Local LLMs are how nerds now justify a big computer they don't need (world.hey.com)

The piece argues that while it’s now technically feasible to run many smaller LLMs locally—examples include downscaled DeepSeek variants and gpt-oss-20b—those models remain well behind the frontier “rentable” models in capability. That gap means local LLMs are primarily a curiosity or hobbyist plaything for most developers, not a production-ready replacement. Buying a workstation or stuffing a desktop with 128 GB+ of VRAM to run local models is often unnecessary because you’ll still resort to cloud APIs for the bulk of real work where accuracy, robustness, and up-to-date weights matter. Technically, this matters because it reframes developer hardware choices and resource allocation: smaller models are improving and useful for offline tasks, privacy-sensitive experiments, latency-sensitive edge use cases, or learning, but they don’t yet justify the cost and power of top-tier GPUs for everyday software development. With RAM and GPU prices rising, the pragmatic takeaway is many developers can get by with modest Linux-friendly mini PCs or cheaper desktops for day-to-day work and leave heavy lifting to rentable models. The ecosystem will keep advancing, but today local LLMs are best treated as complementary tools, not a reason to overbuy hardware.