B300 vs. H200: How to Decide Which Node Your Workload Actually Needs

B300 vs H200: a technical decision framework for bare metal GPU infrastructure, covering memory, FP4 throughput, cooling, and workload fit.

What a 24-Month Bare Metal Contract Actually Looks Like, And Why It Changes How You Plan

What a 24-month GPU infrastructure contract actually locks in, what stays flexible, and how it changes capacity planning for AI teams.

10 Questions That Reveal the Real Cost of Your Cloud GPU Setup

10 questions that expose the real cloud GPU cost behind egress, idle capacity, and hidden fees, and how to calculate what dedicated infrastructure actually saves.

B300 for AI Model Labs: What Sustained Training Looks Like on Dedicated Bare Metal

AI model labs running sustained pretraining campaigns have infrastructure requirements that shared cloud was not built for. Here's what dedicated B300 bare metal delivers, and what a 12-month compute commitment actually looks like in practice.

Why the B300 Is the GPU Serious AI Teams Are Planning Around Right Now

The B300 is in mass production and GB300 shipments are projected to grow 129% in 2026. Here's what the architecture delivers technically, and why dedicated bare metal is the right way to access that capacity.

B300 for Production Inference: What Changes When You Move Beyond Training

What the B300's memory capacity, FP4 compute, and latency profile mean for teams serving LLMs at scale, on dedicated bare metal infrastructure.