VRAM is the bottleneck for everything in AI but GPU manufacturers refuse to put more than 24GB on consumer cards

Running Llama 3 70B at Q4 quantization needs 38GB VRAM. The best consumer GPU has 24GB. Running Stable Diffusion XL with LoRA training needs 16-24GB — barely fits on a 4090, impossible on a 4070 (12GB). Running a RAG pipeline with a local embedding model + LLM simultaneously needs 20-30GB. Every interesting local AI workload is VRAM-limited. The GPU's compute cores sit 30-50% idle because there is not enough VRAM to feed them data. So what? VRAM is the single most important spec for AI workloads, but GPU manufacturers price and allocate it to maximize market segmentation, not user value. A 4090 has 24GB because putting 48GB would cannibalize $6,000 A6000 sales. Apple Silicon has unified memory (up to 192GB on M2 Ultra) but memory bandwidth is 5-10x slower than GDDR6X, making inference 5-10x slower. The result: there is no consumer-priced (<$2,000) GPU with enough VRAM (48GB+) for serious local AI work. You either buy a $6,000 professional card, rent cloud GPUs, or use underpowered quantized models. Why does this persist? GDDR6X memory costs ~$8-10 per GB at volume. Putting 48GB instead of 24GB on a 4090 would add $200 in BOM cost to a $1,600 card — a 12% cost increase. NVIDIA does not do this because a 48GB consumer card would eliminate the reason to buy a $6,000 professional card. The VRAM limit is a business decision, not a technical constraint.