Model reference · open weights

UI-Mate

LLMs tencent Vision + text 2 builds Open weights 723 dl/mo

UI-Mate is an open-weight language model from tencent. UI-Mate-27B (BF16) weighs 54.7 GB; the smallest configuration that runs it is H100 80 GB.

  • UI-Mate-9B is an open-weight foundation GUI agent developed by Tencent that observes live screenshots to produce structured keyboard and mouse actions for native desktop interaction.
  • The model has 9.4 billion parameters and is based on Qwen3.5-9B, using supervised fine-tuning and online reinforcement learning to execute tasks from natural-language instructions.
  • It is released under the Apache-2.0 license and is intended for research and development in controlled desktop environments.

Summary of the tencent/UI-Mate-9B model card, 2026-10-01. The estimate below is for another build of the family.

What it is

Released bytencent
Released2026-08-14
Parameters9.4B
VRAM54.7 GB for the weights

What it runs on

Memory and cards for UI-Mate-27B (BF16)

54.7 GBweights, file size
66 MBcache per 1K tokens
308 MBfixed state per request
2.2 GBruntime overhead, at least
262,144 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB … L40S 48 GB
6 smaller cards
———
A100 80 GB258all 256K78.2 GB
H100 80 GB186227K78.1 GB
RTX PRO 6000 Blackwell 96 GB3712all 256K93.8 GB
DGX Spark (GB10) 128 GB unified5318all 256K107 GB
H200 141 GB8930all 256K138 GB
B200 180 GB13345all 256K176 GB
2× L40S 48 GB
tensor parallel
3411all 256K44.0 GB a card
4× RTX 4090 24 GB
tensor parallel
3512all 256K23.4 GB a card
4× RTX 3090 24 GB
tensor parallel
3512all 256K23.4 GB a card
4× RTX 5090 32 GB
tensor parallel
7124all 256K31.0 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
157.8 GB59.4 GB
561.1 GB69.2 GB
863.7 GB76.6 GB
1670.4 GB96.2 GB
3283.9 GB135 GB
64111 GB214 GB

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

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
UI-Mate-9B ↗ 9.4BBF16 18.8 GBRTX 4090 24 GB
UI-Mate-27B (above) ↗ 27.4BBF16 54.7 GBH100 80 GB

From the model card

What tencent says about UI-Mate

Read the model card

UI-Mate: Advancing Open-Weight Foundation GUI Agents with In-Context Demonstrations

Show the workflow once. Let the agent adapt it to the task at hand.

Tencent HY Frontier

Project Page · GitHub · arXiv

Overview

UI-Mate-9B is an open-weight foundation GUI agent for long-horizon work across applications and operating systems. It observes live screenshots, reasons over the visible state, and produces structured keyboard and mouse actions for native desktop interaction.

It executes a task from a natural-language instruction and live screenshots alone, planning from what is currently on screen and re-planning as the interface changes.

Model Details

  • Parameters: 9B
  • Base model: Qwen3.5-9B
  • Input: task instruction, screenshots, and interaction history
  • Output: reasoning, a concise action description, and structured computer-use tool calls
  • Action space: mouse, keyboard, scrolling, waiting, user interaction, and task completion
  • Training: supervised fine-tuning followed by online reinforcement learning in executable GUI environments
  • License: Apache-2.0

UI-Mate is an agent checkpoint rather than a standalone visual-chat model. We recommend using the official prompt, response parser, and interaction harness from the UI-Mate repository.

CheckpointIntended use
UI-Mate-27BGeneral computer use at 27B
UI-Mate-9BGeneral computer use at 9B
UI-Mate-democua-27BDemonstration-guided computer use

Highlights

  • Strong general computer-use performance at an efficient 9B scale.
  • Long-horizon execution across multiple applications.
  • Live-screen grounding instead of coordinate replay.
  • Structured actions compatible with pyautogui.
  • OpenAI-compatible serving and client interface.

Evaluation

BenchmarkUI-Mate-9B
OSWorld-Verified · average score66.2
WindowsAgentArena · average score61.7
OSWorkerBench · strict success34.00
OSWorkerBench · progress66.55

See the project page for evaluation details and updates.

Quick Start

1. Serve the model with vLLM

vLLM automatically downloads the checkpoint from Hugging Face Hub on first launch.

pip install -U vllm openai pillow

vllm serve tencent/UI-Mate-9B \
    --trust-remote-code \
    --served-model-name UI_Mate \
    --port 8000 \
    --tensor-parallel-size 1 \
    --gpu-memory-utilization 0.85 \
    --mm-encoder-tp-mode data \
    --chat-template-content-format openai \
    --limit-mm-per-prompt '{"image":6,"video":0}'

The default agent retains five screenshots in context. The server must therefore admit at least six images because the newest screenshot arrives before the oldest one is collapsed.

Confirm that the endpoint exposes the expected model name:

curl -s http://127.0.0.1:8000/v1/models

2. Run the reference examples

git clone https://github.com/Tencent/UI-Mate.git
cd UI-Mate

python examples/run_agent.py \
    --base-url http://127.0.0.1:8000/v1

Run a complete recorded trajectory:

python examples/run_agent.py \
    --replay \
    --base-url http://127.0.0.1:8000/v1

Or provide your own screenshot:

python examples/run_agent.py \
    --image /path/to/screen.png \
    --instruction "Export this spreadsheet as HTML and open it in Chrome"

3. Use the Python agent

from agents.ui_mate_agent import UIMateAgent

agent = UIMateAgent(
    base_url="http://127.0.0.1:8000/v1",
    model="UI_Mate",
)

with open("screen.png", "rb") as f:
    response, actions = agent.predict(
        "Install the autoDocstring extension in VS Code.",
        {"screenshot": f.read()},
    )

print(response)
print(actions)

# Reset the interaction history before starting a new task.
agent.reset()

Intended Use and Limitations

UI-Mate-9B is intended for research and development of screenshot-based GUI agents in controlled desktop environments.

Its behavior can be affected by application versions, screen layouts, display scaling, latency, and unexpected UI state. Benchmark performance does not guarantee reliable execution in arbitrary environments, and the model requires an external runtime to execute its predicted actions.

Safety

Computer-use agents can make mistakes, encounter prompt injection, or trigger consequential actions.

  • Prefer isolated or disposable environments.
  • Avoid unattended, high-stakes, or destructive workflows.
  • Require human confirmation before sensitive operations.
  • Monitor the interaction trajectory and verify the resulting application state.
  • Do not treat a model-reported success as proof that the intended outcome was achieved.

License

UI-Mate is released under the Apache License 2.0. Third-party components remain subject to their respective licenses. See the repository LICENSE for details.

Citation

@article{uimate2026,
  title         = {UI-Mate: Advancing Open-Weight Foundation GUI Agents with In-Context Demonstrations},
  author        = {Tencent HY Frontier Team},
  journal       = {arXiv preprint arXiv:2608.15930},
  year          = {2026},
}

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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