Model reference · open weights

UI-TARS-SFT

LLMs ByteDance-Seed Vision + text 1 build Open weights 5k dl/mo

UI-TARS-SFT is an open-weight language model from ByteDance. UI-TARS-2B-SFT (FP32) weighs 4.9 GB; the smallest configuration that runs it is RTX 3060 12 GB.

  • UI-TARS-SFT is a 2.4B parameter image-text-to-text model developed by ByteDance for interacting with graphical user interfaces.
  • It integrates perception, reasoning, and action capabilities within a single vision-language model to enable end-to-end task automation.
  • The model supports English and is released under the Apache-2.0 license.

Summary of the ByteDance-Seed/UI-TARS-2B-SFT model card, 2026-10-01

What it is

Released byByteDance
Released2025-01-20
Parameters2.4B
VRAM4.9 GB for the weights

What it runs on

Memory and cards for UI-TARS-2B-SFT (FP32)

4.9 GBweights, file size
29 MBcache per 1K tokens
2.0 GBruntime overhead, at least
32,768 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB205all 32K11.6 GB
RTX 4060 Ti 16 GB369all 32K15.4 GB
RTX 3090 24 GB7017all 32K23.4 GB
RTX 4090 24 GB7017all 32K23.4 GB
RTX 5090 32 GB10225all 32K31.0 GB
L40S 48 GB15739all 32K44.0 GB
A100 80 GB30375all 32K78.2 GB
H100 80 GB28571all 32K78.1 GB
RTX PRO 6000 Blackwell 96 GB35288all 32K93.8 GB
DGX Spark (GB10) 128 GB unified410102all 32K107 GB
H200 141 GB540135all 32K138 GB
B200 180 GB701175all 32K176 GB
Memory needed at each load
Requests at once8K tokens each32K tokens each
17.1 GB7.8 GB
58.0 GB11.6 GB
88.7 GB14.4 GB
1610.6 GB21.9 GB
3214.4 GB36.9 GB
6421.9 GB67.0 GB

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

From the model card

What ByteDance says about UI-TARS-SFT

Read the model card

UI-TARS-2B-SFT  |  UI-TARS-2B-gguf  |  UI-TARS-7B-SFT  |  UI-TARS-7B-DPO(Recommended)  |  UI-TARS-7B-gguf  |  UI-TARS-72B-SFT  |  UI-TARS-72B-DPO(Recommended)

Introduction

UI-TARS is a next-generation native GUI agent model designed to interact seamlessly with graphical user interfaces (GUIs) using human-like perception, reasoning, and action capabilities. Unlike traditional modular frameworks, UI-TARS integrates all key components—perception, reasoning, grounding, and memory—within a single vision-language model (VLM), enabling end-to-end task automation without predefined workflows or manual rules.

This repository contains the model for the paper UI-TARS: Pioneering Automated GUI Interaction with Native Agents.

Code: https://github.com/bytedance/UI-TARS

Performance

Perception Capabilty Evaluation

ModelVisualWebBenchWebSRCSQAshort
Qwen2-VL-7B73.381.884.9
Qwen-VL-Max74.191.178.6
Gemini-1.5-Pro75.488.982.2
UIX-Qwen2-7B75.982.978.8
Claude-3.5-Sonnet78.290.483.1
GPT-4o78.587.782.3
UI-TARS-2B72.989.286.4
UI-TARS-7B79.793.687.7
UI-TARS-72B82.889.388.6

Grounding Capability Evaluation

  • ScreenSpot Pro
Agent ModelDev-TextDev-IconDev-AvgCreative-TextCreative-IconCreative-AvgCAD-TextCAD-IconCAD-AvgScientific-TextScientific-IconScientific-AvgOffice-TextOffice-IconOffice-AvgOS-TextOS-IconOS-AvgAvg-TextAvg-IconAvg
QwenVL-7B0.00.00.00.00.00.00.00.00.00.70.00.40.00.00.00.00.00.00.10.00.1
GPT-4o1.30.00.71.00.00.62.00.01.52.10.01.21.10.00.90.00.00.01.30.00.8
SeeClick0.60.00.31.00.00.62.50.01.93.50.02.01.10.00.92.80.01.51.80.01.1
Qwen2-VL-7B2.60.01.31.50.00.90.50.00.46.30.03.53.41.93.00.90.00.52.50.21.6
OS-Atlas-4B7.10.03.73.01.42.32.00.01.59.05.57.55.13.84.85.60.03.15.01.73.7
ShowUI-2B16.91.49.49.10.05.32.50.01.913.27.310.615.37.513.510.32.26.610.82.67.7
CogAgent-18B14.90.78.09.60.05.67.13.16.122.21.813.413.00.010.05.60.03.112.00.87.7
Aria-UI16.20.08.423.72.114.77.61.66.127.16.418.120.31.916.14.70.02.617.12.011.3
UGround-7B26.62.114.727.32.817.014.21.611.131.92.719.331.611.327.017.80.09.725.02.816.5
Claude Computer Use22.03.912.625.93.416.814.53.711.933.915.825.830.116.326.911.04.58.123.47.117.1
OS-Atlas-7B33.11.417.728.82.817.912.24.710.337.57.324.433.95.727.427.14.516.828.14.018.9
UGround-V1-7B--35.5--27.8--13.5--38.8--48.8--26.1--31.1
UI-TARS-2B47.44.126.442.96.327.617.84.714.656.917.3

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

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