What GPU do I need to run Qwen/Qwen3.5-122B-A10B?
A 125B (MoE) language model for chat and instruction-following. 125.1B parameters, published in BF16. View on Hugging Face
Qwen3.5-122B-A10B is published by Qwen on Hugging Face, with 863,674 downloads and 612 likes to date. It's a Qwen3_5MoeForConditionalGeneration model built for image-text-to-text, published natively in BF16.
What Qwen3.5-122B-A10B is
Qwen3.5-122B-A10B is a 125B-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 Qwen3.5-122B-A10B'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) |
|---|---|---|---|---|---|
| BF16 | 233.0 GB | 279.6 GB | RTX A6000 | 6 | $2.18/hr |
| FP8 (quantized) | 116.5 GB | 139.8 GB | RTX 4090 | 3 | $1.32/hr |
| INT4 (quantized) | 58.2 GB | 69.9 GB | A100 | 1 | $1.31/hr |
| cheaper alt. | RTX A5000 | 3 | $0.528/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 Qwen3.5-122B-A10B at its published (BF16) precision: 6× RTX A6000, at $0.363/hr per GPU ($2.18/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Qwen3.5-122B-A10B: common questions
Can Qwen3.5-122B-A10B run on a single GPU?
No. At BF16 it needs 279.6 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 48.0 GB RTX A6000, and it takes 6 of them.
How many GPUs do I need to run Qwen3.5-122B-A10B?
6 at BF16. It needs 279.6 GB of VRAM and the cheapest capable live offer is a 48.0 GB RTX A6000, so 6 of them come to $2.18/hr in total.
Does quantizing Qwen3.5-122B-A10B lower the GPU bill?
Yes. At BF16 the cheapest live fit is 6 RTX A6000 cards at $2.18/hr. At INT4 (quantized) it drops to one A100 at $1.31/hr, provided a quantized checkpoint exists for it.
How to run Qwen3.5-122B-A10B
Run Qwen3.5-122B-A10B with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve Qwen/Qwen3.5-122B-A10B --tensor-parallel-size 6Run Qwen3.5-122B-A10B with Ollama
Verified against Ollama's own library listing.
ollama run qwen3.5:122bRun Qwen3.5-122B-A10B with GGUF quantizations
Prebuilt GGUF weights published at unsloth/Qwen3.5-122B-A10B-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/Qwen3.5-122B-A10B-GGUFSource: https://huggingface.co/unsloth/Qwen3.5-122B-A10B-GGUF
Deploy Qwen3.5-122B-A10B on Aquanode
Aquanode has no one-click deploy template for Qwen3.5-122B-A10B; 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 A6000 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 Qwen3.5 models
- Qwen3.5-122B-A10B-FP8 (125.1B, F8_E4M3)
- Qwen3.5-397B-A17B (403.4B, BF16)
- Qwen3.5-397B-A17B-FP8 (403.4B, F8_E4M3)
- Qwen3.5-35B-A3B-FP8 (36.0B, F8_E4M3)
- Qwen3.5-35B-A3B (36.0B, BF16)
Related reading: RTX A6000 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.
