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Can the Intel Core Ultra 200H (Arc 140T) (32 GB) run Qwen1.5 14B Chat?

23Basic quality (D) · 23 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well

Runs well. At Q2_K it needs about 14.4 GB of 18.2 GB usable, with an estimated speed of 11.1 tok/s.

Best quant
Q2_K
Generation speed
11.1 tok/s
Prompt processing
357 tok/s
First token (1K prompt)
2.9 s
Largest safe context
12K

Best app to run it on the Intel Core Ultra 200H (Arc 140T) (32 GB)

On Windows

llama.cpp

Command line + local API · runs on llama.cpp

  • Supports Intel Arc through SYCL and Vulkan.
  • Usually the first to support new model architectures.
llama-server -hf Qwen/Qwen1.5-14B-Chat-GGUF:Q2_K
  • LM Studio Point-and-click: search, download and chat in one window.
  • Ollama Intel GPU support here is experimental (Vulkan).

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

  • Supports Intel Arc through SYCL and Vulkan.
  • Usually the first to support new model architectures.
llama-server -hf Qwen/Qwen1.5-14B-Chat-GGUF:Q2_K
  • LM Studio Point-and-click: search, download and chat in one window.
  • Ollama Intel GPU support here is experimental (Vulkan).

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Qwen1.5 14B Chat

Recommended for the Intel Core Ultra 200H (Arc 140T) (32 GB): Q2_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.

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

ollama run qwen:14b-chat-v1.5-q2_K

Quantized files · GGUF

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

Download the Q2_K file (opens in a new tab)

huggingface.co/Qwen/Qwen1.5-14B-Chat-GGUF (opens in a new tab)

ollama run hf.co/Qwen/Qwen1.5-14B-Chat-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen1.5-14B-Chat (opens in a new tab)

No sign-in needed.

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 23Basic quality (D) · 23 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q2_K, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs well
How well it runs on the Intel Core Ultra 200H (Arc 140T) (32 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.10.7 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.7.9 / 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
Match score58.6 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the Intel Core Ultra 200H (Arc 140T) (32 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 14.4 GB 18.2 GB 11.1 tok/s Runs well 23Basic quality (D) · 23 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 15.7 GB 18.2 GB 8.8 tok/s Slowtight fit 28Basic quality (D) · 28 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 17.4 GB 18.2 GB 6.8 tok/s Slowtight fit 32Basic quality (D) · 32 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 18.8 GB 18.2 GB 4.8 tok/s Slowover GPU memory limit 32Basic quality (D) · 32 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 20.4 GB 18.2 GB 4.0 tok/s Slowfar over GPU memory limit 33Basic quality (D) · 33 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 38/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 23.8 GB 18.2 GB — Won't run 34Basic quality (D) · 34 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 4/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 37.1 GB 18.2 GB — Won't run 34Basic quality (D) · 34 of 100: how capable the model is at F16, whatever it runs on. Match score 4/100 (quality, fit, speed and memory use) sets the ranking. 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

Quick answers

How much memory does Qwen1.5 14B Chat need on the Intel Core Ultra 200H (Arc 140T) 32GB?

At Q2_K it needs about 14.4 GB. The Intel Core Ultra 200H (Arc 140T) 32GB has 18.2 GB usable.

How fast is Qwen1.5 14B Chat on the Intel Core Ultra 200H (Arc 140T) 32GB?

About 11.1 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Qwen1.5 14B Chat should I use on the Intel Core Ultra 200H (Arc 140T) 32GB?

Q2_K. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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.

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