Micron
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Funding history
| Date | Stage | Amount | Valuation | Lead investors |
|---|---|---|---|---|
| Jun 1, 1984 | IPO | — | — | — |
Stock performance
How they make money
AI bull / bear
In the news
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Leadership
Products
HBM3E / HBM4 High-Bandwidth Memory
Micron's stacked high-bandwidth memory for AI accelerators, the core of its AI growth story. Its 12-high HBM3E (36GB) is among the industry's most power-efficient, with ~30% lower power than rivals, and ships into Nvidia's Hopper/Blackwell platforms. Micron is now in high-volume production of HBM4 36GB 12-high, hitting 11+ Gb/s pin speeds and 2.8+ TB/s bandwidth, designed into Nvidia's Vera Rubin generation. Micron's entire 2026 HBM output is sold out under signed agreements.
DDR5, LPDDR5X & SOCAMM Modules
Micron's mainstream DRAM in module form for servers, PCs, and AI systems: high-capacity DDR5 RDIMMs/MRDIMMs for data centers, low-power LPDDR5X for mobile and AI inference, and SOCAMM, a compact, swappable low-power module co-developed for Nvidia's AI servers. These products carry Micron's leading-edge 1-beta and 1-gamma DRAM process nodes into the broad memory market alongside its HBM franchise.
GDDR7 Graphics Memory
Micron's graphics DRAM for GPUs and accelerators, shipping on Nvidia's Blackwell RTX 50-series cards. GDDR7 pushes pin speeds toward and beyond 40 Gbps, delivering the bandwidth needed for high-end gaming, workstation, and AI-inference GPUs. It is a key piece of Micron's portfolio for accelerated computing outside the HBM stack.
9550 / 9650 NVMe Data-Center SSDs
Micron's flagship enterprise NAND storage. The PCIe Gen5 9550 NVMe targets AI training and data-center workloads with high throughput and power efficiency, while the newer 9650 is among the first PCIe Gen6 data-center SSDs, reaching up to 28 GB/s sequential reads and 5.5M random-read IOPS in liquid-cooled environments. These drives anchor Micron's storage business built on its vertically integrated 3D NAND.
Most recent patents
500 patents on file, but none with both an extractable figure and an abstract on Google Patents yet.
