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Can the Intel Core Ultra 200H (Arc 140T) (96 GB) run Qwen3-Next 80B-A3B Thinking?

83 ptsGreat (A) · 83 of 100 points: Model quality 32.5/45, Memory fit 25.0/25, Speed 10.8/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q3_K_M it needs about 45.0 GB of 54.7 GB usable, with an estimated speed of 20.7 tok/s.

Best quant
Q3_K_M
Generation speed
20.7 tok/s
Prompt processing
1,687 tok/s
First token (1K prompt)
0.6 s
Largest safe context
256K

Download Qwen3-Next 80B-A3B Thinking

Quantized files · GGUF

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

huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking-GGUF (opens in a new tab)

ollama run hf.co/Qwen/Qwen3-Next-80B-A3B-Thinking-GGUF

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking (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.32.5 / 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.10.8 / 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
Total83.3 / 10083 ptsGreat (A) · 83 of 100 points: Model quality 32.5/45, Memory fit 25.0/25, Speed 10.8/15, 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 Intel Core Ultra 200H (Arc 140T) (96 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 37.7 GB 54.7 GB 26.3 tok/s Runs well 79 ptsGreat (A) · 79 of 100 points: Model quality 27.1/45, Memory fit 25.0/25, Speed 12.2/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q3_K_M 45.0 GB 54.7 GB 20.7 tok/s Runs well 83 ptsGreat (A) · 83 of 100 points: Model quality 32.5/45, Memory fit 25.0/25, Speed 10.8/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q4_K_M 54.8 GB 54.7 GB 13.6 tok/s Runs with offload 70 ptsGood (B) · 70 of 100 points: Model quality 36.8/45, Memory fit 10.0/25, Speed 8.7/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 62.9 GB 54.7 GB 11.2 tok/s Heavy offload 64 ptsGood (B) · 64 of 100 points: Model quality 37.5/45, Memory fit 4.0/25, Speed 8.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 71.9 GB 54.7 GB — Won't run 11 ptsWon't run (F) · 11 of 100 points: Model quality 11.5/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (opens in a new tab)
Q8_0 91.4 GB 54.7 GB — Won't run 11 ptsWon't run (F) · 11 of 100 points: Model quality 11.6/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (opens in a new tab)
F16 168 GB 54.7 GB — Won't run 11 ptsWon't run (F) · 11 of 100 points: Model quality 11.6/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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.