Model reference · open weights

A3-Qwen3.5

LLMs McGill-NLP Vision + text 3 builds Licence not stated 1k dl/mo

A3-Qwen3.5 is an open-weight language model from McGill-NLP. A3-Qwen3.5-9B (BF16) weighs 18.8 GB; the smallest configuration that runs it is RTX 4090 24 GB.

  • A3-Qwen3.5-9B is a 9.4B parameter multimodal web agent developed by McGill-NLP for image-text-to-text tasks.
  • The model is fine-tuned from Qwen3.5-9B on the A3-Synth dataset and supports English with a maximum sequence length of 16,384 tokens.
  • It achieves a score of 41.5% on the WebArena benchmark.

Summary of the McGill-NLP/A3-Qwen3.5-9B model card, 2026-10-01

What it is

Released byMcGill-NLP
Released2026-04-06
Parameters9.4B
VRAM18.8 GB for the weights

What it runs on

Memory and cards for A3-Qwen3.5-9B (BF16)

18.8 GBweights, file size
33 MBcache per 1K tokens
103 MBfixed state per request
2.1 GBruntime overhead, at least
262,144 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB … RTX 4060 Ti 16 GB
2 smaller cards
———
RTX 3090 24 GB6269K23.4 GB
RTX 4090 24 GB6268K23.4 GB
RTX 5090 32 GB278all 256K31.0 GB
L40S 48 GB6119all 256K44.0 GB
A100 80 GB15448all 256K78.2 GB
H100 80 GB13943all 256K78.1 GB
RTX PRO 6000 Blackwell 96 GB18157all 256K93.8 GB
DGX Spark (GB10) 128 GB unified21768all 256K107 GB
H200 141 GB30094all 256K138 GB
B200 180 GB401126all 256K176 GB
2× RTX 4060 Ti 16 GB
tensor parallel
206227K15.4 GB a card
2× RTX 4090 24 GB
tensor parallel
6320all 256K23.4 GB a card
2× RTX 3090 24 GB
tensor parallel
6320all 256K23.4 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
121.3 GB22.1 GB
522.8 GB26.8 GB
823.9 GB30.4 GB
1626.9 GB39.8 GB
3232.8 GB58.6 GB
6444.7 GB96.3 GB

One card, with vLLM's small-card settings.

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
A3-Qwen3.5-9B (above) ↗ 9.4BBF16 18.8 GBRTX 4090 24 GB
A3-Qwen3.5-4B ↗ 5.2BBF16 10.4 GBRTX 4060 Ti 16 GB
A3-Qwen3.5-2B ↗ 2.7BBF16 5.4 GBRTX 3060 12 GB

From the model card

What McGill-NLP says about A3-Qwen3.5

Read 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.

Usage

Serve with vLLM:

vllm serve McGill-NLP/A3-Qwen3.5-9B --tensor-parallel-size 2 --max-model-len 65536 --enforce-eager --dtype bfloat16

Training

  • Base model: Qwen/Qwen3.5-9B
  • Data: A3-Synth (16k examples from Gemini 3 Pro trajectories)
  • Method: SFT with FSDP
  • Max sequence length: 16,384
  • Learning rate: 1e-5
  • Epochs: 2
  • Batch size: 1 per GPU, gradient accumulation 4

Model Variants

ModelParametersLink
A3-Qwen3.5-9B9BMcGill-NLP/A3-Qwen3.5-9B
A3-Qwen3.5-4B4BMcGill-NLP/A3-Qwen3.5-4B
A3-Qwen3.5-2B2BMcGill-NLP/A3-Qwen3.5-2B

Citation

@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.

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