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GPU Cloud for non-AI workloads: Rendering, simulation, and scientific computing

The GPU cloud conversation over the last three years has been almost entirely captured by AI. Marketing pages talk about training, inference, and foundation models. Vendor announcements focus on which NVIDIA card fits which LLM. Reference customers are AI companies. The infrastructure decisions being made in the market are shaped by AI's specific requirements - high VRAM, fast interconnect, FP8 support, continuous utilization patterns.

If they can turn it off, you don't own it - The AI kill switch problem

If someone else can turn it off, you don't own it. And most organisations haven't fully reckoned with what that means for their AI strategy. Civo Product Director Russ Smith draws a direct line from the Broadcom/VMware licensing shock to the Anthropic model restrictions, two different industries, same structural problem. When a vendor can change the rules overnight, businesses that built their strategy around that vendor are left with uncertainty and no clear next step.

NVIDIA B300 vs. NVIDIA B200: Blackwell Ultra vs. Blackwell

The Blackwell architecture arrived in 2024 as NVIDIA's answer to the next era of AI compute. The B200 set a new standard for inference performance, memory capacity, and training throughput, and many teams are still ramping up their use of it today. Then came Blackwell Ultra. The B300 is built on the same silicon foundation as the B200: same dual-reticle die design, same TSMC 4NP process node, same NVLink 5 interconnect.

An introduction to the NVIDIA B300: The Blackwell Ultra GPU

AI wasn't supposed to move this fast. Twelve months ago, the H100 was still the benchmark everyone measured themselves against. Six months ago, the B200 changed the calculus for serious inference workloads. Now there's the B300, NVIDIA's Blackwell Ultra GPU, and it doesn't just move the goalposts. It takes them off the pitch entirely. The B300 is the highest-performance GPU in the Blackwell family.

Konstruct product updates: GitLab support, platform broadcasts, and a permission model built for scale

July has been one of our most structurally significant releases yet for Konstruct. With 0.6, we've shipped a second git provider, a completely reworked authorization model, new tooling for AI-assisted troubleshooting, and a handful of improvements that are smaller in scope but large in impact for day-to-day platform operations. Let's walk through what shipped and why it matters. You can explore the full 0.6 release notes directly in the docs.

Your first Internal Developer Platform: Zero to day one

Building an Internal Developer Platform doesn't have to be a months-long project. Join us for a practical walkthrough where we'll spin up a fully functional platform from zero to production-ready in under 30 minutes. We're using Konstruct's Hosted Control Plane to show you exactly how fast modern IDP tooling can be. You'll watch as we deploy a team's first cluster, showcase the essentials that actually matter, and explore how this foundation becomes the backbone of developer productivity at scale.

This is what cloud freedom looks like

Proprietary tech. Vendor lock-in. Pricing you can't predict. The hyperscaler model has defined cloud computing for years. Civo was built to change that. Civo CEO Mark Boost sets out the vision, a multi-cloud and hybrid cloud future built on open standards, full cloud parity, and genuine user choice. Public cloud, private cloud, and AI infrastructure that gives you total control over your data, your infrastructure, and your spend.

Why cloud repatriation is happening now

Cloud first was gospel for a decade. But the calculus has changed, and organisations are asking harder questions about where their workloads actually belong. In this clip, Civo Product Director Russ Smith breaks down the four forces that have converged to shift the default: spiralling bills that are no longer defensible at scale, the Broadcom acquisition that detonated VMware pricing overnight, sovereignty becoming a boardroom procurement requirement, and AI making new hardware brutally expensive.

How do you run AI when your data can't leave the network?

Highly classified environment. Strict compliance requirements. Data that can't leave the network. But still a real need for the competitive advantage AI delivers. Civo Director of Enterprise Cloud Solutions John Dietz addresses exactly that challenge and how Konstruct makes it possible to run Kubernetes, deploy your own models, and point Claude Code at your own internal private servers instead of public APIs.