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Can the NVIDIA GeForce RTX 5070 Ti run UI-Mate 27B?

86 ptsExcellent (S) · 86 of 100 points: Model quality 32.5/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q3_K_M it needs about 15.3 GB of 16.0 GB usable, with an estimated speed of 52.0 tok/s.

Best quant
Q3_K_M
Generation speed
52.0 tok/s
Prompt processing
1,155 tok/s
First token (1K prompt)
0.9 s
Largest safe context
17K

Download UI-Mate 27B

Recommended for the NVIDIA GeForce RTX 5070 Ti: Q3_K_M. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q3_K_M file (opens in a new tab)

huggingface.co/bartowski/tencent_UI-Mate-27B-GGUF (opens in a new tab)

ollama run hf.co/bartowski/tencent_UI-Mate-27B-GGUF:Q3_K_M

Original weights · Hugging Face

Full-precision files from Tencent, for fine-tuning, vLLM, Transformers.

huggingface.co/tencent/UI-Mate-27B (opens in a new tab)

No sign-in needed.

Links checked 5 days ago against Hugging Face, Ollama and GitHub.

How the S grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.32.5 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Total86.5 / 10086 ptsExcellent (S) · 86 of 100 points: Model quality 32.5/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the NVIDIA GeForce RTX 5070 Ti

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 12.9 GB 16.0 GB 66.0 tok/s Runs well 82 ptsGreat (A) · 82 of 100 points: Model quality 27.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q3_K_M 15.3 GB 16.0 GB 52.0 tok/s Tight fit 86 ptsExcellent (S) · 86 of 100 points: Model quality 32.5/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 18.6 GB 16.0 GB 13.6 tok/s Runs with offload 70 ptsGood (B) · 70 of 100 points: Model quality 36.8/45, Memory fit 10.0/25, Speed 8.7/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 21.4 GB 16.0 GB 7.5 tok/s Runs with offload 69 ptsGood (B) · 69 of 100 points: Model quality 37.5/45, Memory fit 10.0/25, Speed 6.5/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 24.4 GB 16.0 GB 4.9 tok/s Heavy offload 62 ptsGood (B) · 62 of 100 points: Model quality 38.3/45, Memory fit 4.0/25, Speed 5.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 30.9 GB 16.0 GB 2.8 tok/s Heavy offload 61 ptsGood (B) · 61 of 100 points: Model quality 38.7/45, Memory fit 4.0/25, Speed 3.9/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 56.7 GB 16.0 GB — Won't run 11 ptsWon't run (F) · 11 of 100 points: Model quality 11.6/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.