Developers struggle to find cost-effective hardware configurations for running local LLMs without breaking the bank

Software developers and AI enthusiasts need to run large language models locally for privacy, latency, and cost reasons, but lack clear guidance on which hardware combinations deliver the best performance-per-dollar. Current solutions require either expensive GPU setups, trial-and-error experimentation across incompatible hardware, or settling for slow CPU-only inference. Developers waste weeks researching and testing different configurations instead of shipping products.