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#Ai Infrastructure

27 articles on Ai Infrastructure — news, releases, guides and analysis from the SourceFeed engine.

llama.cpp Finally Competes With Its Own Wrappers

With Hugging Face backing, the engine under Ollama and LM Studio now ships its own installer and model catalog.

Tooling keeps improving, yet emission control, safe filtering and org friction remain open failures at production scale.

Mythos-class capability arrives with 2x pricing, always-on thinking, and fallbacks that can silently change your answer.

The first statewide moratorium on hyperscale data centers matters politically more than it matters to your GPU capacity plans, at least for now.

Enterprise cards at $60–200 remain usable for intermittent inference and training if you tolerate older stacks and power draw.

Meta is transitioning from an ad-driven giant to a vertically integrated AI hyperscaler, challenging the cloud status quo.

By financing specialized AI clouds, NVIDIA bypasses hyperscaler custom silicon but accelerates the commoditization of its own compute.

Vertical transistor stacking promises to slash AI training times and rewrite the rules of accelerator memory bottlenecks.

By building transistors vertically, IBM’s 7-angstrom architecture tackles the memory and power walls limiting next-generation AI accelerators.

Why open models are a structural requirement, not a niche preference, for the global developer ecosystem.

Microsoft strips the hypervisor and expands fleet management to turn Kubernetes into the default runtime for distributed AI.

As AI data centers monopolize high-bandwidth memory production, the resulting DRAM shortage is driving up prices for even decades-old silicon.

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