Elevate Your AI Expertise with Intel® Gaudi® Accelerators

Intel® Gaudi® Al accelerators are designed to enhance the efficiency and performance of deep learning workloads, particularly in the context of generative AI and large language models (LLMs). Gaudi processors are a cost-effective alternative to traditional NVIDIA GPUs, offering optimized solutions for demanding AI tasks such as large-scale model training and inference. Intel’s Gaudi architecture is specifically engineered to support the growing computational requirements of generative AI applications, positioning it as a highly competitive option for enterprises seeking to deploy scalable AI solutions. This webinar explores the key technical features, software integration, and future advancements of the Gaudi AI accelerators.

See the video: Elevate Your AI Expertise with Intel® Gaudi® Accelerators

Overview of Intel® Gaudi® Al Accelerators

The Gaudi AI accelerator targets generative AI applications such as LLM training and inference, which are highly resource-intensive. Gaudi 2, the second-generation processor, supports a range of optimizations for deep learning, while Gaudi 3, expected between 2024-2025, promises further advancements. The key features of Gaudi 2 include:

• Matrix Multiplication Engine: Specialized hardware for efficient tensor processing.
• 24 Tensor Processor Cores: High throughput for AI workloads.
• 96 GB of On-Board HBM2e Memory: Enables larger model and batch sizes for improved performance.
• Integrated 100G Ethernet Ports: 24 on-chip 100 GbE ports provide low-latency, high-bandwidth connectivity for scaling workloads across multiple accelerators.
• 7nm Process Technology: The 7nm architecture ensures high performance and power efficiency for deep learning tasks​.

These features, especially the combination of high memory bandwidth and integrated networking, make Gaudi 2 well-suited for scalable AI tasks, such as training large models across multiple nodes. Gaudi’s unique approach, with its dedicated on-chip networking, eliminates the need for third-party network controllers, significantly reducing latency compared to other architectures.

Software Stack and Ecosystem

Intel’s Gaudi platform is built to integrate seamlessly with popular AI frameworks like PyTorch*, providing a robust software suite. This software stack is composed of several key components:

• Graph Compiler and Runtime: Converts deep learning models into executable graphs optimized for the Gaudi hardware.
• Kernel Libraries: Pre-optimized libraries for deep learning operations, minimizing the need for manual optimizations.
• PyTorch* Bridge: Allows PyTorch* models to run on Gaudi accelerators with minimal code modifications.
• Full Docker Support: Users can easily deploy models using pre-configured Docker images, reducing the complexity of setting up the environment​.

Intel also provides extensive support for migrating models from other platforms, such as NVIDIA GPUs, via a GPU migration toolkit. This tool automatically adjusts model code to be compatible with Gaudi hardware, allowing developers to transition without needing significant rewrites of their existing infrastructure.

Generative AI Use Cases and Open Platform for Enterprise AI

A major highlight of the webinar is the introduction of the Open Platform for Enterprise AI (OPEA). The OPEA vision is to “enable enterprises to develop and deploy GenAI solutions powered by an open ecosystem that delivers on security, safety, scalability, cost efficiency, and agility.” It was launched in May 2024 under the aegis of Linux Foundation AI and Data and is fully open source and open governance. It has garnered over 40 industry partners and has a technical steering committee with representatives from hardware companies, software providers, system integrators, and end-users. OPEA allows enterprises to build and deploy scalable AI solutions across various domains, from chatbots and question-answering systems to more complex multimodal models. The platform leverages Gaudi’s hardware optimizations to achieve better performance at a lower cost. Some key use cases include:

• Visual Q&A: A model that can understand and answer questions based on image input, in this case leveraging the powerful LLaVA model for vision-based reasoning.
• The LLaVA model (Large Language and Vision Assistant) is a multimodal AI model that integrates both vision and language to perform tasks requiring visual understanding and reasoning. Essentially, it is designed to answer questions about visual content, such as images, in a manner that combines the strengths of LLMs and vision models.
• LLaVA is built upon large language models, like GPT or others, and extends their capabilities by integrating visual inputs. It typically combines image processing techniques (such as those from Vision Transformers or CNNs) with the natural language understanding and generation capabilities of large language models. This integration allows LLaVA to not only describe images but also to reason about them in a more sophisticated way than purely vision-based models.

• ChatQnA - Retrieval-Augmented Generation (RAG): A state-of-the-art architecture combining large language models with a vector database for enhanced chatbot functionality. This technique reduces hallucinations by ensuring the model retrieves and processes domain-specific information from the knowledge base, maintaining up-to-date and accurate responses​.

OPEA’s modular architecture allows for the customization of microservices, enabling users to swap out different components like databases or models as needed. This flexibility is a key feature, especially in rapidly evolving AI ecosystems where new models and tools frequently emerge.

Gaudi AI Accelerator Roadmap

Intel’s Gaudi roadmap outlines significant performance improvements from Gaudi 2 to Gaudi 3. Some of the key advancements include:

• Doubling AI Compute: Gaudi 3 will provide 2x the floating-point performance for FP8 and 4x for BF16, crucial for handling the growing complexity of models like LLMs.
• Increased Memory Bandwidth: Gaudi 3 will feature 1.5x the memory bandwidth of its predecessor, enabling it to handle even larger models without sacrificing performance.
• Expanded Network Bandwidth: With 2x the networking bandwidth, Gaudi 3 will further reduce bottlenecks in multi-node training scenarios, making it ideal for scaling out workloads across large clusters​.

Additionally, Intel’s upcoming Falcon Shores architecture will integrate Gaudi AI IP with Intel’s GPU technology into a unified GPU form factor. This hybrid architecture is set to offer an even more powerful platform for deep learning, continuing Intel’s push to provide an alternative to traditional GPU-heavy environments.

Deployment and Development Tools

Developers can access Gaudi accelerators via the Intel® Tiber™ Developer Cloud, which provides cloud-based instances of Gaudi 2 hardware. This platform enables users to experiment with and deploy models at scale without needing to invest in on-premises infrastructure.

The steps to get started with Gaudi accelerators are straightforward:

1. Docker Setup: Users begin by setting up Docker environments using pre-built images.
2. Microservices Deployment: Using tools like Docker Compose and Kubernetes, users can deploy end-to-end AI solutions, such as chatbots or visual Q&A systems.
3. Monitoring and Optimization: Intel provides integrated support for monitoring tools like Prometheus and Grafana, allowing users to optimize performance and resource usage across their AI pipelines​.

Conclusion

Intel’s Gaudi processors, along with the comprehensive OPEA framework and software stack, offer a compelling solution for enterprises looking to scale AI workloads efficiently. With Gaudi 2’s high performance and Gaudi 3’s forthcoming enhancements, Intel is positioning itself as a strong competitor in the AI hardware space, providing a cost-effective alternative to traditional GPU-based architectures. The modular and open nature of OPEA, coupled with extensive ecosystem support, enables developers to rapidly build and deploy AI solutions tailored to their specific needs.

We also encourage you to check out Intel’s other AI Tools and framework optimizations and learn about the unified, open, standards-based oneAPI programming model that forms the foundation of Intel’s AI Software Portfolio.

About the Speakers 

Greg Serochi

Intel Gaudi Developer Ecosystem Enabling Manager

Greg is part of the Intel Gaudi Applications Engineering team and manages a team responsible for creating collateral, tutorials, documentation, and training the public on how to use the Intel Gaudi 2 AI Accelerator. The goal for his team is to make it easier for AI Developers to use the Intel Gaudi processor.

Ezequiel Lanza

Open Source AI Evangelist

Ezequiel Lanza is an Open Source AI Evangelist on Intel’s Open Ecosystem team. He is passionate about helping people discover the exciting world of AI. Ezequiel is also a frequent AI conference presenter and creator of use cases, tutorials, and guides to help developers adopt open-source AI tools. He holds an MS in Data Science. Find him on X at @EZE_lanza and LinkedIn at /eze_lanz.

Srinarayan Srikanthan

AI Software Solutions Engineer

Srinarayan is an AI Software Solutions Engineer who works with customers to optimize their Artificial Intelligence and Deep learning workloads on Intel hardware. Prior to joining Intel, he worked in the Healthcare sector for over two years on the application of AI for detection and localization of various ailments. He holds a Master's Degree in Computer Science with specialization in Analytical Healthcare.