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Can the NVIDIA A100 80GB PCIe run Ornith 1.0 35B-A3B?

92 ptsExcellent (S) · 92 of 100 points: Model quality 40.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.4/15

Yes, it runs well

Runs well. At Q8_0 it needs about 42.9 GB of 80.0 GB usable, with an estimated speed of 202 tok/s.

Best quant
Q8_0
Generation speed
202 tok/s
Prompt processing
18,720 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Download Ornith 1.0 35B-A3B

Recommended for the NVIDIA A100 80GB PCIe: 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/ornith-ai/Ornith-1.0-35B-GGUF (opens in a new tab)

ollama run hf.co/ornith-ai/Ornith-1.0-35B-GGUF:Q8_0

Original weights · Hugging Face

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

huggingface.co/ornith-ai/Ornith-1.0-35B (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.40.1 / 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.12.4 / 15
Total92.4 / 10092 ptsExcellent (S) · 92 of 100 points: Model quality 40.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.4/15

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

Every quantization on the NVIDIA A100 80GB PCIe

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 19.1 GB 80.0 GB 650 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 28.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.5/15 GGUF files (opens in a new tab)
Q3_K_M 22.4 GB 80.0 GB 512 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 6.5/15 GGUF files (opens in a new tab)
Q4_K_M 26.7 GB 80.0 GB 396 tok/s Runs well 85 ptsExcellent (S) · 85 of 100 points: Model quality 38.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.7/15 GGUF (opens in a new tab)
Q5_K_M 30.3 GB 80.0 GB 327 tok/s Runs well 87 ptsExcellent (S) · 87 of 100 points: Model quality 38.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.7/15 GGUF (opens in a new tab)
Q6_K 34.2 GB 80.0 GB 275 tok/s Runs well 89 ptsExcellent (S) · 89 of 100 points: Model quality 39.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.9/15 GGUF (opens in a new tab)
Q8_0 42.9 GB 80.0 GB 202 tok/s Runs well 92 ptsExcellent (S) · 92 of 100 points: Model quality 40.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.4/15 GGUF (opens in a new tab)
F16 76.7 GB 80.0 GB 92.9 tok/s Tight fit 94 ptsExcellent (S) · 94 of 100 points: Model quality 40.1/45, Memory fit 24.0/25, 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.