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Articles, guides, and insights on local artificial intelligence
Reading paths
Start here
The in-order path for newcomers to local AI: from basic concepts to installation and your first model.
Optimize performance
Pick the right hardware and get the most out of what you already own: CPU vs GPU, Mac, Raspberry Pi, and fast serving.
Data and privacy
What actually goes over the network with local AI: the data, the security, the compliance, and the control that stays in your hands.
Choosing hardware and model
How to pick the right hardware and the model suited to your use: VRAM, quantization, GPU or CPU and response speed.
3 articles
Nemotron-Parse: NVIDIA's OCR family for reading documents locally
Nemotron Parse is NVIDIA's vision encoder-decoder family that reads PDFs locally: formatted markdown, LaTeX formulas, coordinates and a semantic class for every element, in reading order. v1.1 (Nov 2025), v1.2 (Feb 2026) and 2.0 (Jun 2026), all under 1 billion parameters and re-released on October 20; 2.0 reaches 0.72 ANLS on Indic scripts. Locally with vLLM v0.20 through v0.26: your documents stay on your hardware.
Qwen: the open model with the best multilingual support
Qwen is the open family with the best support for Italian and French: generations, sizes, MoE, when to use it, and a quick comparison with Llama.
Llama 3 and Llama 4: Meta's open family — which model runs on your hardware
Llama 3 and 4 in a table: 3B/8B/70B and Scout MoE 109B, RAM required per model, the Community license and the choice by budget.