Sometimes, air cooling isn't enough. Direct-to-chip (D2C) liquid cooling targets heat at the source, offering:.💧 Up to 40% energy savings.⚡ Support for high-density GPU racks.🌱 A path to sustainable data center operations. Full breakdown 👉

To take full advantage of the power of your GPUs, they need data. Lots of data. If your storage can’t keep up, your GPUs sit idle. This guide breaks down how to build AI-ready storage:.• High throughput.• Low latency.• Scalable & cost-efficient. Read the full guide here:

Join @WhiteFiber_ CTO, Tom Sanfilippo, and @drivenets Head of AI Product, Yassi Kikozashvili, on July 8th as they discuss the recent our recent fabric-scheduled Ethernet deployment using DriveNets Network Cloud-AI. They'll cover: .- Testing results.- Impact on job completion.

RT @BitDigital_BTBT: .@WhiteFiber_ announced entry into a definitive credit agreement with the Royal Bank of Canada, the largest bank in Ca….

Building AI infrastructure? Don’t just scale—optimize. Smart GPU selection, fast I/O, and elastic scaling = 💸 saved & ⚡ gained. Read the best practices:

Update from Montreal: Direct to chip liquid cooling installation at MTL-3 is underway! 🚀

At WhiteFiber, we build scalable, GPU-optimized infrastructure supporting heterogeneous architectures. Ensure your organization is prepared to harness the full potential of complex AI workloads with the right hardware. Are you ready to scale?.

Industries such as healthcare, financial services, and autonomous vehicles are already relying on heterogeneous clusters for real-time complex inference tasks. This technology drives breakthroughs from personalized medical diagnostics to safer autonomous systems.

By matching specific workload tasks to optimal hardware, heterogeneous clusters can improve energy efficiency and lower operational costs by up to 30%. This tailored approach optimizes both performance and resource utilization.

Heterogeneous clusters significantly enhance performance for complex inference workloads achieving up to 5x faster processing times compared to traditional setups. Businesses leveraging diversified architecture gain clear competitive advantages.

The AI accelerator market is projected to surge from $18 billion in 2023 to over $250 billion by 2030. This rapid growth reflects enterprise demand for more specialized compute capabilities.

As AI workloads grow in complexity, traditional homogeneous computing approaches are hitting limits. Heterogeneous AI clusters combining GPUs, CPUs, FPGAs, and specialized accelerators are quickly becoming essential to power sophisticated inference at scale.

🎉 Introducing the WhiteFiber North American flagship data center campus: NC-1. Learn more here 👉

Busy week. Thanks for having us @HPE Team! 🚀

RT @BitDigital_BTBT: BREAKING: @WhiteFiber_ has completed the acquisition of a 96-acre, ~1,000,000 sq ft industrial property to support up….

Blog Post: Official Press Release:

📍 970,000 square feet of existing industrial space on 96 acres 100 miles north of Raleigh and Charlotte. ⚡ 24MWs Q4 2025 → 99MW Secured → up to 200MW (subject to obtaining Electricity Service Agreements from Duke Energy and certain other conditions.). 🤝First customer secured.

More space, much more power. ⚡. Today WhiteFiber is announcing the acquisition of NC-1, our flagship 24MW Tier-3 data center in North Carolina! Planned to go live in December 2025, the site includes 99MWs of secured power to scale with our customers. The AI Infrastructure

As AI systems grow more powerful, and more integrated with sensitive workflows and data, privacy-preserving techniques like this are becoming increasingly valuable. The full post breaks it down in detail: #AI #Privacy #DifferentialPrivacy #Compliance.

Organizations exploring differential privacy often focus on a few key practices:.– Defining appropriate privacy budgets.– Applying protections early in the pipeline.– Using infrastructure optimized for compute-intensive workloads.– Conducting regular audits.