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Can the NVIDIA GeForce RTX 5090 run Ornith 1.5 9B?

89 ptsExcellent (S) · 89 of 100 points: Model quality 34.7/45, 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 21.8 GB of 32.0 GB usable, with an estimated speed of 53.5 tok/s.

Best quant
F16
Generation speed
53.5 tok/s
Prompt processing
7,819 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Download Ornith 1.5 9B

Recommended for the NVIDIA GeForce RTX 5090: 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/ornith-ai/Ornith-1.5-9B-GGUF (opens in a new tab)

ollama run hf.co/ornith-ai/Ornith-1.5-9B-GGUF:BF16

Original weights · Hugging Face

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

huggingface.co/ornith-ai/Ornith-1.5-9B (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.34.7 / 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
Total89.7 / 10089 ptsExcellent (S) · 89 of 100 points: Model quality 34.7/45, 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 5090

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 6.3 GB 32.0 GB 374 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 24.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.6/15 GGUF files (opens in a new tab)
Q3_K_M 7.2 GB 32.0 GB 295 tok/s Runs well 74 ptsGreat (A) · 74 of 100 points: Model quality 29.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.2/15 GGUF files (opens in a new tab)
Q4_K_M 8.4 GB 32.0 GB 228 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 32.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.0/15 GGUF (opens in a new tab)
Q5_K_M 9.3 GB 32.0 GB 188 tok/s Runs well 80 ptsGreat (A) · 80 of 100 points: Model quality 33.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.7/15 GGUF (opens in a new tab)
Q6_K 10.4 GB 32.0 GB 158 tok/s Runs well 81 ptsGreat (A) · 81 of 100 points: Model quality 34.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.5/15 GGUF (opens in a new tab)
Q8_0 12.7 GB 32.0 GB 116 tok/s Runs well 83 ptsGreat (A) · 83 of 100 points: Model quality 34.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.2/15 GGUF (opens in a new tab)
F16 21.8 GB 32.0 GB 53.5 tok/s Runs well 89 ptsExcellent (S) · 89 of 100 points: Model quality 34.7/45, 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.