Running a useful local LLM requires 40GB+ VRAM but consumer GPUs max at 24GB

A 70B parameter model — the minimum size that can follow complex multi-step instructions reliably — requires ~40GB of VRAM at Q4 quantization. The best consumer GPU (RTX 4090) has 24GB. So you are forced to either: (a) run a 13B model that is too dumb for real agent tasks, (b) buy two GPUs and deal with tensor parallelism setup that barely works, or (c) use Apple Silicon unified memory which loads the model but runs inference at 5 tokens/second — making a 50-call agent loop take 30+ minutes. So what? There is a hardware dead zone: the models worth running locally do not fit on hardware normal people own, and the models that fit are not worth running. This kills the entire local-first AI agent market. Everyone who cares about privacy (healthcare, legal, finance) or wants to avoid per-token costs is told to run local — but running local is either useless (small model) or painfully slow (offloading to RAM/Apple Silicon). Why does this persist? NVIDIA has no incentive to ship more VRAM on consumer cards — they want you to buy $10K+ A100/H100 datacenter GPUs. Apple Silicon has the memory but the memory bandwidth bottleneck (200 GB/s vs 3 TB/s on H100) makes inference 15x slower. AMD GPUs have the VRAM (RX 7900 XTX has 24GB) but ROCm software support is so broken that most inference engines do not work on AMD at all.