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Articles, guides, and insights on local artificial intelligence
6 articles found per "rag"
Yzma: embeddings up to 2.6x faster than Ollama, the comparison
Yzma calls llama.cpp in the same process: no server, no round trip. It is 2.6x faster than Ollama on embeddings — with the numbers, the math and the setup.
Local text-to-SQL: query your business database without sending data to the cloud
A local model, the schema to the model, execution on your database: text-to-SQL in-house, ARCS benchmark numbers, and where it really breaks.
gpt-oss: OpenAI's open reasoning model
21B total parameters but only 3.6B active, native MXFP4 weights and 128K of context: OpenAI's first open reasoning model fits in 16GB. What that means in practice.
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.
Open WebUI: the interface for your local AI
Open WebUI is the full web interface for your local models: Docker install, connecting Ollama, per-model customization, document RAG, and its honest limits.