Two months ago, I left Annapurna Labs, ending a five-year chapter at Amazon. Amazon treated me exceptionally well and gave me more opportunities to grow than I can count. I’m grateful for the people I worked with and everything I learned there. I followed my heart to Prime Intellect. Its mission to build an open superintelligence stack deeply resonated with me. I believe every person and every business should be empowered to build their own intelligence. Your AI agent should be yours, not a third-party stranger. Walking into Prime’s SF office, I felt ten years younger. These past two months have been the most exciting of my career. Three things stand out: 1. The people. Prime’s team doesn’t all come from famous AI labs, but I’ve rarely seen this level of curiosity and determination. When we made our high interactivity GLM-5.3 serving endpoint available internally, everyone switched their agents to it. They never went back. The speed improved productivity. Just as importantly, the team kept sharing feedback, helping us refine performance and tool-calling reliability. Soon, RL rollouts, synthetic data generation, and long-horizon agent research were running on the same infrastructure. As of this week, our internal inference usage is 600–700 billion tokens per day. That experience gave us the confidence to open the service to the public. 2. The full stack. Prime brings together hosted training with prime-rl, verifiers, sandboxes, an RL environment hub, the Prime Agent harness, and now Prime Inference. You can train, deploy, and run agents in one place, without stitching together infrastructure through a massive engineering effort. And most of these components are fully open source. Our inference stack builds on that same foundation, with deep collaboration across NVIDIA Dynamo, Inferact, and the broader vLLM community. 3. The compute. For anyone working on LLM infrastructure, access to compute at this scale is a privilege. Building and operating inference across large scale of GB200 racks and B300s is not something I take for granted. Vera is already in our hands, and Rubin is on the horizon. NVIDIA’s latest hardware gives us the foundation to push inference performance further. Alongside the public launch, we published a technical post on how we built Prime Inference: primeintellect.ai/blog/prime… Inference has moved incredibly fast over the past year, and it can be hard to separate useful advances from the noise. We use GLM-5.3 optimization to show the work end to end: how we serve agent workloads with low latency while also supporting high concurrency, dependable availability, and reliable tool calls. Recommend for a read if you are interested in learning about P/D, topology choice, NVFP4 KV compression kernel fusion, tool call reliability. Fast inference matters. Serving real agents reliably at scale is what counts.