Gemma 4 for Structured Data Extraction: Can It Beat Qwen 3.5?

In this video, I put Gemma 4 to the test on a real-world task — extracting structured data from bank statements — and benchmark it head-to-head against Mistral's Ministral and Qwen 3.5.

I run both the MoE and Dense variants of Gemma 4 to see how architecture affects accuracy on financial documents, then compare the results side-by-side.

My takeaway: Gemma 4 holds its own and performs on par with Qwen 3.5 — a strong result for local structured extraction workflows.

 

Running Multiple Models on One GPU with vLLM and GPU Memory Utilization

In this video I show how to run multiple vLLM model instances on the same GPU (Nvidia) in parallel by adjusting the --gpu-memory-utilization flag.

You'll see: 

- How to launch separate vLLM servers for different models 

- How to split GPU memory between them without running out of VRAM

This approach works when you want to serve several smaller models concurrently on limited hardware.

 

How to Cache vLLM Model in FastAPI for Faster Inference

I show you how to keep your vLLM model loaded in FastAPI cache for much faster inference — without reloading it on every request. 

 

Qwen 3.5 Test for JSON Structured Data Extraction

Quick test of the new Qwen 3.5 models on JSON structured data extraction from images. Testing and comparing results for 9B FP16, 27B Q8, and A3B 35B Q8. The 35B Q8 model wins in terms of both speed and accuracy. Test was run on MLX-VLM using a Mac Mini M4 Pro with 64GB RAM

 

Fast Large Table Extraction: Sparrow + dots.ocr to JSON

Sparrow provides table processing mode. It is optimized to handle large tables, it comes with separate template script (new templates can be easily added) to process dots.ocr markdown output into structure JSON with field mapping.

 

Local OCR Comparison: dots.ocr More Accurate, DeepSeek-OCR 2 Faster (Sparrow + MLX)

I run local tests with Sparrow to compare DeepSeek OCR2 and dots.ocr (by RedNote), both run on MLX-VLM in FP16 precision. Dots.ocr consistently beats DeepSeek OCR2 in accuracy, but DeepSeek OCR2 deliveres much better inference performance.

 

GLM-OCR vs DeepSeek OCR 2: Which One Wins at Markdown Extraction?

I compare two OCR models using real test cases: GLM OCR and DeepSeek OCR2. Both are evaluated on their ability to extract document content and convert it into well-structured Markdown. I demonstrate which model performs better and which one is faster.