LLaVA 1.5 13B on Intel Core Ultra 200V (Arc 140V) (16 GB)

Can the Intel Core Ultra 200V (Arc 140V) (16 GB) run LLaVA 1.5 13B?

Yes, it runs well (over GPU memory limit)

Runs well (over GPU memory limit). At Q2_K it needs about 10.0 GB of 9.1 GB usable, with an estimated speed of 13.4 tok/s.

Best quant: Q2_K
Generation speed: 13.4 tok/s
Prompt processing: 330 tok/s
First token (1K prompt): 3.1 s
Largest safe context: 2K

Best app to run it on the Intel Core Ultra 200V (Arc 140V) (16 GB)

On Windows

Ollama

Command line + local API · runs on llama.cpp (ggml), MLX (Apple silicon, preview)

Splits the model between GPU memory and system RAM, which this setup needs.
Intel GPU support here is experimental (Vulkan).
One command with Ollama’s own build of this model.

ollama run llava:13b-v1.5-q2_K

Docker Model Runner Splits the model between GPU memory and system RAM, which this setup needs.
Text Generation WebUI Splits the model between GPU memory and system RAM, which this setup needs.

On Linux

Ollama

Command line + local API · runs on llama.cpp (ggml), MLX (Apple silicon, preview)

Splits the model between GPU memory and system RAM, which this setup needs.
Intel GPU support here is experimental (Vulkan).
One command with Ollama’s own build of this model.

ollama run llava:13b-v1.5-q2_K

Docker Model Runner Splits the model between GPU memory and system RAM, which this setup needs.
Text Generation WebUI Splits the model between GPU memory and system RAM, which this setup needs.

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 LLaVA 1.5 13B

Recommended for the Intel Core Ultra 200V (Arc 140V) (16 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 llava:13b-v1.5-q2_K

Original weights · Hugging Face

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

huggingface.co/liuhaotian/llava-v1.5-13b (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 15Basic quality (D) · 15 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 40/100 (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 wellover GPU memory limit: How well it runs on the Intel Core Ultra 200V (Arc 140V) (16 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.	6.9 / 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.	10.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	8.7 / 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 score	40.6 / 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 200V (Arc 140V) (16 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	10.0 GB	9.1 GB	13.4 tok/s	Runs wellover GPU memory limit	15Basic quality (D) · 15 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 40/100 (quality, fit, speed and memory use) sets the ranking.
`Q3_K_M`	11.2 GB	9.1 GB	10.5 tok/s	Slowfar over GPU memory limit	18Basic quality (D) · 18 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 35/100 (quality, fit, speed and memory use) sets the ranking.
`Q4_K_M`	12.8 GB	9.1 GB	—	Won't run	20Basic quality (D) · 20 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 2/100 (quality, fit, speed and memory use) sets the ranking.
`Q5_K_M`	14.2 GB	9.1 GB	—	Won't run	21Basic quality (D) · 21 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 2/100 (quality, fit, speed and memory use) sets the ranking.
`Q6_K`	15.6 GB	9.1 GB	—	Won't run	21Basic quality (D) · 21 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 2/100 (quality, fit, speed and memory use) sets the ranking.
`Q8_0`	18.8 GB	9.1 GB	—	Won't run	22Basic quality (D) · 22 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 2/100 (quality, fit, speed and memory use) sets the ranking.
`F16`	31.4 GB	9.1 GB	—	Won't run	22Basic quality (D) · 22 of 100: how capable the model is at F16, whatever it runs on. Match score 2/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Memory figures assume 4K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

22Basic quality (D) · 22 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 Q8_0 · 43.8 tok/s · Runs great
22Basic quality (D) · 22 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 Q8_0 · 47.2 tok/s · Runs great
21Basic quality (D) · 21 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX Q6_K · 52.7 tok/s · Runs great
20Basic quality (D) · 20 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT Q4_K_M · 50.7 tok/s · Runs great
20Basic quality (D) · 20 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPER Q4_K_M · 61.8 tok/s · Runs great
20Basic quality (D) · 20 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPER Q4_K_M · 67.7 tok/s · Runs great

Quick answers

How much memory does LLaVA 1.5 13B need on the Intel Core Ultra 200V (Arc 140V) 16GB?

At Q2_K it needs about 10.0 GB. The Intel Core Ultra 200V (Arc 140V) 16GB has 9.1 GB usable.

How fast is LLaVA 1.5 13B on the Intel Core Ultra 200V (Arc 140V) 16GB?

About 13.4 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of LLaVA 1.5 13B should I use on the Intel Core Ultra 200V (Arc 140V) 16GB?

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.