https://github.com/zai-org/GLM-OCR

Use mlx-vlm for inference:

https://github.com/zai-org/GLM-OCR/blob/main/examples/mlx-de...

Then you can run a single command to process your PDF:

  glmocr parse example.pdf

  Loading images: example.pdf
  Found 1 file(s)
  Starting Pipeline...
  Pipeline started!
  GLM-OCR initialized in self-hosted mode
  Using Pipeline (enable_layout=true)...

  === Parsing: example.pdf (1/1) ===

After it's done, you'll have a directory output/example/ that contains .md and .json files. The .md file will contain a markdown rendition of the complete document. The .json file will contain individual labeled regions from the document along with their transcriptions. If you get all the JSON objects with

  "label": "table"

It might still be inaccurate -- I don't know how challenging your original tables are -- but it shouldn't be terribly slow. The tables it produced for me were good.

I have also built more complex work flows that use a mixture of OCR-specialized models and general purpose VLM models like Qwen 3.5, along with software to coordinate and reconcile operations, but GLM-OCR by itself is the best first thing to try locally.

2. The n8n workflow passes a given binary pdf to gemma, which (based on a detailed prompt) analyzes it and produces JSON output.

See https://github.com/LinkedInLearning/build-with-ai-running-lo... if you want more details. :)

https://unsloth.ai/docs/basics/unsloth-dynamic-2.0-ggufs

For the best quality reply, I used the Gemma-4 31B UD-Q8_K_XL quant with Unsloth Studio to summarize the URL with web search. It produced 4.9 tok/s (including web search) on an MacBook Pro M1 Max with 64GB.

Here an excerpt of it's own words:

Unsloth Dynamic 2.0 Quantization

Dynamic 2.0 is not just a "bit-reduction" but an intelligent, per-layer optimization strategy.

- Selective Layer Quantization: Instead of making every layer 4-bit, Dynamic 2.0 analyzes every single layer and selectively adjusts the quantization type. Some critical layers may be kept at higher precision, while less critical layers are compressed more.

- Model-Specific Tailoring: The quantization scheme is custom-built for each model. For example, the layers selected for quantization in Gemma 3 are completely different from those in Llama 4.

- High-Quality Calibration: They use a hand-curated calibration dataset of >1.5M tokens specifically designed to enhance conversational chat performance, rather than just optimizing for Wikipedia-style text.

- Architecture Agnostic: While previous versions were mostly effective for MoE (Mixture of Experts) models, Dynamic 2.0 works for all architectures (both MoE and non-MoE).