You've probably read that you need a GPU with tons of VRAM to run local models. That's true, but only half the story. Memory bandwidth is what actually controls whether your token generation feels snappy or gets bottlenecked to a crawl. Here's the problem: running a 7B model doesn't need that much computation. The GPU sits around doing almost nothing while it waits for data from VRAM. Think of it like a chef with a slow kitchen window - no amount of skill helps if the ingredients show up one at a time. Your GPU is the chef, and memory bandwidth is the window. The difference shows up fast. An RTX 4090 with 1TB/s bandwidth generates tokens roughly twice as fast as an A100 80GB with identical compute specs, purely because of bandwidth. The 4090 pushes data faster, so the GPU stays busy. Most consumer GPUs max out around 500-700 GB/s, while datacenter cards hit 1000+ TB/s. This is why people see such huge differences in inference speed between cards that look equivalent on paper.…