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The Data Has Always Been at the Edge. The Compute is Finally Following.

The Data Has Always Been at the Edge. The Compute is Finally Following.

GigaIO Alan Benjamin, CEO, GigaIO
Why datacenter-class GPUs belong at the edge — and why the industry is finally making it happen.
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Why Gryf Is Rewriting the Rules of Edge Deployment

Why Gryf Is Rewriting the Rules of Edge Deployment

15 Jun 2026 GigaIO Alan Benjamin, CEO, GigaIO
The future of edge computing isn’t just about what a system can do — it’s about what you can do with it, wherever it happens to be.
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Mirantis k0rdent AI Enterprise: Unified Control for Distributed Enterprise Infrastructure

Mirantis k0rdent AI Enterprise: Unified Control for Distributed Enterprise Infrastructure

Mirantis
Enterprise platform teams are under growing pressure to support more clusters, more environments, and now AI workloads, while headcount stays flat and compliance requirements increase. Most organizati …
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Building AI Factories with Mirantis k0rdent AI

Building AI Factories with Mirantis k0rdent AI

Mirantis
AI Factories are rapidly becoming the industry’s gold standard for delivering modern AI and ML services. These factories are highly automated, secure, and scalable infrastructure platforms purpose-bui …
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Tactical Edge Supercomputing – It’s Real, It’s Today

Tactical Edge Supercomputing – It’s Real, It’s Today

14 Jul 2026 GigaIO Flywheel Data, Nutanix, and GigaIO
This white paper details a validated, full-stack design combining the GigaIO Gryf Carry-on AI Supercomputer, Nutanix Kubernetes Platform (NKP), and Nutanix Enterprise AI (NAI). This integrated solutio …
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Data Center Networking in 2026 - Scaling to New Heights: Up, Out, and Across

Clockwork.io
AI workloads are fueling a massive expansion of data center infrastructure. Leading technology companies are projected to spend over $630 billion on AI infrastructure in 2026. A key component of this …
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The AI infrastructure race is measuring the wrong thing

Clockwork.io
The AI industry keeps counting GPUs and gigawatts, but the real competitive edge is shifting toward squeezing more useful work out of the infrastructure companies already own — not buying more of it.
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Clockwork Fleet-Monitoring: A Layered Architecture for GPU Network Observability

Clockwork.io
Why large-scale GPU fabrics fail partially and silently — links stay "up" while training slows down — and how combining edge-based one-way-delay probing with switch telemetry closes the gaps neither c …
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TorchPass Live GPU Migration for Resilient Distributed Training

Clockwork.io
Eliminate the biggest source of wasted GPU-hours in distributed training. Instead of restarting from the last checkpoint after a failure, TorchPass migrates training state live to a spare GPU and resu …
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HBM4E Memory: Break Through to Greater Bandwidth

Rambus
Download the article below to learn more
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