Qwen3.5 9B on Intel Core Ultra 200H (Arc 140T) (32 GB)

Can the Intel Core Ultra 200H (Arc 140T) (32 GB) run Qwen3.5 9B?

78 pts

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 Qwen3.5 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.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run qwen3.5:9b

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

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/unsloth/Qwen3.5-9B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Qwen3.5-9B-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3.5-9B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Alibaba. The weights themselves download from Hugging Face.

github.com/QwenLM/Qwen3.8 (opens in a new tab)

git clone https://github.com/QwenLM/Qwen3.8

Qwen’s current repo: Qwen3.5 and Qwen3.6 redirect here.

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.	33.9 / 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
Total	78.0 / 100	78 ptsGreat (A) · 78 of 100 points: Model quality 33.9/45, 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 23.9/45, Memory fit 25.0/25, Speed 9.6/15, Memory use 7.8/15	GGUF (UD-Q2_K_XL) (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 28.7/45, Memory fit 25.0/25, Speed 8.5/15, Memory use 8.9/15	GGUF (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 32.5/45, 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	76 ptsGreat (A) · 76 of 100 points: Model quality 33.2/45, 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 33.9/45, 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 34.2/45, 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 34.2/45, 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

89 ptsExcellent (S) · 89 of 100 points: Model quality 34.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5090 F16 · 53.5 tok/s
89 ptsExcellent (S) · 89 of 100 points: Model quality 34.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4070 Ti SUPER Q8_0 · 43.5 tok/s
89 ptsExcellent (S) · 89 of 100 points: Model quality 34.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4080 SUPER Q8_0 · 47.7 tok/s
89 ptsExcellent (S) · 89 of 100 points: Model quality 34.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5070 Ti Q8_0 · 58.0 tok/s
89 ptsExcellent (S) · 89 of 100 points: Model quality 34.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5080 Q8_0 · 62.2 tok/s
88 ptsExcellent (S) · 88 of 100 points: Model quality 33.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4070 SUPER Q6_K · 44.5 tok/s

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.