UI TARS 1.5 7B — Hardware Requirements & GPU Compatibility
VisionFunctions
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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~122 tok/s · 125K)Macs from 8 GB (IQ3_XXS · ~20 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run UI TARS 1.5 7B
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/mradermacher/UI-TARS-1.5-7B-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 17.8 GB free · 16K context
Download: UI-TARS-1.5-7B.Q4_K_M.gguf4.68 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (5 GB headroom) · Q4_K_M
- Generation speed
- ~110 tok/s
- generation speed
- Cost per 1M output tokens
- $0.31
- per 1M output tokens
How Much VRAM Does UI TARS 1.5 7B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.9 GB | 5.7 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 125K context · ~166 tok/s | |
| 4.0 GB | 5.8 GB | 3-bit small — 3-bit small quantization | Fits: 125K context · ~162 tok/s | |
| 4.5 GB | 6.2 GB | 3-bit medium — 3-bit medium quantization | Fits: 125K context · ~147 tok/s | |
| 4.6 GB | 6.3 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 125K context · ~144 tok/s | |
| 5.4 GB | 7.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 125K context · ~122 tok/s | |
| 6.3 GB | 8.1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 125K context · ~104 tok/s | |
| 7.3 GB | 9 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 125K context · ~90 tok/s | |
| 8.7 GB | 10.5 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 125K context · ~75 tok/s |
Selected Q4_K_M
4-bit medium quantization6.2GBat 16K context
- Weights
- 5.0 GB
- KV cache
- 0.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 17.8 GB free · ~122 tok/s
≥50 tok/s needs ≥414 GB/s · RTX 5090 ~216, RTX 3090 Ti ~122
Which Hardware 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. 151 devices with unified memory can run UI TARS 1.5 7B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 1.8 GB free · up to 32K ctx
16 GPUs · 1 device family
RTX 3080~92Simulate output on RTX 3080RTX 2080 Ti~74Simulate output on RTX 2080 TiRTX 3070 Ti~73Simulate output on RTX 3070 TiOrin Nano~12Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 13.8 GB free · up to 125K ctx
1 GPU
RX 7900 XT~89Simulate output on RX 7900 XT32–40 GB
fits · 25.8 GB free · up to 125K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~216Simulate output on RTX 5090M5 Max36 GB~60Simulate output on Mac Studio M5 Max (36 GB)M532 GB~20Simulate output on MacBook Air 13" M5 (32 GB)Orin~25Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~16Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite Switch 4.1 8BIBM | — | 8.4 GB | ~111 | same size |
| Ling 3.0 TinyInclusion AI | — | 5.5 GB | ~364 | same size |
| Apertus V1.5 8Bswiss-ai | — | 8.5 GB | ~111 | same size |
| LFM2.5 8B A1BLiquid AI | — | 5.6 GB | ~335 | same size |
| BitCPM CANN 8BOpenBMB | — | 5.6 GB | ~127 | same size |
| Gemma 4 E4BGoogle | — | 5.6 GB | ~123 | same size |
Specifications
- 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
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 7.6 GB (at full 128K context)
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run UI TARS 1.5 7B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run UI TARS 1.5 7B, the smallest at IQ3_XXS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.4 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M2 Pro (16 GB).
Can I run UI TARS 1.5 7B locally?
Yes — UI TARS 1.5 7B can run locally on consumer hardware. 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 ÷ 5.39 × 0.65 = ~965 tok/s
Estimated speed at Q4_K_M (5.4 GB)
Real-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. 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?
151 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, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.