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Can the NVIDIA Jetson Orin Nano Super (8 GB) run Ling 3.0 Tiny (8B-A1.3B)?

76 ptsGreat (A) · 76 of 100 points: Model quality 22.7/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q3_K_M it needs about 5.2 GB of 6.0 GB usable, with an estimated speed of 62.3 tok/s.

Best quant
Q3_K_M
Generation speed
62.3 tok/s
Prompt processing
1,163 tok/s
First token (1K prompt)
0.9 s
Largest safe context
37K

Download Ling 3.0 Tiny (8B-A1.3B)

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF (opens in a new tab)

ollama run hf.co/inclusionAI/Ling-3.0-tiny-GGUF

Original weights · Hugging Face

Full-precision files from Ant Group (inclusionAI), for fine-tuning, vLLM, Transformers.

huggingface.co/inclusionAI/Ling-3.0-tiny (opens in a new tab)

No sign-in needed.

Links checked 7 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.22.7 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.24.0 / 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
Total76.7 / 10076 ptsGreat (A) · 76 of 100 points: Model quality 22.7/45, Memory fit 24.0/25, 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 Jetson Orin Nano Super (8 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 4.5 GB 6.0 GB 79.1 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 18.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q3_K_M 5.2 GB 6.0 GB 62.3 tok/s Tight fit 76 ptsGreat (A) · 76 of 100 points: Model quality 22.7/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q4_K_M 6.1 GB 6.0 GB 40.9 tok/s Runs with offload 65 ptsGood (B) · 65 of 100 points: Model quality 25.7/45, Memory fit 10.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 6.9 GB 6.0 GB 33.8 tok/s Heavy offload 58 ptsGood (B) · 58 of 100 points: Model quality 26.2/45, Memory fit 4.0/25, Speed 13.8/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 7.8 GB 6.0 GB — Won't run 8 ptsWon't run (F) · 8 of 100 points: Model quality 8.0/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (opens in a new tab)
Q8_0 9.7 GB 6.0 GB — Won't run 8 ptsWon't run (F) · 8 of 100 points: Model quality 8.1/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (opens in a new tab)
F16 17.1 GB 6.0 GB — Won't run 8 ptsWon't run (F) · 8 of 100 points: Model quality 8.1/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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.