EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Lobsters·Aug 22·5 min read
There continue to be reasons for software to be slow
TL;DR
Despite advancements in technology and tools like LLMs, software performance issues persist due to various underlying factors. The notion that software can be universally fast is challenged by the complexity of optimization choices.
✦ Why It Matters
Evaluate your current software optimization strategies and consider integrating specialized solutions for better performance.
Key Takeaways
- Evaluate the impact of LLMs on your team's performance budget.
- Prioritize performance work alongside feature development.
- Understand the complexities of implementing optimizations in production.
- Use performance analysis tools to identify bottlenecks.
Full Summary
Dan Luu's blog post argues that while large language models (LLMs) have made certain optimizations cheaper, they haven't fundamentally changed the reasons software remains slow. Key issues include increased tolerance for slower performance, reduced budgets for performance work, and the complexity of implementing optimizations in production systems.
For instance, developers may prioritize new features over performance improvements, leading to a backlog of performance-related tasks. Additionally, the assumption that LLMs will drastically reduce costs for performance work often fails, as the actual implementation and maintenance costs can be significant.
The article highlights that while tools for performance analysis are improving, the average software is becoming slower and more resource-intensive.