RTX 2000 Ada Generation

Speed is modeled, not measured: decode is memory-bandwidth bound, so tokens per second is bytes read per token against achievable bandwidth. Mixture-of-experts models carry a wider band because only the routed experts are read each step, and few have been measured publicly.

Aggregated from community-submitted runs, so the spread is wide by nature — it covers different models, resolutions, step counts and settings, not one controlled configuration. Read the middle 50% rather than the median alone. These figures are reproduced with attribution from llama.cpp-discussion-15013.

What AI models can a RTX 2000 Ada Generation run?

1813 of 2118 indexed open-weight models fit a RTX 2000 Ada Generation at 32,768 context with q8_0 KV cache, the largest being ERNIE-4.5-21B-A3B-Thinking at Q4_1. That covers text, vision-language, image, video and speech models.

How much usable memory does a RTX 2000 Ada Generation actually have?

Its nameplate is 16 GB, but about 14.88 GiB is available to a model once driver and compositor overhead is accounted for.

Is a RTX 2000 Ada Generation fast for local AI?

Its memory bandwidth is 224 GB/s, and that figure — not teraflops — is what governs token generation speed. Capacity decides what you can run; bandwidth decides how fast it runs.