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Can the Intel Core Ultra 200V (Arc 140V) (16 GB) run TeleOCR?

40Fair quality (C) · 40 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q8_0 it needs about 3.8 GB of 9.1 GB usable, with an estimated speed of 45.9 tok/s.

Best quant
Q8_0
Generation speed
45.9 tok/s
Prompt processing
3,099 tok/s
First token (1K prompt)
0.3 s
Largest safe context
53K

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

On Windows

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Handy if you already work in Docker.
docker model run hf.co/XingChen-AGI/TeleOCR

On Linux

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Handy if you already work in Docker.
docker model run hf.co/XingChen-AGI/TeleOCR

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 TeleOCR

Original weights · Hugging Face

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

huggingface.co/XingChen-AGI/TeleOCR (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from XingChen-AGI. The weights themselves download from Hugging Face.

github.com/caipeng328/TeleOCR (opens in a new tab)

Official code for full document parsing; the model card points here. Formerly named NaviDC-OCR.

git clone https://github.com/caipeng328/TeleOCR

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 40Fair quality (C) · 40 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q8_0, 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 great
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.18.0 / 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.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.9.6 / 15
Match score67.6 / 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 2.8 GB 9.1 GB 148 tok/s Runs great 28Basic quality (D) · 28 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 3.0 GB 9.1 GB 117 tok/s Runs great 33Basic quality (D) · 33 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q4_K_M 3.1 GB 9.1 GB 90.1 tok/s Runs great 38Basic quality (D) · 38 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q5_K_M 3.3 GB 9.1 GB 74.5 tok/s Runs great 38Basic quality (D) · 38 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 3.4 GB 9.1 GB 62.5 tok/s Runs great 39Basic quality (D) · 39 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 3.8 GB 9.1 GB 45.9 tok/s Runs great 40Fair quality (C) · 40 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
F16 5.1 GB 9.1 GB 21.1 tok/s Runs well 40Fair quality (C) · 40 of 100: how capable the model is at F16, whatever it runs on. Match score 66/100 (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 TeleOCR need on the Intel Core Ultra 200V (Arc 140V) 16GB?

At Q8_0 it needs about 3.8 GB. The Intel Core Ultra 200V (Arc 140V) 16GB has 9.1 GB usable.

How fast is TeleOCR on the Intel Core Ultra 200V (Arc 140V) 16GB?

About 45.9 tok/s at Q8_0, an estimate from the hardware’s memory bandwidth and the size of the model.

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

Q8_0. 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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