Local AI refers to artificial intelligence systems that run directly on personal computers, workstations, local servers, or private infrastructure rather than relying entirely on cloud-based services. This approach is especially relevant for privacy, data control, reproducibility, cost management, and experimentation with open-source models. In healthcare, research, education, and software development, Local AI can support workflows such as document analysis, coding assistance, clinical text processing, knowledge retrieval, and agent-based applications while keeping sensitive data under closer user control. This tag collects articles on local language models, AI infrastructure, private inference, open-source tools, and practical workflows for building AI systems outside the cloud.
Part 4: the conclusion of the “Local AI” series — The LM Studio surprise: same model, same hardware, 40-200x faster In Part 3, we pushed gemma4:e4b through four demanding tests. The results were impressive — excellent clinical reasoning, comprehensive summaries, working code. But the wait times were brutal: 5 to 16 minutes per response on … Read more
Part 3 of the “Local AI” series — Coding, reasoning, medical summarization, and clinical decision support Benchmarks tell you how fast a model runs. They don’t tell you if it can actually do anything useful. In Part 1 we set up Ollama, and in Part 2 we measured generation speeds. Now we push these models … Read more
Part 2 of the “Local AI” series — Benchmarks and recommendations for CPU-only inference In Part 1, we installed Ollama and ran our first local model. Now the question becomes: which model should you actually use? The answer depends on your hardware and your patience. On CPU-only systems, model size directly impacts speed — and … Read more
Part 1 of the “Local AI” series — A practical guide to running local LLMs on CPU-only consumer hardware You’ve probably heard that running AI models locally requires expensive hardware: a Mac with Apple Silicon, or a gaming PC with a high-end NVIDIA GPU. This isn’t entirely wrong — those setups are faster. But they’re … Read more