Simpson MFG

The rising cost of building Nvidia AI servers is pushing Taiwan manufacturers to ready more cash for operations as the cost of a single AI rack surges, media report:

Source: Dan Nystedt

$Sandisk(SNDK.US) A simple analogy: Imagine a chef working on a tiny cutting board (HBM). Every time a customer adds a request—"No onions... Wait, add onions... Now make it vegetarian... And add a side dish"—these sticky notes (context/KV cache) pile up on the board. Eventually, the board is covered in notes, and the chef can't chop; this expensive chef just stands idle. The so-called "architectural refactoring" is simply establishing a reasonable kitchen workflow: Keep the most urgent notes on the cutting board (HBM), move "important but not immediate" notes to the prep table next to it (DRAM), and store the rest in a nearby filing cabinet/pantry (enterprise SSD). Then, you add a runner and an organizer (DPU + network) to fetch and place the right notes at the right time, allowing the chef to cook at full speed—this means higher throughput, lower per-token cost, and less wasted GPU time.$Micron Tech(MU.US)

Nvidia is working with SK Hynix on a brand-new AI solid-state drive (SSD) with 10x the performance of current AI server SSDs, a project code-named “Storage Next”, media report. A prototype is expected in 2026. It's being designed as a memory layer between an AI server’s memory and storage, while also preparing for the future needs of data centers in Space. The performance target of the new AI SSD is 100 million Input/Output operations per second (Wow!). Taiwan’s Phison is a co-developer. $NVIDIA(NVDA.US) #SKhynix #Phison #SSD #Semiconductors

Source: Dan Nystedt