MinIO

Brenna Buuck and the MinIO team were at Iceberg Summit this week talking about how your choice of object storage software affects your lakehouse performance. Our TPC-DS benchmarks show AIStor accelerates Iceberg lakehouses when compared to cloud storage alternatives. https://bit.ly/4cdCedZ #ApacheIceberg #DataLakehouse #AIStor #MinIO #IcebergSummit2026

MinIO Co-CEO and Co-Founder Garima Kapoor Ph.D took the HumanX stage yesterday alongside J.J. Kardwell CEO of Vultr, and Piotr Tomasik President  and Co-Founder of TensorWave, in a conversation moderated by Max A. Cherney, Tech Correspondent at Reuters, to talk about what's really happening in AI infrastructure. The takeaways were clear: ➡️ The underlying infrastructure must be open, portable, and standardized regardless of whether workloads live on private cloud, new clouds, or hyperscalers. ➡️ The value isn't in the compute layer anymore. It's in what you do with it the inferencing, the applied AI, the application layer on top. ➡️ Storage has to be inside the AI data path, not adjacent to it. That's not a feature. It's the foundation. The winners won't be whoever hoards the most infrastructure. They'll be whoever builds the most efficient infrastructure. #HumanX #AIConversations #ArtificialIntelligence #AIEvent #MinIO

What should you look for in object storage for AI? Speed. Scale. Simplicity. AI isn’t bottlenecked by compute. It’s bottlenecked by storage. Most storage wasn’t built for this, but MinIO was. Get answers to your most frequently asked questions. https://bit.ly/411uiaH

📣 Happening today at HumanX: Garima Kapoor Ph.D on where AI infrastructure is headed next. In this panel, she will unpack the real-world demands of scaling AI—from low-latency performance to globally distributed deployment. This conversation will go beyond compute, highlighting why data infrastructure is emerging as the critical factor in how enterprises design for scale. Stay tuned for more. 👀

We believe the future belongs to teams who treat data as the foundation, not an afterthought. That’s exactly the perspective Garima Kapoor Ph.D brings in this conversation. Founder-led thinking, paired with the right data infrastructure, is what turns vision into real-world impact, especially in the age of AI. 🎧 Listen to the interview with Trace3: https://lnkd.in/gxMxxEjA

It’s not the models. It’s not the GPUs. It’s the data layer. https://bit.ly/4mgluYg Modern AI is data-bound. When the data layer isn’t built for parallel, high-throughput workloads, everything slows down. GPUs idle, pipelines stall, costs climb. The organizations that successfully scale AI treat the data layer as architecture, not storage, because AI performance is ultimately a data problem.

Enterprises don’t need to copy massive datasets into proprietary platforms to run AI. The future of AI infrastructure is software-defined—where data stays put, compute moves to it, and scale isn’t constrained by legacy architectures. It’s a shift redefining performance, cost, and control across on-prem and cloud, and we're at the forefront. Just listen to Garima Kapoor Ph.D. ⬇️

AI and analytics run in the cloud, while enterprise data remains distributed across on-prem and cloud environments, creating friction between where compute runs and where data lives. Databricks and MinIO, powered by Delta Sharing, bring a new access layer that keeps data in place while enabling scalable compute for AI and analytics. One source of truth. Director of Product Marketing and Solutions Marketing, Ryan Garrett, and Partner Engineering at Databricks, Denis Dubeau, help you connect on-prem data to Databricks without replication, unlocking customer benefits. https://lnkd.in/ew-F574N

A practical perspective on how modern data systems actually perform is coming to Iceberg Summit 2026, hosted by Apache Iceberg. Developer Evangelist Brenna Buuck brings a hands-on look at where things break—from object storage’s impact on Iceberg query times to building real, working agents. A clear view into where performance is won (or lost). See you in San Francisco. 🌁 https://lnkd.in/gdf32zj2

AI infrastructure is entering a new phase. For years, progress was driven by larger models and more compute. As AI systems scale, a different constraint is emerging: how data is stored, accessed, and delivered. This shift is changing how modern AI architectures are designed and where performance bottlenecks show up. Here's a closer look at what’s driving it. https://bit.ly/411uiaH