Model reference · open weights
A3-Qwen3.5 is an open-weight language model from McGill-NLP. A3-Qwen3.5-2B (BF16) weighs 5.4 GB; the smallest configuration that runs it is RTX 3060 12 GB.
Summary of the McGill-NLP/A3-Qwen3.5-9B model card, 2026-10-01. The estimate below is for another build of the family.
What it is
| Released by | McGill-NLP |
|---|---|
| Released | 2026-04-06 |
| Parameters | 9.4B |
| VRAM | 5.4 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory | |
|---|---|---|---|---|
| 8K each | 32K each | |||
| RTX 3060 12 GB | 29 | 9 | all 256K | 11.6 GB |
| RTX 4060 Ti 16 GB | 56 | 17 | all 256K | 15.4 GB |
| RTX 3090 24 GB | 113 | 35 | all 256K | 23.4 GB |
| RTX 4090 24 GB | 113 | 35 | all 256K | 23.4 GB |
| RTX 5090 32 GB | 168 | 53 | all 256K | 31.0 GB |
| L40S 48 GB | 260 | 82 | all 256K | 44.0 GB |
| A100 80 GB | 505 | 160 | all 256K | 78.2 GB |
| H100 80 GB | 468 | 148 | all 256K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 580 | 183 | all 256K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 677 | 214 | all 256K | 107 GB |
| H200 141 GB | 896 | 283 | all 256K | 138 GB |
| B200 180 GB | 1000+ | 369 | all 256K | 176 GB |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 7.6 GB | 7.9 GB |
| 5 | 8.2 GB | 9.7 GB |
| 8 | 8.6 GB | 11.0 GB |
| 16 | 9.7 GB | 14.6 GB |
| 32 | 12.0 GB | 21.6 GB |
| 64 | 16.4 GB | 35.8 GB |
One card, with vLLM's small-card settings.
Builds
| Build | Params | Precision | Weights | Smallest setup |
|---|---|---|---|---|
| A3-Qwen3.5-9B ↗ | 9.4B | BF16 | 18.8 GB | RTX 4090 24 GB |
| A3-Qwen3.5-4B ↗ | 5.2B | BF16 | 10.4 GB | RTX 4060 Ti 16 GB |
| A3-Qwen3.5-2B (above) ↗ | 2.7B | BF16 | 5.4 GB | RTX 3060 12 GB |
From the model card
| 💾 Code | 📄 Paper | 🌐 Website |
|---|---|---|
| 🤗 Dataset | 🤖 Models | 📦 PyPI |
Structured Distillation of Web Agent Capabilities Enables Generalization
Xing Han Lù, Siva Reddy
A3-Qwen3.5-9B is a 9B multimodal web agent fine-tuned from Qwen/Qwen3.5-9B on A3-Synth, a synthetic dataset generated using the Agent-as-Annotators (A3) framework.
The model achieves 41.5% on WebArena, surpassing closed-source models such as Claude 3.5 Sonnet (36.0%) and GPT-4o (31.5%) under the same evaluation protocol.
Serve with vLLM:
vllm serve McGill-NLP/A3-Qwen3.5-9B --tensor-parallel-size 2 --max-model-len 65536 --enforce-eager --dtype bfloat16
| Model | Parameters | Link |
|---|---|---|
| A3-Qwen3.5-9B | 9B | McGill-NLP/A3-Qwen3.5-9B |
| A3-Qwen3.5-4B | 4B | McGill-NLP/A3-Qwen3.5-4B |
| A3-Qwen3.5-2B | 2B | McGill-NLP/A3-Qwen3.5-2B |
@misc{lu2026structured,
title={Structured Distillation of Web Agent Capabilities Enables Generalization},
author={Xing Han Lù and Siva Reddy},
year={2026},
eprint={2604.07776},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2604.07776},
}
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.