UI TARS 1.5 7B — Hardware Requirements & GPU Compatibility
Vision
UI-TARS-1.5-7B is ByteDance's 8.3-billion-parameter vision-language model, built on a Qwen2.5-VL-7B foundation and tuned as a GUI and computer-use agent rather than a chatbot. It reasons through its thoughts before acting, using reinforcement learning to control desktop and browser interfaces, play games, and complete multi-step tasks from screenshots. ByteDance evaluates it against agents like OpenAI's CUA and Claude on benchmarks such as OSWorld and Windows Agent Arena. At this size, local inference is practical on a single mainstream-to-high-end consumer GPU once quantized. It supports a 128,000 token context window, useful for long action histories in agent tasks. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use. Published in April 2025, it builds on the original UI-TARS architecture with added inference-time reasoning scaling.
600.7K downloads
605 likesSpecifications
- Publisher
- ByteDance-Seed
- Parameters
- 8.3B
- Architecture
- Qwen2_5_VLForConditionalGeneration
- Context Length
- 128,000 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2025-04-16
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does UI TARS 1.5 7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.9 GB | 11.2 GB | 3.52 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.0 GB | 11.3 GB | 3.63 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.5 GB | 11.7 GB | 4.04 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.6 GB | 11.8 GB | 4.15 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.4 GB | 12.6 GB | 4.98 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.3 GB | 13.6 GB | 5.91 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.3 GB | 14.5 GB | 6.84 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.7 GB | 15.9 GB | 8.29 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run UI TARS 1.5 7B?
Q4_K_M · 5.4 GBUI TARS 1.5 7B (Q4_K_M) requires 5.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 128K context window can add up to 7.2 GB, bringing total usage to 12.6 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run UI TARS 1.5 7B?
Q4_K_M · 5.4 GB58 devices with unified memory can run UI TARS 1.5 7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download UI TARS 1.5 7B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does UI TARS 1.5 7B need?
UI TARS 1.5 7B requires 5.4 GB of VRAM at Q4_K_M, or 17 GB at BF16. Full 128K context adds up to 7.2 GB (12.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.3B × 4.8 bits ÷ 8 = 5 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7.6 GB (at full 128K context)
VRAM usage by quantization
Q4_K_M5.4 GBQ4_K_M + full context12.6 GB- What's the best quantization for UI TARS 1.5 7B?
For UI TARS 1.5 7B, Q4_K_M (5.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.7 GB.
VRAM requirement by quantization
IQ2_XXS2.7 GBIQ3_XS3.8 GBQ4_04.6 GBIQ4_NL5.1 GBQ4_K_M ★5.4 GBBF1617.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run UI TARS 1.5 7B on a Mac?
UI TARS 1.5 7B requires at least 2.7 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run UI TARS 1.5 7B locally?
Yes — UI TARS 1.5 7B can run locally on consumer hardware. At Q4_K_M quantization it needs 5.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is UI TARS 1.5 7B?
At Q4_K_M, UI TARS 1.5 7B can reach ~891 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~122 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 5.4 × 0.65 = ~965 tok/s
Estimated speed at Q4_K_M (5.4 GB)
~965 tok/s~122 tok/s~965 tok/s~891 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of UI TARS 1.5 7B?
At Q4_K_M, the download is about 4.98 GB. The full-precision BF16 version is 16.58 GB. The smallest option (IQ2_XXS) is 2.28 GB.
- Which GPUs can run UI TARS 1.5 7B?
52 consumer GPUs can run UI TARS 1.5 7B at Q4_K_M (5.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
- Which devices can run UI TARS 1.5 7B?
59 devices with unified memory can run UI TARS 1.5 7B at Q4_K_M (5.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.