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Can the Intel Core Ultra 200H (Arc 140T) (32 GB) run Ornith 1.5 9B?

78 ptsGreat (A) · 78 of 100 points: Model quality 34.3/45, Memory fit 25.0/25, Speed 6.2/15, Memory use 13.0/15

Yes, it runs well

Runs well. At Q6_K it needs about 10.2 GB of 18.2 GB usable, with an estimated speed of 6.9 tok/s.

Best quant
Q6_K
Generation speed
6.9 tok/s
Prompt processing
524 tok/s
First token (1K prompt)
2.0 s
Largest safe context
246K

Download Ornith 1.5 9B

Recommended for the Intel Core Ultra 200H (Arc 140T) (32 GB): Q6_K. 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 Q6_K 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:Q6_K

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 6 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.34.3 / 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.6.2 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.13.0 / 15
Total78.5 / 10078 ptsGreat (A) · 78 of 100 points: Model quality 34.3/45, Memory fit 25.0/25, Speed 6.2/15, Memory use 13.0/15

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

Every quantization on the Intel Core Ultra 200H (Arc 140T) (32 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 6.2 GB 18.2 GB 16.3 tok/s Runs well 66 ptsGood (B) · 66 of 100 points: Model quality 24.3/45, Memory fit 25.0/25, Speed 9.6/15, Memory use 7.8/15 GGUF files (opens in a new tab)
Q3_K_M 7.1 GB 18.2 GB 12.9 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 29.1/45, Memory fit 25.0/25, Speed 8.5/15, Memory use 8.9/15 GGUF files (opens in a new tab)
Q4_K_M 8.2 GB 18.2 GB 10.0 tok/s Runs well 75 ptsGreat (A) · 75 of 100 points: Model quality 32.9/45, Memory fit 25.0/25, Speed 7.5/15, Memory use 10.4/15 GGUF (opens in a new tab)
Q5_K_M 9.2 GB 18.2 GB 8.2 tok/s Runs well 77 ptsGreat (A) · 77 of 100 points: Model quality 33.6/45, Memory fit 25.0/25, Speed 6.8/15, Memory use 11.6/15 GGUF (opens in a new tab)
Q6_K 10.2 GB 18.2 GB 6.9 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 34.3/45, Memory fit 25.0/25, Speed 6.2/15, Memory use 13.0/15 GGUF (opens in a new tab)
Q8_0 12.6 GB 18.2 GB 5.1 tok/s Runs well 79 ptsGreat (A) · 79 of 100 points: Model quality 34.7/45, Memory fit 25.0/25, Speed 5.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 21.6 GB 18.2 GB 2.0 tok/s Heavy offload 56 ptsUsable (C) · 56 of 100 points: Model quality 34.7/45, Memory fit 4.0/25, Speed 3.3/15, 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.