EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Lobsters·Aug 11·5 min read
No, local models will not win
TL;DR
Local AI models are unlikely to surpass datacenter models due to their inherent limitations in power and cost efficiency. As AI capabilities grow, users will prefer the strongest models available, which will continue to be hosted in datacenters.
✦ Why It Matters
Consider investing in cloud-based AI services rather than attempting to run models locally.
Key Takeaways
- Prioritize datacenter models for powerful AI applications.
- Understand the limitations of local models in processing power.
- Leverage batching capabilities of datacenters for cost efficiency.
- Explore niche applications for local models where control is essential.
Full Summary
The discussion centers around the belief that local AI models will dominate the future, but this perspective is challenged. Current frontier models, whether open or closed, are too large to run effectively on personal devices like laptops or phones.
While smaller models may improve, user expectations will continue to rise, leading them to prefer the most powerful options available. Datacenter models are also more cost-effective due to their ability to batch process requests, making them inherently more efficient than local setups.
For instance, local inference can waste resources as it lacks the batching capabilities that datacenters utilize. Although local models may find a niche for specific use cases, such as low-latency applications or user control, the overall trend suggests that datacenter models will remain the preferred choice for most users.