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Can the NVIDIA GeForce RTX 5070 Ti run Fara 1.5 4B?

81 ptsGreat (A) · 81 of 100 points: Model quality 26.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At F16 it needs about 10.7 GB of 16.0 GB usable, with an estimated speed of 56.8 tok/s.

Best quant
F16
Generation speed
56.8 tok/s
Prompt processing
6,962 tok/s
First token (1K prompt)
0.3 s
Largest safe context
164K

Download Fara 1.5 4B

Recommended for the NVIDIA GeForce RTX 5070 Ti: F16. 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 F16 file (opens in a new tab)

huggingface.co/bartowski/Fara1.5-4B-GGUF (opens in a new tab)

ollama run hf.co/bartowski/Fara1.5-4B-GGUF:BF16

Original weights · Hugging Face

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

huggingface.co/microsoft/Fara1.5-4B (opens in a new tab)

No sign-in needed.

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

How the A 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.26.1 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.25.0 / 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
Total81.1 / 10081 ptsGreat (A) · 81 of 100 points: Model quality 26.1/45, Memory fit 25.0/25, 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 3.5 GB 16.0 GB 398 tok/s Runs well 63 ptsGood (B) · 63 of 100 points: Model quality 18.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.0/15 GGUF (opens in a new tab)
Q3_K_M 3.9 GB 16.0 GB 313 tok/s Runs well 67 ptsGood (B) · 67 of 100 points: Model quality 21.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.6/15 GGUF (opens in a new tab)
Q4_K_M 4.4 GB 16.0 GB 242 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 24.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.4/15 GGUF (opens in a new tab)
Q5_K_M 4.9 GB 16.0 GB 200 tok/s Runs well 72 ptsGreat (A) · 72 of 100 points: Model quality 25.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.1/15 GGUF (opens in a new tab)
Q6_K 5.4 GB 16.0 GB 168 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 25.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.8/15 GGUF (opens in a new tab)
Q8_0 6.5 GB 16.0 GB 123 tok/s Runs well 75 ptsGreat (A) · 75 of 100 points: Model quality 26.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.3/15 GGUF (opens in a new tab)
F16 10.7 GB 16.0 GB 56.8 tok/s Runs well 81 ptsGreat (A) · 81 of 100 points: Model quality 26.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (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.