Running large AI models locally saves costs and preserves privacy. Current solutions struggle with models over 100B parameters.
Develop optimization layers that split computation across GPU memory hierarchies. Focus on reducing memory overhead and maximizing throughput for transformer architectures.
Monetize through Pro licenses for researchers and commercial users, plus paid support contracts for enterprises.
Start with a proof-of-concept showing 100B+ models running at usable speeds on 4090s, then optimize for specific use cases.
Risk: Cloud providers may undercut with cheaper inference APIs before your solution gains traction.