GPU-rich labs have won: What's left for the rest of us is distillation
16 days ago
- #Distillation
- #AI
- #LLM
- OpenAI spends over $50M daily on LLM training, making competition without massive resources futile.
- 2024 saw wasteful AI spending by enterprises, with their models quickly becoming outdated by new releases from major labs.
- Open-source models are catching up through distillation of large proprietary models, exemplified by Deepseek.
- The gap between open-source and proprietary models is widening due to GPU wealth disparity.
- 2025 focuses on agents and the application layer, with enterprises shifting to smaller, task-specific LLMs.
- Distillation allows training smaller models using outputs from large models, conserving performance while reducing costs.
- Distillation is key for reducing latency and costs post-product-market fit.
- Inference.net offers end-to-end distillation and inference solutions for founders focused on the application layer.