Choosing a GPU for AI infrastructure is becoming less straightforward. A few years ago, the question...
NVIDIA's H100 (proven), H200 (higher memory), and B200 (Blackwell) match different AI workloads; GPU choice depends on memory constraints, scale, and ROI—not generation. Auto-upgrading for novelty wastes capex; H200 is critical for memory-intensive inference, B200 targets hyperscale clusters, H100 remains sound when migration costs exceed performance gains.
Choosing a GPU for AI infrastructure is becoming less straightforward.
A few years ago, the question might have been:
What's the fastest GPU we can afford?
Today, infrastructure teams have more variables to consider.
Do you need the GPUs for training or inference?
The AI hardware landscape has shifted significantly in 2026, with NVIDIA, AMD, and Intel all...
The Nvidia H100 was the workhorse behind nearly every major language model trained between 2023 and...
If you have spent any time provisioning AI infrastructure over the last few years, you have watched...
As companies put AI to work, investors are looking beyond GPUs to CPUs, servers, data centers and power infrastructure.
Two AI computing clusters built from identical NVIDIA H100, GB200 NVL72, or GB300 NVL72 systems can deliver materially different…
Which hardware should run this model? Most teams answer that question with habit instead of arithmetic. The model card says GPU.…
