1 Petabyte Storage On The Way! Samsung is developing nearline solid-state drives that scale from 250 terabytes to 1 petabyte. One petabyte is 1,024 terabytes. The form factors under discussion are EDSFF E3.L or E2. At the upper end, nearly fifty of the largest drives could occupy a 4U shelf and approach 50 petabytes; a full rack would approach half an exabyte. The information comes from a May 2026 briefing between storage software maker Scality and Blocks & Files. Scality chief product officer Erwan Girard stated that Samsung is building these ultra-density units with the explicit goal of replacing hard drives. He described a new type of flash with endurance of roughly 0.1 drive writes per day, about one-fifth the rate of contemporary QLC. Scality has early samples in its lab. Initial manufacturer numbers suggested slower performance, but lab tests showed no visible difference from QLC. Scality CEO Jérôme Lecat said the company has a co-development agreement with the Samsung Memory Research Center and that a 2026 release is unlikely. Samsung has shown high-capacity enterprise SSDs before. In 2022 it displayed a 128 TB QLC prototype. In 2023 it announced a 256 TB QLC drive aimed at data centers and outlined a petabyte-scale PBSSD architecture built from multiple drives. The current nearline work uses a different flash type, optimized for density and low write traffic rather than general-purpose endurance. For data-center operators the density reduces the number of racks needed for archives, model weights, or infrequently rewritten datasets. The endurance limit sets the boundary: the drive is not built for daily rewrites. Scality positions the devices in a warm tier alongside nearline hard drives, where the workload is read-heavy. Samsung’s semiconductor division continues to publish roadmaps that list higher layer counts and packaging advances as the route to larger single-drive capacities. The nearline samples under test represent one step along that path. No public product name or firm ship date has been announced. Samsung Semiconductor:

CEREBRAS $CBRS IS ABOUT TO GO PUBLIC ... HERE IS A DEEP DIVE ON WHAT THEY ACTUALLY DO Most people have no idea what this company actually does. Here is the plain-English version: THE BIG IDEA Every AI model you have ever used (ChatGPT, Claude, Gemini, Llama) is trained on chips. The dominant company is NVIDIA $NVDA, which sells lots of small chips that get wired together into clusters by the thousands. Cerebras went the opposite direction. They build ONE giant chip the size of a dinner plate. Not exaggerating. Their flagship "Wafer-Scale Engine" (WSE-3) is 46,225 square millimeters of silicon. A normal AI chip is smaller than a postage stamp. THE NUMBERS On that one giant chip: - 4 trillion transistors (a top-end NVIDIA GPU has roughly 80 billion) - 900,000 AI cores - 44 GB of on-chip memory - 125 petaflops of compute power - Built on TSMC's 5nm process WHY THAT MATTERS When you wire thousands of small chips together, the wires become the bottleneck. Data has to travel between chips constantly. That eats time, power, and money. Cerebras keeps everything on one piece of silicon. No cables between chips. No slow networking. Just one giant brain. The pitch: faster training, faster inference, fewer engineers needed to manage cluster bottlenecks. WHAT THEY SELL Three ways to use Cerebras: - Buy the system outright (the "CS-3" is the box that holds the chip) - Rent compute via Cerebras Cloud - Dedicated capacity contracts for big customers They have 6 new AI inference data centers coming online across North America and Europe. WHO ACTUALLY USES IT The customer list is the validation: - OpenAI: $20B+ committed over three years - Meta $META: powers the Llama API for developers - Perplexity: runs its Sonar search model on Cerebras - Mistral: the French AI lab runs Le Chat on Cerebras - Mayo Clinic: trains genomic AI models on Cerebras infrastructure - GSK $GSK: trains biological language models - Argonne National Lab: has used Cerebras hardware since 2019 - AWS: hosts Cerebras chips inside Amazon data centers, accessed through Bedrock - US Department of Energy: signed an MOU for the Genesis Mission THE TRADE-OFF Cerebras is small compared to NVIDIA. 2025 revenue: $510M. 2025 operating loss: $146M. Concentration is the risk most coverage will not flag: - G42 (the UAE conglomerate) was 85% of 2024 revenue per Reuters - G42 plus MBZUAI (the Abu Dhabi AI university) were 86% of 2025 revenue per FT - The OpenAI deal is the big bet to diversify away from that concentration THE STORY IN ONE LINE NVIDIA bet that the future of AI is millions of small chips working together. Cerebras bet on one giant chip doing the work in one place. The market just decided their bet is worth nearly twice what they priced it at.