0:00/76.5606671× AI demand has turned chip choice into a business decision, not only an engineering one. If you run model training, large-scale inference, or edge AI, the hardware mix now shapes cost, speed, power use, and lock-in. That matters because the market is no longer centred on
AI Grid with stack8s - podcast0:00/117.21× Telcos no longer run AI in one neat data centre. They run it across towers, central offices, regional sites, and cloud zones. That spread creates a hard problem: how do you manage all of it as one platform without losing control
I keep seeing founders burn weeks building shiny AI agents, then wonder why nothing sticks. The bottom line is simple: most "agents" don't create durable value, they create moving parts. When the model changes, the tool changes, the prompt breaks, and the whole thing wobbles. I&
Running GPT-OSS-120B in production sounds like a pure compute problem. In practice, it's a memory problem first, then everything else. DevOps teams want predictable latency and clean scaling. CTOs want a platform choice that won't stall delivery. CFOs want a cost line they can defend. GPT-OSS-120B
If you're pricing an AI cluster in March 2026, the names can feel like a trap. H100 SXM5, H100 PCIe, and H100 NVL all say "H100", so they must behave the same, right? In practice, the module, power limit, memory bandwidth, and GPU-to-GPU links change what