As an AI researcher, you’re always looking for the most powerful infrastructure to train your complex deep learning models.
Intel servers know you need blazing-fast performance on a massive scale to push the boundaries of what’s possible with artificial intelligence.
Their latest generation of IntelXeonScalable processors and OptaneTM persistent memory are specifically engineered from silicon up for the unique demands of AI workloads.
In this blog post, we’ll take an inside look at 10 key ways Intel servers are optimized to accelerate your AI and maximize your productivity.
1. Parallel Processing Power
Intel CPUs are equipped with many processing cores that can handle multiple tasks simultaneously. Whether you’re training large neural networks or running real-time inference, parallel processing allows Intel servers to significantly reduce the time it takes to complete jobs. Their multi-core architectures are well-suited for the parallel nature of common AI operations like matrix math. So you can get faster results without having to spend a fortune on GPU accelerators.
2. Built-In Deep Learning Boost
Intel’s latest Xeon Scalable processors come with DL Boost, a set of instructions optimized for deep learning. DL Boost includes technologies like vector neural network instructions that can accelerate common layers like convnets by up to 21x compared to a non-optimized baseline. With DL Boost on an Intel server, you can train models much more efficiently without having to worry about managing different hardware accelerators. Your code will also be portable across CPUs with this common set of instructions.
3. Flexible Configuration Options
Intel offers a wide variety of server CPUs, from low-power models up to processors with dozens of cores. They also support platforms ranging from small form factors to multi-socket configurations.
- Intel offers a wide range of server CPUs to choose from for configuring an AI system. This includes low-power Atom, Pentium and Celeron chips up to high-end Xeon Scalable processors.
- Xeon Scalable processors range from dual-core models up to 28-core chips, allowing you to right-size CPU power.
- These CPUs support one or two processor configurations via standard server motherboards and chassis. This allows scaling-out processing via multi-socket towers or scaling up in compact 2U or 4U racks.
- For scale-out AI clusters, you can mix single and dual-CPU nodes to optimize cost and density within a rack. This provides flexibility to grow the cluster over time.
- Intel server platforms also vary from small form factors to standard 1U and 2U racks. This enables AI hardware to fit any environment, from small edge sites to large data centers.
- Modular server designs allow easy serviceability and field-upgradeability. Components like CPUs, memory, and drives can be swapped without downtime.
- Intel Xeon CPUs are supported across various server platforms from all major ODMs like Dell, HPE, Lenovo and more. This provides supply-chain flexibility.
- If AI demands change, it is easy to reconfigure existing Intel servers. For example, simply upgrading CPUs or adding GPUs, memory, or SSDs.
The ability to flexibly scale, reconfigure, and optimize Intel-based AI hardware for any need or budget provides long-term flexibility and sustainability.
4. Abundant Memory and Fast Storage
Intel Xeon Scalable processors can access massive amounts of system memory, with some models supporting up to 6TB per socket. They also feature integrated memory controllers for low-latency access. Coupled with fast Intel Optane persistent memory, Intel servers give your models room to grow without performance bottlenecks.
They also support high-speed networking and storage technologies like Intel Optane DC SSDs and Intel SSDs to quickly load and save massive datasets.
5. Built-in Security Features
As an AI developer, data security is paramount. Intel processors have security features like Intel Software Guard Extensions (SGX) that allow confidential computation on untrusted platforms. SGX creates protected memory regions where data is encrypted and access is controlled. For AI models that could reveal sensitive training data, SGX provides added protection without compromising performance. Other features help defend against common threats like malware and side-channel attacks.
6. Excellent Performance Per Watt
Power efficiency is critical for large-scale AI deployments. Intel Xeon Scalable CPUs are designed for high-performance computing while maintaining low power consumption. Their performance per watt easily outpaces comparable GPU-based servers. This means lower cooling and electricity costs if you need to run inference or training 24/7. It also enables more nodes to be powered in dense cluster configurations for greater aggregate throughput within the same power envelope.
7. Vast Software Ecosystem
Intel has worked closely with OS and framework developers to ensure broad compatibility and optimization for their hardware. Ecosystems like Linux, Windows Server, TensorFlow, PyTorch, MXNet, Caffe and more are thoroughly tested and supported on Intel Xeon platforms. You can develop and deploy using the tools and libraries you already know how to use. A large community of developers also contributes optimized modules to accelerate workloads.
8. Simple Management
Intel servers provide management tools that make it easy to monitor resource utilization, deploy models, and update firmware and drivers on large fleets of Intel-based servers. Tools like Intel Data Center Manager allow you to easily set up clusters and orchestrate jobs. Remote management capabilities help minimize on-site visits for maintenance. Intel Xeon servers also integrate with all major public clouds, private clouds, and virtualization platforms for flexible deployment.
9. Robust Support
As an Intel customer, you benefit from the company’s global support network. Experts are available to assist with setup, configuration or troubleshooting. Extended hardware warranties provide peace of mind. And the large installed base of Intel Xeon means any issues can usually be resolved quickly thanks to a vast knowledge base. If reliability is important for your production workloads, few vendors can match Intel’s track record.
10. Long-Term Investment
Intel continues to heavily invest in advancing AI performance through hardware, software and research collaborations. Future Intel Xeon platforms will offer significant performance gains through architectures like Intel OneAPI, Intel Advanced Matrix Extensions, and Intel Xe graphics.
So as your AI needs grow over time, Intel servers can continue to efficiently power them. You reduce the risk of technology lock-in by partnering with a leader committed to the long haul.
Final Words
As an AI leader, your top priority is achieving the best results as fast as possible while maintaining a competitive advantage. Intel’s end-to-end optimized solutions are purpose-built exactly for these AI-first workloads, from the silicon up. Their platforms provide the performance, scalability, flexibility and manageability to accelerate your research and give you an edge in developing the next wave of transformative AI applications.