io.net

Myth: Centralized Cloud = Always Available Try spinning up an A100 on AWS during peak hours. ❌ Quota denied ❌ Instance unavailable ❌ Try again later Azure and CoreWeave? Same story. Waitlists, regional caps, opaque approvals. This isn’t availability. It’s bottlenecked access wrapped in enterprise branding. io.net = GPU access when you need it $IO accelerates AI

GM Vietnam brought 10,000+ builders, founders, and investors to Hanoi. io.net ran its first educational workshop in collaboration with UBA, one of Vietnam’s leading blockchain universities. 120+ students joined to learn how to build AI tools and customize models using decentralized compute. Raj Karan gave a keynote on why scalable AI depends on open-source, decentralized infrastructure to a packed room. Thanks to the team who made it happen and to everyone we met in Hanoi. Looking forward to what’s ahead in the region.

70% of AI teams are solving the wrong latency problem. While everyone optimizes models, there's an infrastructure shift happening that most technical leaders are completely missing. It's not about faster GPUs or better algorithms. It's about where computation actually happens. Find out more in our new breakdown of mobile edge computing and the future of mobile-based data processing. Link in comments ↓

Thanks to AI Journal for covering io.net's latest milestone: Training-as-a-Service. Their breakdown captures why decentralized infrastructure is uniquely suited for model fine-tuning, RAG workflows, and multi-agent systems: “Instead of relying on centralized cloud providers, teams can now access permissionless compute at scale.” Link to full article in comments.

The harsh reality: 73% of distributed systems fail to scale beyond initial deployment. Is your AI startup heading for a compute crisis? ML workloads need 40x more resources than traditional applications, but most teams are using infrastructure strategies that break at production scale. The companies that make it past this bottleneck understand something critical about distributed architecture that the 73% who fail completely miss. It's not about adding more machines. It's about three specific design decisions that determine whether your system scales or collapses under load. Link in comments for the full breakdown of what separates the 27% who succeed.

The White House just released "Winning the AI Race: America's AI Action Plan" today - a major shift from safety-first to innovation-first AI policy. And its timeline is aggressive: 90+ federal actions in the next 6-12 months. It's three key pillars are: ‣ Removing regulatory barriers to free up private sector R&D ‣ A massive investment in data centers, chips, and energy to make the US the global AI foundation ‣ Exporting full-stack AI packages to allies to set worldwide standards This means a much more favorable regulatory environment for AI deployment is coming. What's your take on this innovation-first approach?

AI hardware cycles are accelerating faster than infrastructure can adapt. Hopper to Blackwell = 30x performance jump in 2 years. While others get locked into 3-year cloud contracts, teams building cutting-edge AI spin up the latest GPUs in minutes. The question: Is your infrastructure strategy built for 2022 hardware or 2024 breakthroughs?

Most teams are asking the wrong question about infrastructure. It's not edge vs cloud. That's like asking "email or Slack?" when you obviously need both. But 90% of technical teams are still approaching this as an either/or decision, missing massive performance and cost opportunities. The real question is: which workloads belong where? Read the full article to help decide.

AI infrastructure is hitting a wall. And it's not a technical problem. Data center energy consumption will double by 2030, with cooling and networking bottlenecks creating 15-25GW shortages across APAC alone. Meanwhile, your AI development timeline isn't slowing down. io.cloud can help: Intelligent workload distribution across global compute resources. Route training jobs to regions with available power. Run inference where cooling costs are lowest. This delivers 70% cost savings and infrastructure that adapts to constraints rather than being constrained by them. Link to the full report in comments.

Figma is spending $300K daily on AWS. That's $100M annually or 12% of their revenue. Their S-1 filing is telling: the risk of complete dependency on AWS is an operational vulnerability they can't control. If AWS changes terms or cancels its contract, it would "create significant challenges" for its business. For AI/ML teams, this hits harder. Training and inference workloads consume massive compute, and traditional cloud pricing scales linearly with usage. io.cloud delivers the same enterprise-grade GPUs at 70% lower cost with zero vendor lock-in. Access H100s and A100s across 130+ countries without giving up operational control. Link to full story in the comments. Try io.cloud now: https://lnkd.in/ghtaxZ9E