OLLAMA

A fair, sourced comparison of the four runtimes developers reach for when they want a coding agent talking to a model on their own hardware instead of an API: Ollama's convenience, LM Studio's GUI, vLLM's throughput, and llama.cpp's control. What each is actually for, and which to pick.

Install Ollama and LM Studio, pull your first model, and run AI locally for coding, chat, and automation - with zero cloud dependency.

Microsoft's PHI-4 is an MIT-licensed 14 billion parameter model that matches Llama 3.3 70B and Qwen 2.5 72B on key benchmarks. Here is what makes it special, how to run it locally, and why small language models are increasingly practical for real development work.

Meta surprised the AI community with Llama 3.3, a 70 billion parameter model that delivers 405B-class performance at a fraction of the cost. Here is what the benchmarks show, where to run it, and why this release matters for developers building with open-source models.