Qwen3.8 27B on Intel Core Ultra 200H (Arc 140T) (32 GB)

Can the Intel Core Ultra 200H (Arc 140T) (32 GB) run Qwen3.8 27B?

80 pts

Yes, with little memory to spare

Tight fit. At Q3_K_M it needs about 16.2 GB of 18.2 GB usable, with an estimated speed of 4.5 tok/s.

Best quant: Q3_K_M
Generation speed: 4.5 tok/s
Prompt processing: 182 tok/s
First token (1K prompt): 5.6 s
Largest safe context: 38K

Download Qwen3.8 27B

Recommended for the Intel Core Ultra 200H (Arc 140T) (32 GB): Q3_K_M. 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 qwen3.8:27b

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 Q3_K_M (UD-Q3_K_XL) file (opens in a new tab)

huggingface.co/unsloth/Qwen3.8-27B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Qwen3.8-27B-GGUF:UD-Q3_K_XL

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3.8-27B (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 5 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.	36.1 / 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.	5.0 / 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
Total	80.2 / 100	80 ptsGreat (A) · 80 of 100 points: Model quality 36.1/45, Memory fit 24.0/25, Speed 5.0/15, 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 Intel Core Ultra 200H (Arc 140T) (32 GB)

Quant	Memory needed	Available	Speed	Fit	Grade · points	Download
`Q2_K`	13.7 GB	18.2 GB	5.7 tok/s	Runs well	75 ptsGreat (A) · 75 of 100 points: Model quality 30.1/45, Memory fit 25.0/25, Speed 5.7/15, Memory use 15.0/15	GGUF (UD-Q2_K_XL) (opens in a new tab)
`Q3_K_M`	16.2 GB	18.2 GB	4.5 tok/s	Tight fit	80 ptsGreat (A) · 80 of 100 points: Model quality 36.1/45, Memory fit 24.0/25, Speed 5.0/15, Memory use 15.0/15	GGUF (UD-Q3_K_XL) (opens in a new tab)
`Q4_K_M`	19.5 GB	18.2 GB	2.9 tok/s	Runs with offload	69 ptsGood (B) · 69 of 100 points: Model quality 40.9/45, Memory fit 10.0/25, Speed 4.1/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q5_K_M`	22.3 GB	18.2 GB	2.4 tok/s	Heavy offload	64 ptsGood (B) · 64 of 100 points: Model quality 41.7/45, Memory fit 4.0/25, Speed 3.7/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q6_K`	25.4 GB	18.2 GB	—	Won't run	12 ptsWon't run (F) · 12 of 100 points: Model quality 12.8/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF (opens in a new tab)
`Q8_0`	32.0 GB	18.2 GB	—	Won't run	12 ptsWon't run (F) · 12 of 100 points: Model quality 12.9/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF (opens in a new tab)
`F16`	58.2 GB	18.2 GB	—	Won't run	12 ptsWon't run (F) · 12 of 100 points: Model quality 12.9/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (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

97 ptsExcellent (S) · 97 of 100 points: Model quality 42.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5090 Q6_K · 54.9 tok/s
95 ptsExcellent (S) · 95 of 100 points: Model quality 40.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 3090 Q4_K_M · 41.4 tok/s
95 ptsExcellent (S) · 95 of 100 points: Model quality 40.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4090 Q4_K_M · 44.5 tok/s
95 ptsExcellent (S) · 95 of 100 points: Model quality 40.9/45, Memory fit 25.0/25, Speed 14.3/15, Memory use 15.0/15 AMD Radeon RX 7900 XTX Q4_K_M · 36.5 tok/s
91 ptsExcellent (S) · 91 of 100 points: Model quality 43.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.5/15 NVIDIA RTX PRO 6000 Blackwell Q8_0 · 40.3 tok/s
90 ptsExcellent (S) · 90 of 100 points: Model quality 36.1/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4080 SUPER Q3_K_M · 42.0 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.