EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Lobsters·20d ago·5 min read
tokens too cheap to meter
TL;DR
The cost of using machine learning is rapidly decreasing, with large language models (LLMs) expected to become integral to computing infrastructure within a few years. Local deployment of frontier-quality LLMs on standard hardware is anticipated within 3-6 years, shifting focus from token quantity to quality and access.
✦ Why It Matters
Evaluate your current AI tools and consider transitioning to local models for better performance and cost efficiency.
Key Takeaways
- Monitor advancements in local AI model capabilities.
- Consider integrating LLMs into your existing infrastructure.
- Stay informed about GPU efficiency improvements.
- Evaluate open-weight models for cost-effective solutions.
Full Summary
Machine learning costs are plummeting, with predictions that large language models (LLMs) will soon be embedded in all computing aspects as foundational infrastructure. Current trends suggest that within 1-2 years, LLMs will be widely integrated, and in 3-6 years, high-quality models will run locally on everyday hardware.
This shift will change the landscape of AI usage, where the limiting factors will be the quality of models and access rather than the sheer number of tokens processed. Evidence supporting these claims includes advancements in both proprietary models like GPT-6 Astra and open-weight models such as GLM-5.3-flash.
Additionally, GPUs are becoming exponentially more efficient, further enabling these developments. The implications are significant, as they suggest a democratization of AI capabilities across various sectors.