CloudLogics

Why put a CPU in the middle when data needs to get to the GPU? 👀 In traditional architectures, data moving from NVMe storage to GPU compute can pass through the CPU first. That extra step can create a bottleneck, especially when AI and HPC workloads need to move large amounts of data quickly. A direct NVMe-to-GPU data path changes the flow. By reducing CPU involvement in the data path, storage can feed GPU resources more efficiently, helping reduce latency, increase throughput, and keep expensive accelerators working. The result: up to 60% lower latency, 3× higher throughput, and 95%+ GPU utilization. #AIInfrastructure #HPC #GPUComputing #NVMe

A lot can happen when you have your head in the clouds. ☁️ What started in 2025 with a team of cloud and HPC veterans in Cleveland has quickly grown into something much bigger. New locations, an expanding footprint, and a platform built around a different vision for AI infrastructure. And we're just getting started. To our team, customers, partners, and everyone who has followed along, asked questions, challenged ideas, and supported what we're building: thank you for being part of the journey. The forecast? More CloudLogics ahead. #AIInfrastructure #CloudComputing #HPC #SovereignCloud

What happens when an AI workload needs to scale beyond a single data center? Adding GPUs is only part of the answer. Those GPUs still need to communicate with storage, data, and each other, which makes the network connecting them critical to overall performance. Dedicated GPU infrastructure connected by private dark fiber offers a different model. Think of dark fiber as a private highway for data, providing dedicated, high-bandwidth connectivity between locations rather than relying on shared network infrastructure. This makes it possible to connect GPU resources across multiple sites while maintaining low-latency, predictable connectivity. If your GPU capacity doubled tomorrow, could your network keep up? 👀 #AIInfrastructure #GPUInfrastructure #CloudComputing #HighPerformanceComputing

What is the real cost of waiting two days for infrastructure? 🕐 For engineering teams, every manual ticket, approval, and handoff adds time between an idea and the infrastructure needed to execute it. Self-service changes that equation. Engineers can provision the resources they need in minutes, while policy guardrails maintain control over how infrastructure is deployed and used. The result is less time managing requests and more time building. #CloudInfrastructure #PlatformEngineering #DevOps

Moving an application does not necessarily mean moving everything it depends on. Containers can still rely on a cloud provider’s storage, identity, networking, and other proprietary services. The application may be portable while the environment around it remains tied to one platform. That distinction matters as organizations rethink their software stack for AI. Software portability does not always mean cloud portability. If containerization gave us freedom, why does changing clouds still feel almost impossible? 👀 Read more: https://loom.ly/5BDt4Ys #CloudComputing #CloudNative #Containers #SoftwareArchitecture

When you think about AI performance, what do you look at first? 🔍 AI performance depends on more than GPU speed. Data has to move efficiently between storage, networking, and compute. When any part of that path becomes a bottleneck, expensive GPU capacity can sit underutilized. That is why AI infrastructure needs to be evaluated as a system, not a collection of individual components. Faster compute only goes so far if storage cannot keep up, the network introduces latency, or data has to travel farther than necessary. What is creating the biggest AI infrastructure bottleneck for your organization today? #GPUInfrastructure #CloudComputing #DataInfrastructure

Is your cloud infrastructure built for today's AI workloads, or yesterday's applications? 👀 As artificial intelligence becomes central to business, organizations are rethinking the cloud infrastructure behind it. Traditional cloud computing was designed for flexibility across many workloads. AI infrastructure has different priorities. Performance depends on GPU compute, high-speed storage, low-latency networking, and efficient data movement working together as one system. That's why more organizations are exploring infrastructure purpose-built for AI. Instead of moving data into centralized cloud environments, CloudLogics brings compute closer to the data, helping reduce latency, improve performance, and support the full AI lifecycle. How is AI changing the way your organization thinks about cloud infrastructure? #CloudComputing #AICloud #GPUInfrastructure #EdgeComputing

Every successful project starts with strong relationships. Meet Gabriella Pettograsso, Account Manager at CloudLogics. With more than eight years of experience in account management, project leadership, and client partnerships, Gabriella is passionate about building genuine relationships that create lasting value. Her experience spans technology and research organizations, where she's helped build lasting client partnerships, drive business growth, and align cross-functional teams to deliver outstanding customer experiences. Today, she brings that same customer-first mindset to CloudLogics, helping ensure every engagement is built on trust, communication, and execution. Thank you, Gabriella, for the dedication, collaboration, and care you bring to our customers and the CloudLogics team. #AccountManagement #CustomerSuccess #ProjectManagement #BusinessDevelopment #CloudInfrastructure

The cloud didn't become the industry standard by accident. Hyperscalers transformed how organizations deploy, scale, and operate applications. They solved the challenges of an era defined by web applications, virtualization, and elastic compute. But AI introduces a different set of constraints. James Williams, Chief Technology Officer at CloudLogics, shares why the next generation of AI infrastructure may require a fundamentally different architecture. Learn why: https://loom.ly/5BDt4Ys #AIInfrastructure #CloudComputing #SovereignCloud #EdgeComputing

Meet John Blais, Senior Technical Account Manager at CloudLogics. With more than 10 years of experience in enterprise customer success, AI platforms, and cloud infrastructure, John helps organizations navigate complex technical challenges and maximize the value of their AI investments. His background in technical account management, solution architecture, and AWS enables customers to scale with confidence. At CloudLogics, John is committed to helping every customer succeed from deployment through long-term growth. #EnterpriseAI #AIInfrastructure #CustomerSuccess #CloudInfrastructure #AWS