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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.
9 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.
MagpieTTS 357M: 12-language speech synthesis that runs on a laptop
NVIDIA releases MagpieTTS Multilingual, 364M parameters and 12 languages in one model: local text-to-speech with a 332 MB GGUF file, no GPU. The C++ runtime NeMo-Speech.cpp takes you from zero to first audio in three commands; the five voices are the same in every language, and for a natively Italian voice Piper is still ahead.
Mistral vs Gemma: Light Models Compared
Full comparison of the Mistral and Gemma open model families: sizes, required VRAM, context, supported languages and licenses, with a practical method for picking the right model for your hardware.
RAG: ask questions to your own documents
RAG (Retrieval Augmented Generation) is the simplest way to make a local model answer questions about your own documents: embeddings, vector database and retrieval loop.
Local AI for your business: 5 practical use cases
Five practical use cases of local AI for businesses: customer support, documents, internal search, CRM and generation — with the tools, minimum hardware and a 30-minute starting plan.
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.
What is local AI and why you need it
A practical explanation of local AI: running a model on your own hardware instead of the cloud. Differences from cloud AI, the concrete benefits (availability, privacy, predictable cost, full control), honest limits, and a five-minute start with Ollama.
Privacy and GDPR: What Local AI Changes
Extra-EU transfers, sub-processors and Arts. 5, 25, 28, 32, 44: what the GDPR really says about cloud vs local, and what is still owed even with in-house AI.
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.