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Tuning and Inference for Generative AI with 4th Generation Intel Xeon Processors (Part 1 of 3)

Mohan_Potheri
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Introduction to Generative AI Models and CPU Tuning:

 

Generative Artificial Intelligence (AI) models represent a revolutionary leap in the field of machine learning, enabling computers to generate new content that closely mimics human-created data. These models, particularly those based on architectures like GPT (Generative Pre-trained Transformer), have demonstrated remarkable capabilities in tasks such as natural language processing, image generation, and even music composition.

At the heart of these generative models lies the intricate dance between algorithms and hardware. While much emphasis has been placed on utilizing powerful GPUs for training deep neural networks, the role of Central Processing Units (CPUs) in fine-tuning and optimizing generative AI models should not be underestimated. CPU tuning is a crucial aspect of achieving efficient performance and ensuring that these models can be seamlessly integrated into a variety of computing environments.

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Figure 1: Generative AI Models (Image Source: eweek)

In this blog series, we look at the relationship between generative AI models and CPU tuning. We will examine the significance of CPU optimization in enhancing the inference speed, responsiveness, and overall efficiency of these models. As the computational landscape evolves, understanding how to harness the full potential of CPUs in conjunction with generative AI models becomes imperative for researchers, developers, and practitioners alike.

Join us on a journey through the intricate interplay of cutting-edge AI algorithms and the foundational hardware that powers them, as we uncover the nuances of tuning CPUs to unlock the true potential of generative AI models.

Advantages of Tuning Generative AI models:

 

Tuning general-purpose generative AI models offers a range of advantages that significantly enhance their performance, adaptability, and applicability across various domains. Here are some key benefits of tuning such models:

  1. Optimized Performance: Tuning allows practitioners to fine-tune the hyperparameters and configurations of generative AI models to achieve optimized performance. This process can lead to improvements in both training and inference speed, making the models more efficient and responsive.
  2. Domain-Specific Adaptability: General-purpose models may not excel in specific domains or tasks out of the box. Tuning enables the customization of these models for particular applications, ensuring they adapt well to the nuances and requirements of specific industries or use cases.

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Figure 2: Fine Tuning AI Models (Source: Product Coalition)

  1. Resource Efficiency: Generative AI models often demand substantial computational resources during training. Tuning can help strike a balance between model complexity and resource utilization, making it feasible to deploy and run these models on hardware with varying capabilities, including CPUs.
  2. Scalability: Tuning allows for the adjustment of model parameters to accommodate different scales of data and tasks. This scalability is crucial for deploying generative AI models across diverse applications, from small-scale tasks to large-scale, enterprise-level solutions.
  3. Reduced Latency: By optimizing parameters for inference, tuning can significantly reduce the latency associated with generating responses. This is particularly important in real-time applications such as chatbots, virtual assistants, and other interactive systems where quick and accurate responses are essential.
  4. Cost-Efficiency: Tuning can help strike a balance between model accuracy and computational cost. This is vital for organizations looking to implement generative AI solutions cost-effectively, especially in cloud computing environments where resource usage directly impacts operational expenses.
  5. Improved Robustness and Generalization: Tuning facilitates the enhancement of a model's robustness and generalization capabilities. By fine-tuning on diverse datasets and adjusting hyperparameters, models can better handle a variety of inputs and perform well in real-world scenarios.
  6. Adherence to Ethical and Regulatory Standards: Fine-tuning allows practitioners to incorporate ethical considerations and ensure that generative AI models adhere to regulatory standards. This is particularly important in sensitive domains, such as healthcare or finance, where compliance with privacy and security regulations is paramount.
  7. Facilitation of Transfer Learning: Tuning general-purpose models is essential for effective transfer learning, enabling the application of knowledge gained from one task to improve performance on a different but related task. This can save significant computational resources and time when deploying models in new contexts.

In summary, tuning general-purpose generative AI models is a crucial step in unlocking their full potential, tailoring them to specific needs, and ensuring their seamless integration into diverse applications and environments.

4th Gen Intel(R) Xeon(R) processors for Tuning and Inference of Large Language Models (LLM):

 

The 4th Gen Intel(R) Xeon(R) processors[i] (previously codenamed Sapphire Rapids) are well-suited for distributed AI training because they offer many advantages, including:

  • High performance: The latest generation processors offer significant performance improvements over previous generations, thanks to the new architecture and advanced features, making them an ideal choice for training large and complex AI models.
  • Scalability: The 4th generation Intel Xeon Scalable processors can be scaled to meet the needs of any training workload, from small research projects to large production deployments. They can be used to build clusters of hundreds or even thousands of machines, which can be used to train the largest and most complex AI models.
  • Cost-effectiveness: The 4th generation Intel Xeon Scalable processors are a cost-effective solution for distributed AI training. They offer a good balance of performance and price, and they are supported by a wide range of software and hardware vendors.
  • Intel Optimizations: Intel provides a suite of software optimization tools, such as Intel(R) oneAPI Toolkit and Intel Distribution for Python, that further enhance the performance of distributed AI training on Intel Xeon processors.
  • Memory Capacity: Intel Xeon processors support large memory capacities, enabling efficient handling of massive datasets used in distributed AI training.

While the advantages above are significant, the 4th Gen Intel Xeon processors also offer advanced features ideal for distributed AI training including:

  • Intel Advanced Matrix Extensions (Intel® AMX): Intel AMX is a new instruction set that accelerates matrix multiplication and other operations that are commonly used in AI training. This can lead to significant performance improvements for AI training workloads.
  • Intel® In-Memory Analytics Accelerator (Intel® IAA): Intel IAA is a new hardware accelerator that can improve the performance of memory-intensive workloads, such as AI training workloads.
  • Intel® Deep Learning Boost (Intel® DL Boost): Intel DL Boost is a suite of technologies that accelerate deep learning workloads on Intel Xeon Scalable processors. This includes support for popular deep learning frameworks, such as TensorFlow, PyTorch, and MXNet.

Overall, the 4th generation Intel Xeon Scalable processors are a great choice for distributed AI training because they offer high performance, scalability, cost-effectiveness, and several features that can be specifically beneficial for distributed AI training.

Intel C7i instances on Amazon EC2:

 

Amazon Elastic Compute Cloud (Amazon EC2) C7i[ii] instances represent the latest advancement in compute-optimized solutions, featuring state-of-the-art custom 4th Generation Intel Xeon Scalable processors, codenamed Sapphire Rapids. These instances boast a compelling 2:1 ratio of memory to virtual CPU (vCPU). Exclusively available on AWS, EC2 instances equipped with these tailored processors outperform comparable Intel processors in the cloud, delivering a remarkable up to 15% improvement in performance compared to those used by other cloud providers.

The C7i instances not only excel in performance but also offer up to 15% enhanced price performance when compared to their predecessors, the C6i instances. Tailored to meet the demands of compute-intensive workloads, C7i instances are well-suited for a diverse range of applications, including batch processing, distributed analytics, high-performance computing (HPC), ad serving, highly scalable multiplayer gaming, and video encoding. Whether handling complex analytics or powering resource-demanding applications, C7i instances on Amazon EC2 provide a powerful and cost-effective solution for a variety of computing needs.

The inclusion of C7i instances expands the already extensive range of EC2 instances available on AWS, offering a diverse selection to cater to various computing needs. C7i introduces 11 sizes, encompassing a comprehensive range that includes two bare-metal configurations: c7i.metal-24xl and c7i.metal-48xl. These instances come in varying specifications, providing flexibility in terms of virtual CPU (vCPU), memory, networking, and storage to accommodate a wide array of application requirements.

 

In part 2 of this blog series, we will look at taking a public domain LLM and tuning it with Amazon EC2 C7i instances for a specific use case.

References:

[i] https://www.intel.com/content/www/us/en/products/docs/processors/xeon-accelerated/4th-gen-xeon-scalable-processors-product-brief.html : With the most built-in accelerators of any CPU on the market, Intel® Xeon® Scalable processors offer the most choice and flexibility in cloud selection with smooth application portability.

[ii] https://aws.amazon.com/ec2/instance-types/c7i/ : Amazon Elastic Compute Cloud (Amazon EC2) C7i instances are next-generation compute optimized instances powered by custom 4th Generation Intel Xeon Scalable processors (code named Sapphire Rapids) and feature a 2:1 ratio of memory to vCPU. EC2 instances powered by these custom processors, available only on AWS, offer the best performance among comparable Intel processors in the cloud – up to 15% better performance than Intel processors utilized by other cloud providers.

About the Author
Mohan Potheri is a Cloud Solutions Architect with more than 20 years in IT infrastructure, with in depth experience on Cloud architecture. He currently focuses on educating customers and partners on Intel capabilities and optimizations available on Amazon AWS. He is actively engaged with the Intel and AWS Partner communities to develop compelling solutions with Intel and AWS. He is a VMware vExpert (VCDX#98) with extensive knowledge on premises and hybrid cloud. He also has extensive experience with business critical applications such as SAP, Oracle, SQL and Java across UNIX, Linux and Windows environments. Mohan Potheri is an expert on AI/ML, HPC and has been a speaker in multiple conferences such as VMWorld, GTC, ISC and other Partner events.