AI GPU Sourcing Guides

These AI GPU sourcing guides are for teams building, training, or running AI products. Each one explains a single decision: where to run, which GPUs to choose, and how to commit. Read the one that matches your next step.

Which guide do you need?

Where should your AI workloads run?

What Is a Neocloud?: for teams deciding where to train and serve models, and how neoclouds differ from hyperscalers.
GPUaaS vs Neocloud: What’s the Difference?: for teams sourcing GPUs for training or inference who keep hearing both terms.

Which GPUs fit your models?

H100 vs H200: for teams choosing NVIDIA Hopper GPUs to train models or serve them in production.
NVIDIA vs AMD GPUs: for teams weighing both makers for training and inference.
B200 vs GB200 vs DGX B200: for teams choosing an NVIDIA Blackwell system to train models or run them in production.

How should you buy GPU capacity?

On-Demand vs Reserved GPU: for teams with AI in production deciding what to reserve and what to burst.
GPU Cloud Pricing Comparison 2026: for whoever funds an AI product or program and needs to compare provider proposals fairly.
How to Right-Size a Multi-Year GPU Contract Before You Sign: for teams sizing a long-term GPU commitment to proven production demand.

Where do the guides fit with the rest of the site?

Guides explain the decision. Our GPU architectures archive covers each NVIDIA and AMD GPU in detail. For news and quarterly trends, read the GPUSeeker Blog.

Next step

A guide can frame the decision. Finding hard-to-find capacity is the harder part. Talk to a GPU sourcing advisor. In a short conversation, we learn what you need to train or run. Then we connect you with a respected Technology Services Distributor (TSD) whose providers build a right-sized proposal.