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Can the Intel Arc A770 16GB run Chandra OCR 2?

48Fair quality (C) · 48 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q8_0 it needs about 7.3 GB of 16.0 GB usable, with an estimated speed of 50.4 tok/s.

Best quant
Q8_0
Generation speed
50.4 tok/s
Prompt processing
4,078 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Best app to run it on the Intel Arc A770 16GB

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 mradermacher/chandra-ocr-2-GGUF:Q8_0
  • LM Studio Point-and-click: search, download and chat in one window.
  • Jan Point-and-click: search, download and chat in one window.

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 mradermacher/chandra-ocr-2-GGUF:Q8_0
  • LM Studio Point-and-click: search, download and chat in one window.
  • Jan Point-and-click: search, download and chat in one window.

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 Chandra OCR 2

Recommended for the Intel Arc A770 16GB: Q8_0. Every other quantization has its own download link in the table below.

Quantized files · GGUF

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

Download the Q8_0 file (opens in a new tab)

huggingface.co/mradermacher/chandra-ocr-2-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/chandra-ocr-2-GGUF:Q8_0

Original weights · Hugging Face

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

huggingface.co/datalab-to/chandra-ocr-2 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/datalab-to/chandra (opens in a new tab)

git clone https://github.com/datalab-to/chandra

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 48Fair quality (C) · 48 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 72/100 (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 Arc A770 16GB: 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.21.6 / 45
Memory fitFull marks when everything sits in GPU 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.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.10.5 / 15
Match score72.1 / 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 Arc A770 16GB

Quant Memory needed Available Speed Runs Quality Download
Q2_K 3.8 GB 16.0 GB 163 tok/s Runs great 33Basic quality (D) · 33 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 4.3 GB 16.0 GB 128 tok/s Runs great 40Fair quality (C) · 40 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 4.9 GB 16.0 GB 99.1 tok/s Runs great 45Fair quality (C) · 45 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 5.4 GB 16.0 GB 81.8 tok/s Runs great 46Fair quality (C) · 46 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 6.0 GB 16.0 GB 68.7 tok/s Runs great 47Fair quality (C) · 47 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 7.3 GB 16.0 GB 50.4 tok/s Runs great 48Fair quality (C) · 48 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 12.3 GB 16.0 GB 23.2 tok/s Runs well 48Fair quality (C) · 48 of 100: how capable the model is at F16, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. 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

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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