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Can the NVIDIA GeForce RTX 5060 Ti 16GB run Apriel 1.5 15B Thinker?

85 ptsExcellent (S) · 85 of 100 points: Model quality 30.8/45, Memory fit 25.0/25, Speed 14.4/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q4_K_M it needs about 12.1 GB of 16.0 GB usable, with an estimated speed of 37.0 tok/s.

Best quant
Q4_K_M
Generation speed
37.0 tok/s
Prompt processing
1,148 tok/s
First token (1K prompt)
0.9 s
Largest safe context
27K

Download Apriel 1.5 15B Thinker

Recommended for the NVIDIA GeForce RTX 5060 Ti 16GB: Q4_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 Q4_K_M file (opens in a new tab)

huggingface.co/unsloth/Apriel-1.5-15b-Thinker-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Apriel-1.5-15b-Thinker-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/ServiceNow-AI/Apriel-1.5-15b-Thinker (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.30.8 / 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.14.4 / 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
Total85.2 / 10085 ptsExcellent (S) · 85 of 100 points: Model quality 30.8/45, Memory fit 25.0/25, Speed 14.4/15, 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 5060 Ti 16GB

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 9.0 GB 16.0 GB 60.8 tok/s Runs well 75 ptsGreat (A) · 75 of 100 points: Model quality 22.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.9/15 GGUF (opens in a new tab)
Q3_K_M 10.3 GB 16.0 GB 47.8 tok/s Runs well 82 ptsGreat (A) · 82 of 100 points: Model quality 27.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.8/15 GGUF (opens in a new tab)
Q4_K_M 12.1 GB 16.0 GB 37.0 tok/s Runs well 85 ptsExcellent (S) · 85 of 100 points: Model quality 30.8/45, Memory fit 25.0/25, Speed 14.4/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 13.6 GB 16.0 GB 30.6 tok/s Runs well 84 ptsGreat (A) · 84 of 100 points: Model quality 31.4/45, Memory fit 25.0/25, Speed 13.1/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 15.2 GB 16.0 GB 25.7 tok/s Tight fit 83 ptsGreat (A) · 83 of 100 points: Model quality 32.1/45, Memory fit 24.0/25, Speed 12.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 18.8 GB 16.0 GB 9.4 tok/s Runs with offload 64 ptsGood (B) · 64 of 100 points: Model quality 32.4/45, Memory fit 10.0/25, Speed 7.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 32.7 GB 16.0 GB 2.1 tok/s Heavy offload 54 ptsUsable (C) · 54 of 100 points: Model quality 32.4/45, Memory fit 4.0/25, Speed 3.4/15, 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.