Hey there, folks! I just came across an interesting article about scaling AI inference to billions of users and agents. The article dives deep into the full infrastructure stack required to achieve this feat. It’s not just about a single engine, but rather the entire system.
The author highlights several key components, including the GKE Inference Gateway, which cuts tail latency by 60% and boosts throughput by 40% with model-aware routing. There’s also vLLM on GPUs and TPUs, which serves as a unified layer to handle models across different hardware. Additionally, the article explores the future of llm-d, a new Google/Red Hat project for disaggregated inference.
Other notable topics include planetary-scale networking, managing capacity and cost, and building a resilient and cost-effective mix of Spot, On-demand, and Reserved instances. If you’re interested in learning more, I recommend checking out the full article, which includes architecture diagrams and walkthroughs.
What do you think about the future of AI inference? Do you think we’ll see widespread adoption of these technologies in the near future?