AI AT THE EDGE
Make AI faster, more cost-effective at the edge
Shorten the distance between data creation and success
Win the AI race with edge computing
In the race to grow business with AI, edge computing offers a strong competitive advantage.
An effective cloud-to-edge AI strategy can reduce latency, optimize GPU utilization, improve data security and reduce the cost and power associated with transporting data to the cloud.
Proven solutions for AI at the edge
10 considerations for a winning cloud-to-edge AI strategy
Choose use cases that optimize GPU usage, data egress, and power consumption
Accelerate smart retail with computer vision and high performance memory
Leverage solutions from Micron and AHEAD to help ensure the future of retail is safer
The data center of the future
See how hybrid models combine centralized data centers and edge infrastructure
Accelerate AI at the edge
Enable efficient AI model training offload
Decrease GNN training workload completion times while lowering system energy.
Choose the best DRAM for your workloads
Select the right memory to improve server performance, which translates to better real-world results.
Optimize mainstream server applications
Improve performance, latency and response times in mainstream data centers.
Expand capacity for data lakes and cloud storage
Gain faster access to large datasets for AI/ML training and other resource-intensive tasks.
Edge AI storage solutions
Find purpose-built storage solutions to match your edge workloads
Micron 6500 ION NVMe SSD
Unleash the potential of massive data lakes with high-capacity solutions
Edge AI memory solutions
Maximize edge servers with your ideal memory configuration
Find your fit
No matter your edge AI workload, Micron has the right server solution to exceed expectations
| NVMe SSD Series/Model | Form Factor | Capacity | Edge | Cloud |
|---|---|---|---|---|
| 9550 MAX 9550 PRO |
U.2 (15mm) E3.S (7.5mm) |
3.2TB to 25.6TB 3.84TB to 30.72TB |
• Real-time AI inferencing • Data aggregation and preprocessing • NLP and computer vision |
• AI model training • High-performance computing • Graph Neural Network (GNN) training |
| 7500 MAX 7500 PRO |
U.3 (15 mm) | 0.80TB to 12.80TB 0.96TB to 15.36TB |
• Edge AI training • IoT data management • NLP |
• Cloud storage • Big data • High-volume OLTP |
| 6500 ION | U.3 (15 mm) | 30.72TB | • Model storage • Content delivery • Data aggregation and analytics |
• AI data lakes • Big data • Cloud infrastructure |
| DRAM | Form Factor | Speed (MT/s) | Densities (GB) |
|---|---|---|---|
| DDR5 | RDIMM | 4800, 5600, 6400 | 16, 24, 32, 48, 64, 96, 128 |
Support
Buy server solutions
Purchase Micron Server DRAM and SSDs through one of our valued partners.
Contact sales
Learn how Micron can help you optimize memory and storage for your systems.
TEP (Technology Enablement Program)
TEP (Technology Enablement Program)
TEP for DDR5 offers a path into Micron to gain early access to technical information and support.
Resources
TECH BRIEF
Tips to get the most from your GPUs
Solve AI challenges to avoid under or over utilizing costly GPUs
Ebook
Solve your data center challenges
Overcome obstacles and lay the groundwork for sustainable growth
FLYER
5 reasons to choose Micron’s server solutions
Partner with Micron to reduce the strain on your engineers
FAQs
Learn more about Micron’s solutions for AI at the edge
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Why move AI workloads to the edge?
AI and the edge fit together naturally, since moving AI workloads to the edge can provide real-time insights, reduce data transport costs, and lower power consumption. Moving select workloads to the edge can meet and exceed your leadership’s expectations of what AI can do for your business.
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How do I accelerate AI at the edge with low-latency server solutions?
Implement advanced memory and storage architectures that reduce model retraining time and improve inferencing accuracy. This way, you can accelerate critical edge AI workloads like NLP, predictions, personalization, and computer vision.
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What are some examples of edge AI use cases?
Edge AI use cases are chosen to optimize GPU usage, data egress, and power consumption. Examples include:
- Smart retail: Analyze customer behavior, manage inventory, and personalize shopping experience
- Computer vision: Gain real-time processing and low latency for computer vision workloads
- Predictive maintenance: Monitor devices to help prevent equipment failures and minimize downtime
- NLP: Enhance interactions between humans and machines with real-time inferencing
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What are some considerations for deciding which workloads to move to the edge?
Latency: For some workloads, moving to the edge can reduce latency, which in turn can improve customer experiences, make safer work environments, decrease downtime, and provide real-time insights. Other workloads don’t rely as heavily on low-latency performance, making them more suitable for the cloud.
Data transport: Cloud bills can skyrocket if the volume of data transport gets too high. Edge AI can reduce the strain by processing most of the data locally, and only transferring the essentials to the cloud. With this strategy, you can reduce the requirements and congestion of your network.
Resource efficiency: Lightweight workloads can often be moved to the edge to run more efficiently. At the same time, deploying edge AI devices can be costly, leading to compromises about how to balance performance and efficiency.
Security: Cloud systems can provide suitable security for a range of workloads. However, there are some situations where edge servers provide a necessary extra layer of security to comply with security regulations. -
Are there regulations to consider?
In regions where data sovereignty laws dictate that data must remain within national borders, edge computing may be a legal obligation.
Processing and storing data locally helps you stay compliant with regulatory requirements while implementing new AI applications. This is particularly important in industries like finance and healthcare, where data integrity can have major ramifications. -
How can I overcome lack of in-house AI expertise?
Collaborate with Micron’s ecosystem experts to develop a cloud-to-edge strategy that harnesses the power of your data, wherever it lives. Micron rigorously tests and optimizes AI workloads across diverse platforms, ensuring seamless performance and scalability for AI-powered edge applications. We also work closely with customers at engineering sites across the country to streamline processes and reduce the load on your engineering teams.
Note: All values provided are for reference only and are not warranted values. For warranty information, visit https://www.micron.com/sales-support/sales/returns-and-warranties or contact your Micron sales representative.