Micron memory and storage accelerates AI success
AI is unlocking the insights hidden within massive data lakes to help your business run (and grow) more efficiently, securely, and smarter than your competition.
Micron server DRAM and SSDs play a critical role in improving AI use cases
Reduce time to train AI models, minimize AI-related compute costs and improve AI inferencing accuracy for key AI use cases with Micron server DRAM and data center SSDs
Computer vision
AI’s ability to process images and video for facial recognition, tracking theft, fraud prevention and more requires massive amounts of high-bandwidth, low-latency memory to ensure that data is processed quickly enough.
Natural language processing (NLP)
Natural language processing needs an efficient flow of data to prevent backups from allowing the compute process to focus on background tasks during gaps. AI’s personal assistants, for instance, can’t afford to “multitask.” Fast, efficient memory and storage is critical for accelerating processing.
Predictions and forecasting
Predicting business outcomes, finding products that customers might be interested in and forecasting trends requires massive amounts of capacity and low-latency memory and storage to quickly analyze vast amounts of data to find patterns.
Micron DRAM and SSDs pave the way for AI perfection
AI applications can be accelerated with the right server memory and storage. Download this infographic to learn which solutions are the right fit for faster training and insights, taming growing LLMs and improving power efficiency.
Micron has the memory and storage you need to turn unmined data into business gold
AI offers a wealth of information when it tames your data. Micron’s memory and storage solutions can make AI more efficient in training and then transforming your data into insights.
96GB DDR5 helps accelerate AI while minimizing costs
AI inference demands fast memory with large amounts of capacity to store trained models while feeding the data to CPUs and GPUs for processing. However, not all workloads require 128GB DDR5 capacities, and you can achieve comparable throughput with 96GB DDR5 at a lower cost.
Keep expensive GPUs and CPUs from idling
Training AI takes time, but there are ways to reduce this by making the process more efficient. A particular problem that AI runs into is idle during the training flow. Test data shows that Micron 9400 NVMe SSDs solve this challenge to keeps expensive GPUs and CPUs from idling.
6500 ION NVMe SSD
Quickly feeding volumes of data from networked data lakes can significantly reduce the idle time for costly GPUs and improve AI investment returns. Micron’s high-capacity 6500 ION NVMe SSDs are engineered to ingest massive data lakes significantly faster than competitor’s latest capacity-focused SSD7.
9400 and 7450 NVMe SSDs
Accelerating the ingest and flow of massive datasets is necessary to shorten both AI training times and the time to valuable insight. Micron’s high-performance 9400 and mainstream 7450 NVMe SSDs are ideal for local persistent storage caching to achieve simultaneous ingest and training.
AI and machine learning
Micron’s industry-leading memory and storage solutions enable the latest generation of faster, more intelligent global infrastructures that make AI, machine learning and generative AI possible. From collaborative robots to personal AI companions, complex AI models are created from data, and data lives in Micron solutions.
Your top seven AI architecture questions answered from the ground up
To help you understand how memory and storage support the AI revolution
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Aren’t the CPUs and GPUs doing all the heavy lifting in AI? Does it really matter which memory and storage products I use in my AI servers?
Answer: While CPUs and GPUs are essential components of AI servers, they aren’t the only components to consider. Memory and storage products can substantially affect outcomes.
CPUs and GPUs (the “compute” parts of any AI server) don’t directly manage the large datasets needed for AI. In fact, the CPUs and GPUs rely on fast memory and storage to store and manage training data, ensuring that it’s fed to the compute elements quickly and consistently.
If the dataset ingest backs up, it can slow the training process, incurring additional costs due to CPU and GPU underutilization. Advanced memory and storage offer the capacity and throughput to help ensure AI runs continuously, efficiently, and smoothly.
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How can I reduce the time to train my AI models?
Answer: Micron’s AI product offerings are optimized for complex AI training workloads.
Fast, Micron DDR5 memory delivers local data access to compute with double the bandwidth, burst length, number of bank groups, banks, and concurrent operations1. DDR5 enables 7x results for AI performance2, faster image classification for computer vision, and improved define and recognize for low-level, mid-level, and higher-level categories. It also improves the speed of light/dark identification, accelerating face recognition2.
Local SSD cache accelerates data access to GPUs. In addition, networked data lakes benefit from capacity-focused SSDs to help ensure that multiple AI servers are fed with the data they need. Micron offers the PCIe Gen4 SSD performance leader for AI, helping avoid storage bottlenecks and data backups that slow AI training time3.
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Large language models (LLM) with trillions of parameters can lead LLM inference to be memory bound. How do I overcome this challenge?
Answer: “Memory bound” refers to data processing backing up which can be caused by not providing sufficient bandwidth in DRAM or storage. For example, Micron engineers analyzed the effects of storage performance in AI training and found that higher performance storage enabled better results4. Advanced memory and storage can enhance continuous data flow and help reduce wait times for expensive GPUs and CPUs.
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CPUs and GPUs are the most expensive components in my AI servers. How can I get the most out of this investment and ensure I’m not underutilizing compute resources?
Answer: Feeding a steady stream of data from fast memory and storage utilizes CPUs and GPUs with the most efficiency. Choosing memory and storage without this consideration may result in bottlenecks and increased costs.
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Training complex AI models may require many GPUs and CPUs which are becoming more power hungry with each generational update. What hardware optimizations can I make to reduce power consumption?
Answer: There are two ways to analyze power consumption: Overall consumption and consumption efficiency. The former is a simple measure of the total power consumed by the platform, while the latter analyzes power consumed for productive work. The latter is commonly referenced in a total cost of ownership (TCO) calculation5.
Micron has designed advanced memory and storage products with energy efficiency to help reduce power consumption while providing high performance. This same solution addresses the increasing cost (and potential environmental impact) of huge electricity use. Micron incorporates a “feed-right balance” that helps ensure energy efficiency while powering the advanced needs of AI.
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How can I improve the accuracy of AI inferencing?
Answer: Memory resources with high throughput enable better accuracy in AI inferencing. Performance-based CPUs are also required, but memory is essential to their optimal operation.
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In general, how large of a dataset is needed to train an AI model?
Answer: According to NVIDIA (a widely-recognized leader in AI technology), “….Training any AI model requires carefully labeled and diverse datasets that contain thousands to tens of millions of elements, some of which are beyond the visual spectrum. Collecting and labeling this data in the real world is time-consuming and expensive. This can hinder the development of AI models and slow down the time to solution…”6.
- See https://www.micron.com/about/blog/2022/november/boost-hpc-workload-performance-with-micron-ddr5-and-amd-zen-4-cpu for additional information.
- Micron DDR5 with Intel AMX delivers 5-7x performances for recommender, training and vision workloads, see www.micron.com/intel. Also see https://towardsdatascience.com/why-deep-learning-is-needed-over-traditional-machine-learning-1b6a99177063 for additional information on deep learning model transitions among lower (letters), middle (words), and higher-level (sentences) categories.
- The Micron 9400 SSD is ranked #1 in the MLCommons storage performance benchmark ranking: https://mlcommons.org/en/storage-results-05/.
- See https://media-www.micron.com/-/media/client/global/documents/products/white-paper/micron_9400_nvidia_gds_vs_comp_white_paper.pdf for complete test results.
- See https://www.snia.org/education/online-dictionary/term/total-cost-of-ownership.
- See https://www.nvidia.com/en-us/omniverse/synthetic-data/.
- Based on the comparison 100TB ingest time calculated from the 128KB sequential write specs in the public product briefs for the Micron 6500 ION SSD and the Solidigm D5-P5430)