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Can the NVIDIA GeForce RTX 5090 run Apriel 1.5 15B Thinker?

86 ptsExcellent (S) · 86 of 100 points: Model quality 32.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.1/15

Yes, it runs well

Runs well. At Q8_0 it needs about 19.6 GB of 32.0 GB usable, with an estimated speed of 75.4 tok/s.

Best quant
Q8_0
Generation speed
75.4 tok/s
Prompt processing
5,078 tok/s
First token (1K prompt)
0.3 s
Largest safe context
69K

Download Apriel 1.5 15B Thinker

Recommended for the NVIDIA GeForce RTX 5090: Q8_0. 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 Q8_0 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:Q8_0

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 6 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.32.4 / 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.14.1 / 15
Total86.5 / 10086 ptsExcellent (S) · 86 of 100 points: Model quality 32.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.1/15

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

Every quantization on the NVIDIA GeForce RTX 5090

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 9.8 GB 32.0 GB 243 tok/s Runs well 69 ptsGood (B) · 69 of 100 points: Model quality 22.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.0/15 GGUF (opens in a new tab)
Q3_K_M 11.1 GB 32.0 GB 191 tok/s Runs well 75 ptsGreat (A) · 75 of 100 points: Model quality 27.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.0/15 GGUF (opens in a new tab)
Q4_K_M 12.9 GB 32.0 GB 148 tok/s Runs well 80 ptsGreat (A) · 80 of 100 points: Model quality 30.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.3/15 GGUF (opens in a new tab)
Q5_K_M 14.4 GB 32.0 GB 122 tok/s Runs well 81 ptsGreat (A) · 81 of 100 points: Model quality 31.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.4/15 GGUF (opens in a new tab)
Q6_K 16.0 GB 32.0 GB 103 tok/s Runs well 83 ptsGreat (A) · 83 of 100 points: Model quality 32.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.5/15 GGUF (opens in a new tab)
Q8_0 19.6 GB 32.0 GB 75.4 tok/s Runs well 86 ptsExcellent (S) · 86 of 100 points: Model quality 32.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.1/15 GGUF (opens in a new tab)
F16 33.5 GB 32.0 GB 14.9 tok/s Runs with offload 66 ptsGood (B) · 66 of 100 points: Model quality 32.4/45, Memory fit 10.0/25, Speed 9.2/15, 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.