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Micron technology helps usher in the AI data center of the future
- Near memory
- Main memory
- Expansion Memory
- Local SSD data cache
- Networked data lakes
Unlocking the potential of AI data centers
Within every AI server box lives a pyramid or hierarchy of memory and storage to support fast groundbreaking AI. When built with Micron’s leading technology, data center bottlenecks are reduced, sustainability and power efficiencies are increased, and the total cost of ownership is improved.
Interact with the pyramid to explore our AI portfolio
Take a peek inside the box
The most advanced boxes on the planet, still need a planet
Frequently asked questions
AI workloads demand high computational power and generate substantial heat, necessitating robust infrastructure updates. Consequently, modern AI data centers are designed with cutting-edge cooling technologies, renewable energy sources, and optimized layouts to ensure maximum performance and sustainability. Additionally, selecting appropriate CPUs and GPUs is crucial, as AI applications often rely on specialized hardware to handle complex computations efficiently. This careful selection helps maximize processing power while minimizing energy consumption, further contributing to the overall efficiency and effectiveness of AI data centers.
Micron’s HBM3E 8-high 24GB and HBM3E 12-high 36GB deliver industry-leading performance with bandwidth greater than 1.2 TB/s and consume up to 30% less power than any other competitor in the market.
When it comes to AI data and machine learning data workloads, memory plays a crucial role in determining the overall performance of the system. Two prominent types of memory that are often considered for AI data and machine learning data workloads are high-bandwidth memory (HBM) and double data rate (DDR) memory, specifically DDR5. Which memory is right for an AI training data workload depends on various factors, including the specific requirements of the AI model training algorithms, the scale of automated data processing and the overall system configuration. Both HBM3E and DDR5 offer significant advantages, and their suitability depends on the specific AI memory use case, budget and available hardware options. Micron offers the latest generation of HBM3E and DDR5 for AI model training.
HBM3E memory is the highest-end AI model training solution in terms of bandwidth, speed and energy efficiency due to its advanced architecture and high-bandwidth capabilities. DDR5 AI training memory modules are generally more mainstream and cost-effective at scale than HBM solutions.
If total capacity is the most important factor for your AI workloads, Micron CZ120 memory expansion modules leverage the CXL standard to optimize performance beyond direct-attach memory channels.
The ideal machine learning data and AI model storage solution depends on several factors. Key considerations should include speed, performance, capacity, reliability, endurance and scalability. The best intelligent storage solution for AI workloads depends on the specific demands of your applications, your budget and your overall system configuration. Micron can offer the best-in-class NVMe SSDs for your specific machine learning data and AI model storage needs. The Micron 9550 NVMe SSD is the world’s fastest data center SSD, built with industry leading innovation to deliver superior PCIe® Gen5 performance, flexibility and security for AI and beyond. The Micron 6500 ION NVMe SSD is the ideal high-capacity solution for networked data lakes.
1 As compared to previous 1α node generation.
2 Based on JEDEC specification.
3 Measured data in pJ/bit compared to commercially available (June 2023) competitive 3DS modules.
4 Empirical Intel Memory Latency Checker (Intel MLC) data comparing 128GB MRDIMM 8800MT/s against 128GB RDIMM 6400MT/s.
5 Empirical Stream Triad data comparing 128GB MRDIMM 8800MT/s against 128GB RDIMM 6400MT/s at 1TB.
6 Empirical OpenFOAM task energy comparing 128GB MRDIMM 8800MT/s against 128GB RDIMM 6400MT/s.
7 Compared to LPDDR5X 8533 Mbps
8 Compared to previous generation
9 MLC bandwidth using 12-channel 4800MT/s RDIMM+ 4x256GB CZ120 vs. RDIMM only.
10 Performance comparisons are based on publicly available data information for performance-focused Gen5 SSDs with 1 DWPD 7.68TB capacity SSDs available at product launch. Sequential and random throughput at QD512. Several public sources note big accelerator memory (BaM) such as: https://www.tomshardware.com/news/nvidia-unveils-big-accelerator-memory-solid-state-storage-for-gpus and GPU-initiated direct storage (GIDS) for graph neural network (GNN) training workloads using NVIDIA H100 GPU as tested in Micron's labs against performance-focused Gen5 SSDs.
11 Checkpoint workload modeled on Llama3 405B parameter LLM. The model represents an 8 GPU server. Checkpoint size is 415GB. Figures represent the time required for one checkpoint, the SSD energy consumed for one checkpoint, and the SSD throughput during checkpoint operations compared to the Solidigm D5-P5336. See Micron 6550 ION SSD AI Tech Brief for details.
12 These comparisons use publicly available competitor information from published sources at the time of the 6550 ION launch, with the 6550 ION using a maximum power of 20W and competitive drives using 25W, resulting in up to 20% less maximum power consumption for the 6550 ION.
13 The Micron 6550 ION offers a capacity of up to 61.44TB. E3.S-capable servers can be configured with up to 20 SSDs in 1U.