3NextForCausalLM model built for text-generation, published natively in BF16.
What Qwen3-Next-80B-A3B-Thinking is
Qwen3-Next-80B-A3B-Thinking is a 81.3B-parameter mixture-of-experts language model published by Alibaba (Qwen) on Hugging Face. It works through a problem step by step before answering, rather than responding directly. 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-Next-80B-A3B-Thinking's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Multi-step reasoning and math
- Code generation
- Agentic tool use
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) |
|---|---|---|---|---|---|
| BF16 | 151.5 GB | 181.8 GB | RTX A5000 | 8 | $1.41/hr |
| FP8 (quantized) | 75.7 GB | 90.9 GB | RTX PRO 6000 | 1 | $1.38/hr |
| cheaper alt. | RTX 5060 Ti | 6 | $0.660/hr | ||
| INT4 (quantized) | 37.9 GB | 45.4 GB | RTX A6000 | 1 | $0.363/hr |
| cheaper alt. | RTX 5060 Ti | 3 | $0.330/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-Next-80B-A3B-Thinking at its published (BF16) precision: 8× RTX A5000, at $0.176/hr per GPU ($1.41/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Qwen3-Next-80B-A3B-Thinking: common questions
Can Qwen3-Next-80B-A3B-Thinking run on a single GPU?
No. At BF16 it needs 181.8 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 24.0 GB RTX A5000, and it takes 8 of them.
How many GPUs do I need to run Qwen3-Next-80B-A3B-Thinking?
8 at BF16. It needs 181.8 GB of VRAM and the cheapest capable live offer is a 24.0 GB RTX A5000, so 8 of them come to $1.41/hr in total.
Does quantizing Qwen3-Next-80B-A3B-Thinking lower the GPU bill?
Yes. At BF16 the cheapest live fit is 8 RTX A5000 cards at $1.41/hr. At INT4 (quantized) it drops to one RTX A6000 at $0.363/hr, provided a quantized checkpoint exists for it.
How to run Qwen3-Next-80B-A3B-Thinking
Run Qwen3-Next-80B-A3B-Thinking with vLLM
From Qwen/Qwen3-Next-80B-A3B-Thinking's own deployment docs.
vllm serve Qwen/Qwen3-Next-80B-A3B-Thinking --port 8000 --tensor-parallel-size 4 --max-model-len 262144 --reasoning-parser deepseek_r1Source: https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking/raw/main/README.md
Run Qwen3-Next-80B-A3B-Thinking with Ollama
Verified against Ollama's own library listing.
ollama run qwen3-next:80bRun Qwen3-Next-80B-A3B-Thinking with GGUF quantizations
Prebuilt GGUF weights published at skymizer/Qwen3-Next-80B-A3B-Thinking-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf skymizer/Qwen3-Next-80B-A3B-Thinking-GGUFSource: https://huggingface.co/skymizer/Qwen3-Next-80B-A3B-Thinking-GGUF
Deploy Qwen3-Next-80B-A3B-Thinking on Aquanode
Aquanode has no one-click deploy template for Qwen3-Next-80B-A3B-Thinking; 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 (8× RTX A5000 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-Next-80B-A3B-Instruct (81.3B, BF16)
- Qwen3-Next-80B-A3B-Instruct-FP8 (81.3B, F8_E4M3)
- Qwen3-Coder-Next-FP8-dynamic (79.8B, F8_E4M3)
- Qwen3-Coder-Next-FP8 (79.7B, F8_E4M3)
- Qwen3-Coder-Next-FP8 (79.7B, F8_E4M3)
Related reading: RTX A5000 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.
