Show HN: LocalCoder – Tell it your hardware, get the exact local AI model to run (localcoder.xyz)

LocalCoder has launched a tool that allows users to specify their hardware configuration to identify the optimal local AI model to deploy. This innovation is particularly significant for the AI/ML community as it streamlines the decision-making process regarding model selection, ensuring that users can efficiently operate high-quality AI models on their available hardware without extensive technical expertise. The tool supports quantized models, with different quality and speed trade-offs depending on the quantization level, from Q2 to Q8. Additionally, it offers a comparison between Ollama, which simplifies the installation and running of models, and llama.cpp, which allows for advanced performance tuning by controlling VRAM, threads, and context size. Notably, LocalCoder utilizes a Mixture of Experts (MoE) approach with its Qwen3-Coder model, enabling it to deliver top-tier output by activating only a fraction of its 480 billion parameters at any time. This efficient mechanism not only conserves memory but also enhances processing speeds, ultimately boosting user productivity, especially when integrated with popular development environments like VS Code and Cursor.