5 common AI myths debunked
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AI is transforming how we work. Organizations want to reduce costs and move quickly to create new opportunities using state-of-the-art AI models for language, text-to-image, and code generation. But many AI applications never make it to deployment. Can you identify the misconceptions around AI versus the truth? Take our quiz to test your knowledge!
AI can be achieved with CPUs.
1/5
You can use all the latest Intel® Xeon® processors for AI.
• With AI acceleration in every core, Intel Xeon® processors are ready to handle your demanding AI workloads before you need to add discrete accelerators. • The Intel® Advanced Matrix Extensions (Intel® AMX) built-in accelerator significantly improves deep learning training and inference. • Built-in accelerators help increase the capacity for AI, data analytics, networking, storage, and high-performance computing (HPC), so you can do more with the same number of cores if working on-premises and virtual cores if using cloud.
Next question
https://edc.intel.com/content/www/us/en/products/performance/benchmarks/overview/
1
True or False
True
False
You need to build AI models from scratch.
2/5
Intel has dozens of pre-trained, optimized AI models that are ready out of the box, easy to customize, and designed to dramatically speed your development time and help you achieve better results.
• Intel® Software Development Tools give developers the freedom to migrate code across different hardware architectures and vendors without costly and time-consuming challenges. • And there’s no need to wait for hardware, thanks to the largest ecosystem and supply availability, a familiar toolset, and the ability to scale across your cloud, data center, and edge. • Developers can find recipes, benchmarks, and other resources on how to optimize key workloads at the Intel Developer Cloud.
The correct answer was true.
Migrating servers running on 1st Gen Intel Xeon processors to servers using 4th Gen Intel Xeon Scalable processors can lower fleet power from 1.1M kWh to 458 kWh, resulting in a 60% reduction in power. At the same time, this lowers CO emissions from 472,087 kg to 194,242 kg, a reduction of 60%.
2
3
Your data isn’t safe if you deploy AI in the cloud.
3/5
Intel is building platforms and technologies for customers to confidently help secure diverse AI workloads across the data center, cloud, PC, and edge.
• Intel’s confidential computing with trusted execution environments (TEEs) help protect data and AI models, even when working with sensitive or regulated data. • With Intel, you can choose from the most deployed options on the market today—now including application or virtual machine (VM)-level isolation. This enables you to match a solution to specific business and regulatory needs.
The correct answer was 75%.
Moving to servers running 4th Gen Intel Xeon Scalable processors allows for up to 80% reduction in server count and cost. Example: 50 servers based on the 1st Gen Intel Xeon processor cost $1.5 million. But 10 servers based on the 4th Gen Intel Xeon Scalable processor costs $381K, a 75% reduction in TCO.
4
You can enable machine learning quickly.
4/5
Classical machine learning (ML) plays a crucial role in HPC and AI applications, from life sciences to finance to academic research. With Intel, enabling ML is fast and easy.
Moving from servers based on 2nd Gen Intel Xeon processors to those using 4th Gen Intel Xeon Scalable processors can:
• With large memory, fast cores, and Intel® Advanced Vector Extensions 512 (Intel® AVX-512), 5th Gen Intel® Xeon® Scalable processors deliver excellent machine learning training and inference performance. • With the Intel® AI software portfolio, developers can accelerate end-to-end machine learning and data science pipelines. These tools include optimized frameworks, a model repository, Intel® Extension for Scikit-learn, and Intel® Optimization for XGBoost for machine learning, among others.
5/5
Building a dedicated system for AI is expensive and power-hungry.
The Intel® Gaudi® AI accelerator is a power- and cost-efficient alternative to GPUs.
• For more intensive generative AI needs, dedicated AI, or inference and training on larger models, the Gaudi® AI accelerators help expand your CPU-based foundation. What’s more, Gaudi2 continues to be the only viable alternative to NVIDIA’s H100 for AI compute needs, delivering significant price-performance.
Modernization is the first step in freeing up resources to reinvest in growth. It’s also the path to meeting sustainability goals. Intel has a growing and varied portfolio of technologies designed to power the modern data center, drive faster value from your technology investments, and make a lasting difference to the environment.
Results
• For more intensive generative AI needs, dedicated AI, or inference and training on larger models, the Gaudi® AI accelerators help expand your CPU-based foundation.
What’s more, Gaudi2 continues to be the only viable alternative to NVIDIA’s H100 for AI compute needs, delivering significant price-performance.
Intel Gaudi AI Accelerator Gains 2x Performance Leap on GPT-3 with...
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1 https://www.intel.com/content/www/us/en/newsroom/news/intel-gaudi-ai-accelerator-brings-greater-ai-choice.html#gs.375qsq Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex Intel technologies may require enabled hardware, software or service activation. No product or component can be absolutely secure. Your costs and results may vary. © Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.
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Accelerate AI with 4th Generation Intel® Xeon® Scalable Processors
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