We’re sharing how GLM-5.3 helped build and optimize the inference infrastructure serving GLM-5.3-Flash. The system went from its first successful run to production readiness in less than two weeks, with end-to-end throughput tripling relative to the initial baseline. The key was dense feedback: local correctness tests, execution traces, microbenchmarks, and end-to-end measurements that enabled targeted hypothesis testing rather than reliance on aggregate performance metrics alone.

At @ionet we did not just sit around theorizing about open intelligence. I watched the bottleneck up close when we launched IO Intelligence in Feb 2025. Brilliant teams with real ideas kept getting stuck behind waitlists, quotas, and prices that quietly decide who even gets to test the frontier tech. I kept thinking about what happened with AWS and Azure. They locked startups early, and for years these firms either rented compute on their terms (price, tenure, KYB etc.) or few attempted to stand up their own data center nodes. Innovation still happened. It just happened inside someone else’s garden. You could build, but only on their inventory, their timeline, and their bill. Open weights models without usable and affordable compute is still a gated system. That is the part that stays with me. At @ionet we built a marketplace so idle GPUs could become clusters people could actually launch. We built our own inference stack to allow users to access multiple open source models at industry grade TTFS with zero data retention. Not another dashboard. Not another permission slip. It is the same story with the recent AI safety calls by leading frontier labs. Whenever the essential layer concentrates, progress does not disappear. It just becomes something a few companies get to ration. Defense before restriction only works if the people doing the defending can afford to run the work.

🚀 Introducing DeepSeek-V4.1-Flash: smarter, faster, more efficient. 🔹 Introducing the smallest model in our new architecture family, with native visual understanding. 🔹 Designed for greater capability, faster inference, higher throughput, and scaling to larger models. 1/6

Introducing GLM-5.3-Flash - Leading capabilities at a highly competitive price - Natively multimodal with a 1M-token context window - A 320B-A18B model released under the MIT License - Previously previewed as Ox Alpha, running entirely on Chinese AI chips Blog: Available now across all official platforms: Weights: API: Coding Plan: ZCode: Chat: AutoClaw:

Since announcing Jalapeño, our first custom inference chip, we’ve been testing it and the system around it. The results show a major advance: more intelligence from every watt and faster responses, delivering both higher throughput and lower latency in one architecture without sacrificing efficiency.