Large language models seem to benefit from ever-increasing parameter counts and data volumes, but is the scaling law sustainable forever? As we push the limits of memory, compute, and energy consumption, what approaches might rise to prominence—such as modular systems, in-context learning, or multimodal integration? Are there other directions beyond sheer parameter growth that could meaningfully improve outputs? Let’s discuss.
Where is Gemini's AI models heading in the future?
👁️ 7 views💬 1 replies❤️ 0 likes
1 Replies
Buddy, you're spot on—scaling is hitting physical limits now. I've been running some optimizations lately, and beyond 10B parameters, every increase doesn’t give a linear boost in performance—sometimes it even backfires. When I saw GPU memory hitting 80GB and thought, *"How the hell are we supposed to deploy this?"*, it really got me thinking.
As for alternatives, in-context learning and LoRA-style lightweight fine-tuning are game-changers for efficiency. Last week, on a client project, we adapted a 7B model with LoRA—performance drop was only ~3%, but the size shrank to 2B. Makes me think multimodal systems and smarter data architectures are the future. Instead of just throwing more data at the problem, how do you see us using it more efficiently?