Jun. 15 at 4:30 PM
Core names in the data center server chain - the hidden backbone AI depends on to actually run.
$AMD
CPU layer - still central for training + inference compute.
$MU
HBM memory - feeding GPUs with the bandwidth they actually need.
$SNDK
NAND storage - datasets, checkpoints, model persistence layer.
$ANET
Network backbone - high-speed switching between racks and clusters.
$MRVL
Data movement silicon - optimizing internal AI data center traffic.
MXL
Connectivity layer - broadband, fiber, and infrastructure bridge chips.
CRDO
Low-cost interconnect - cables + chips linking AI servers at scale.
What’s important here is simple: AI scaling isn’t just GPUs. It’s the entire stack compounding together. When demand accelerates, it doesn’t hit one ticker-it hits the whole chain.
