Comment on China's first real gaming GPU is here, and the benchmarks are brutal
CheeseNoodle@lemmy.world 3 days agoThe crazy part is outside LLMs the other (actually useful) AI does not need that much processing power, more than you or I use sure but notthing that would have justified gigantic data centers.
DacoTaco@lemmy.world 2 days ago
Debatable. The basics of an llm might not need much, but the actual models do need it to be anywhere near decent or usefull. Im talking minutes for a simple reply.
Source: ran few <=5b models on my system with ollama yesterday and gave it access to a mcp server to do stuff with
CheeseNoodle@lemmy.world 2 days ago
Yes, my whole post was that non-LLMs take far less processing power.
DacoTaco@lemmy.world 2 days ago
Oh derp, misread sorry! Now im curious though, what ai alternatives are there that are decent in processing/using a neural network?
CheeseNoodle@lemmy.world 2 days ago
So the two biggest examples I am currently aware of are googles AI for unfolding proteins and a startup using one to optimize rocket engine geometry but AI models in general can be highly efficient when focussed on niche tasks. As far as I understand it they’re still very similar in underlying function to LLMs but the approach is far less scattershot which makes them exponentially more efficient.
A good way to think of it is even the earliest versions of chat GPT or the simplest local models are all equally good at actually talking but language has a ton of secondary requirements like understanding context and remembering things and the fact that not every gramatically valid bannana is always a useful one. So an LLM has to actually be a TON of things at once while an AI designed for a specific technical task only has to be good at that one thing.