Foundation Model Training Bottlenecks: Memory Bandwidth, Not GPU Count

Foundation model training infrastructure depends on GPU memory bandwidth and interconnect, not GPU count. Here's the data behind it

Why Long-Duration Training Runs Fail on Shared Cloud, And What to Do About It

Job preemption, noisy neighbor effects, inconsistent storage I/O, and checkpoint egress costs are structural properties of shared cloud, not edge cases. Here's what long training runs cost, and what dedicated bare metal resolves.

10 Questions to Ask Your GPU Infrastructure Provider Before You Sign Anything

Before signing a GPU infrastructure contract, most teams ask about specs and price. The questions that actually matter are different. Here are 10 to ask any provider before you commit.

B300 on Bare Metal: What the Architecture Means for LLM Training at Scale

The B300 delivers 3.6x more GPU memory and a generational leap in FP4 compute over the H100. Here's what that means for LLM training at scale, and why bare metal infrastructure makes the difference.

The Real Cost of Training AI Models in the Cloud

The Smarter Way to Scale Generative AI Workloads

Learn how to build scalable, high-performance generative AI infrastructure with bare-metal RTX 5090 GPUs — transparent, predictable, and built for speed.