#intelgaudi3

Boost Your Knowledge of AI with Intel Gaudi-3 Accelerators

Large language models (LLMs) and generative artificial intelligence (AI) are two areas in which Intel Gaudi Al accelerators are intended to improve the effectiveness and performance of deep learning workloads. Gaudi processors provide efficient solutions for demanding AI applications including large-scale model training and inference, making them a more affordable option than typical NVIDIA GPUs. Because Intel’s Gaudi architecture is specifically designed to accommodate the increasing computing demands of generative AI applications, businesses looking to implement scalable AI solutions will find it to be a highly competitive option. The main technological characteristics, software integration, and upcoming developments of the Gaudi AI accelerators are all covered in this webinar.

Intel Gaudi Al Accelerators Overview

The very resource-intensive generative AI applications, as LLM training and inference, are the focus of the Gaudi AI accelerator. While Intel Gaudi-3, which is anticipated to be released between 2024 and 2025, promises even more breakthroughs, Gaudi 2, the second-generation CPU, enables a variety of deep learning enhancements.

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Intel Gaudi 2

The main attributes of Gaudi 2 consist of:

Matrix Multiplication Engine: Hardware specifically designed to process tensors efficiently.

For AI tasks, 24 Tensor Processor Cores offer high throughput.

Larger model and batch sizes are made possible for better performance by the 96 GB of on-board HBM2e memory.

24 on-chip 100 GbE ports offer low latency and high bandwidth communication, making it possible to scale applications over many accelerators.

7nm Process Technology: For deep learning tasks, the 7nm architecture guarantees excellent performance and power efficiency.

These characteristics, particularly the combination of integrated networking and high memory bandwidth, make Gaudi 2 an excellent choice for scalable AI activities like multi-node training of big models. With its specialized on-chip networking, Gaudi’s innovative design does away with the requirement for external network controllers, greatly cutting latency in comparison to competing systems.

Intel Gaudi Pytorch

Software Environment and Stack

With its extensive software package, Intel’s Gaudi platform is designed to interact easily with well-known AI frameworks like PyTorch. There are various essential components that make up this software stack:

Graph Compiler and Runtime: Generates executable graphs that are tailored for the Gaudi hardware using deep learning models.

Kernel Libraries: Reduce the requirement for manual optimizations by using pre-optimized libraries for deep learning operations.

PyTorch Bridge: Requires less code modification to run PyTorch models on Gaudi accelerators.

Complete Docker Support: By using pre-configured Docker images, users may quickly deploy models, which simplifies the environment setup process.

With a GPU migration toolset, Intel also offers comprehensive support for models coming from other platforms, like NVIDIA GPUs. With the use of this tool, model code can be automatically adjusted to work with Gaudi hardware, enabling developers to make the switch without having to completely rebuild their current infrastructure.

Open Platforms for Enterprise AI

Use Cases of Generative AI and Open Platforms for Enterprise AI

The Open Platform for Enterprise AI (OPEA) introduction is one of the webinar’s main highlights. “Enable businesses to develop and implement GenAI solutions powered by an open ecosystem that delivers on security, safety, scalability, cost efficiency, and agility” is the stated mission of OPEA. It is completely open source with open governance, and it was introduced in May 2024 under the Linux Foundation AI and Data umbrella.

It has attracted more than 40 industry partners and has members from system integrators, hardware manufacturers, software developers, and end users on its technical steering committee. With OPEA, businesses can create and implement scalable AI solutions in a variety of fields, ranging from chatbots and question-answering systems to more intricate multimodal models. The platform makes use of Gaudi’s hardware improvements to cut costs while improving performance. Among the important use cases are:

Visual Q&A: This is a model that uses the potent LLaVA model for vision-based reasoning to comprehend and respond to questions based on image input.

Large Language and Vision Assistant, or LLaVA, is a multimodal AI model that combines language and vision to carry out tasks including visual comprehension and reasoning. In essence, it aims to combine the advantages of vision models with LLMs to provide answers to queries pertaining to visual content, such as photographs.

Large language models, such as GPT or others, are the foundation of LLaVA, which expands their functionality by incorporating visual inputs. Typically, it blends the natural language generation and interpretation powers of big language models with image processing techniques (such those from CNNs or Vision Transformers). Compared to solely vision-based models, LLaVA is able to reason about images in addition to describing them thanks to this integration.

Retrieval-Augmented Generation (RAG) or ChatQnA is a cutting-edge architecture that combines a vector database and big language models to improve chatbot capabilities. By ensuring the model obtains and analyzes domain-specific data from the knowledge base and maintains correct and up-to-date responses, this strategy lessens hallucinations.

Microservices can be customized because to OPEA’s modular architecture, which lets users change out databases and models as needed. This adaptability is essential, particularly in quickly changing AI ecosystems where new models and tools are always being developed.

Intel Gaudi Roadmap

According to Intel’s Gaudi roadmap, Gaudi 2 and Intel Gaudi-3 offer notable performance gains. Among the significant developments are:

Doubling AI Compute: In order to handle the increasing complexity of models like LLMs, Intel Gaudi-3 will offer floating-point performance that is 2 times faster for FP8 and 4 times faster for BF16.

Enhanced Memory Bandwidth: Intel Gaudi-3 is equipped with 1.5 times the memory bandwidth of its predecessor, so that speed won’t be compromised when handling larger models.

Increased Network capacity: Intel Gaudi-3’s two times greater networking capacity will help to further eliminate bottlenecks in multi-node training scenarios, which makes it perfect for distributing workloads over big clusters.

Additionally, Gaudi AI IP and Intel’s GPU technology will be combined into a single GPU form factor in Intel’s forthcoming Falcon Shores architecture, which is anticipated to launch in 2025. As part of Intel’s ongoing effort to offer an alternative to conventional GPU-heavy environments, this hybrid architecture is expected to provide an even more potent foundation for deep learning.

Tools for Deployment and Development

Through the Intel Tiber Developer Cloud, which offers cloud-based instances of Gaudi 2 hardware, developers can utilize Gaudi accelerators. Users can test and implement models at large scale using this platform without having to make investments in on-premises infrastructure.

Starting with Gaudi accelerators is as simple as following these steps:

Docker Setup: First, users use pre-built images to build up Docker environments.

Microservices Deployment: End-to-end AI solutions, such chatbots or visual Q&A systems, can be deployed by users using tools like Docker Compose and Kubernetes.

Intel’s inherent support for monitoring tools, such as Prometheus and Grafana, enables users to manage resource utilization and performance throughout their AI pipelines.

In summary

Enterprises seeking an efficient way to scale AI workloads will find a compelling solution in Intel’s Gaudi CPUs, in conjunction with the extensive OPEA framework and software stack. With Gaudi 2’s impressive performance and Intel Gaudi-3‘s upcoming improvements, Intel is establishing itself as a formidable rival in the AI hardware market by offering a reasonably priced substitute for conventional GPU-based architectures. With OPEA’s open and modular design and wide ecosystem support, developers can quickly create and implement AI solutions that are customized to meet their unique requirements.

Exciting Dell-Intel AI Updates from Dell Technologies World: The second part of 2024 will see Dell PowerEdge XE9680 servers with Intel Gaudi 3 AI accelerators.

At this year’s Intel Vision conference, Intel CEO Pat Gelsinger introduced the Gaudi 3 AI accelerator. Michael Dell attended Pat’s keynote via livestream to announce that Dell Technologies will release the Intel Gaudi 3 AI Accelerator on the Dell PowerEdge XE9680 server later this year.

Dell PowerEdge XE9680

Dell Technologies’ Infrastructure Solutions Group President Arthur Lewis reiterated this in his keynote talk at Dell Technologies World last week.

Dell PowerEdge XE9680 will support Gaudi 3 in the second half of 2024. Before launch, customers can seek quotes from Dell and Dell partners in late June.

With its improved AI acceleration and variety of GPU options, the Dell PowerEdge XE9680 server, which debuted last year, has set the benchmark for purpose-built AI and GenAI computation. Gaudi 3 will improve Dell and Intel’s collaboration by meeting customer infrastructure demands, lowering total cost of ownership, and simplifying deployment when it joins Dell’s server lineup later this year.

This is made possible by an open ecosystem that includes scalable Ethernet-based AI fabrics coupled with AI frameworks tailored for Dell and Gaudi 3.

In order to enable a restricted group of customers to evaluate Intel AI solutions on Dell hardware before implementing them in their data centres, Intel will add Dell PowerEdge XE9680 servers with Intel AI Accelerators to the Intel Developer Cloud prior to the general release of Gaudi 3.

For additional details regarding Dell Technologies’ announcement that the PowerEdge XE9680 series will now include the Gaudi 3.

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High-performance servers like the Dell PowerEdge XE9680 are made to withstand the rigorous demands of contemporary data centres, especially those with applications including high-performance computing (HPC), machine learning (ML), and artificial intelligence (AI).

Below are the main attributes and details of the Dell PowerEdge XE9680

Important characteristics

High Compute Density: Supports up to eight NVIDIA A100 GPUs for AI and HPC computation.

Processor: Latest Intel Xeon Scalable CPUs provide powerful processing and support for huge memory configurations.

Memory: Some configurations allow terabytes of RAM for data-intensive applications.

Storage: Offers NVMe, SSDs, and HDDs for various performance and capacity needs. With many 100GbE ports and other high-speed networking options, can support large-scale computation data flow.

Cooling and Power Efficiency: State-of-the-art cooling techniques to guarantee peak performance and dependability, along with effective power control to minimise running expenses.

XE9680 spec sheet

Form Factor: Server rack, 4U

GPUs: Eight NVIDIA A100 GPUs maximum

CPUs: The most recent generation of Intel Xeon Scalable processors

Memory: Multiple terabyte capacities are supported, including DDR4 or DDR5 support.

Storage: NVMe, SSD, and HDD are supported by several disc bays.

Networking: A number of fast network connectors, including the expansion of 100GbE PCIe slots are available for adding more expansion cards.

Use cases for machine learning and artificial intelligence

Because of its strong GPU capabilities, the XE9680 is perfect for workloads including training and inference in AI and ML.

High-Performance Computing: Appropriate for data analytics, scientific simulations, and other HPC applications needing substantial processing power.

Data Analytics: Able to manage complicated query execution and real-time data processing, among other large-scale data analytics operations.

Virtualization is a flexible option for combining several workloads on a single server since it supports virtualized environments.

Management and Security: For remote monitoring and management, use the Integrated Dell Remote Access Controller (iDRAC).

Hardware and firmware security mechanisms are included to prevent unwanted access and guarantee data integrity.

PowerEdge XE9680

High-performance servers like the Dell PowerEdge XE9680 are made to last in today’s data centres, especially for applications involving machine learning (ML), artificial intelligence (AI), and high-performance computing (HPC). The Dell PowerEdge XE9680 offers the following main advantages:

High Performance CPU Power: The PowerEdge XE9680’s potent CPUs offer remarkable processing power that can effectively handle demanding applications.

GPU Acceleration: The multiple GPU support on this server greatly speeds up work related to AI, ML, and data analytics. For jobs requiring parallel processing capability, it’s perfect.

Equilibrium

Modular Design: Organisations may easily scale their servers by adding more processing power or storage as needed thanks to the server’s modular design.

Flexible Configurations: Businesses can customise the server to meet unique workload requirements thanks to support for a variety of configurations.

Improved Storage

High space: With a significant amount of storage space, the XE9680 is a good choice for applications that require a lot of data. Large-scale AI model training, databases, and big data analytics all depend on this.

High-speed Storage Options: The server guarantees quick data access and transfer rates by supporting NVMe SSDs and other high-speed storage technologies.

Expert Networking

High-bandwidth connectivity: In order to facilitate real-time data processing and analysis, the server is built to accommodate high-speed networking solutions. This guarantees quick data transfer and minimal latency.

Multiple Network Interfaces: It provides a number of network interfaces for load balancing and redundancy, which improves the performance and stability of the network.

The Efficiency of Energy

Optimised Power Usage: By reducing power consumption without sacrificing performance, Dell has integrated energy-efficient technologies into the XE9680, helping to cut operational costs.

Characteristics of Sustainability: It is equipped with features that lessen the carbon impact of data centre operations, all with an eye towards sustainability.

Trustworthiness and Safety

Robust Design: With features like redundant power supplies and cooling systems to guarantee uptime, the server is designed to be extremely dependable.

Advanced Security: Dell’s security features, which include secure boot, silicon-based root of trust, and system lockdown capabilities, shield infrastructure and data from online attacks.

Management Ease

Easy-to-use Management Tools: Dell offers a range of tools that make monitoring, maintenance, and server deployment easier. One such product is the Dell EMC OpenManage line, which provides all-inclusive infrastructure management.

Remote Management: Administrators may control the server from any location with integrated remote management capabilities, which eliminates the need for on-site assistance and streamlines processes.

Services and Support

Expert Support: To guarantee that the server stays functional and effective, Dell provides a wide range of support services, including proactive maintenance and prompt issue resolution.

Training and Resources: To assist IT workers in efficiently managing and utilising the server, Dell offers a range of resources, such as training courses and manuals.

In conclusion, the Dell PowerEdge XE9680 is a strong and adaptable server made to fulfil the most exacting performance requirements of contemporary data centres. It is appropriate for a wide range of demanding applications, from AI and ML to HPC and beyond, thanks to its support for numerous high-end GPUs, current generation CPUs, and an abundance of memory and storage options.

Intel Gaudi 3 AI Accelerator

The Dell PowerEdge XE9680 server has developed into a vital component in the machine learning, deep learning training, HPC modelling, and AI and generative AI acceleration. This server portfolio has advanced significantly with the addition of Intel Gaudi 3 AI Accelerator, which offers an improved set of technological capabilities tailored to difficult, data-intensive tasks. This development offers choices for developers and corporate experts to push the boundaries of GenAI acceleration while accommodating a wider variety of workloads.

Intel Gaudi 3 release date

The AI accelerator Intel Gaudi 3, which debuted on April 9, 2024, is intended primarily for corporate applications, especially those that use generative AI (GenAI). Below is a brief summary of our current understanding:

Gaudi 3 Intel Performance

Provides significant gains in performance over Gaudi 2, its predecessor.

Compared to Nvidia’s H100, a top rival, Intel says it offers 40% greater power efficiency and 50% quicker inference at a more affordable price.

It promises 1.5x more memory capacity, doubled networking bandwidth for large-scale systems, and 4x AI compute for BF16 format.

Emphasis on Generative AI

Gaudí 3 is intended to tackle the difficulties that businesses have while implementing and growing GenAI projects, such as multimodal and large language models (LLMs).

In 2023, just 10% of businesses will have successfully moved GenAI initiatives into production. With Gaudi 3, Intel hopes to close this gap.

Choice and Openness

Gaudi 3’s open architecture is highlighted by Intel, enabling flexible integration with a range of hardware and applications.

In addition to giving businesses greater autonomy, this may lessen vendor lock-in.

Intel Gaudi 3 architecture

Using Silicon Diversities to Create Tailored Solutions

Being the first platform from Dell to combine eight-way GPU acceleration with x86 server architecture, the PowerEdge XE9680 stands out for its exceptional performance in AI-centric operations. This ecosystem’s capabilities are further enhanced with the addition of the Intel Gaudi 3 accelerator, which gives customers the option to customize their systems to meet certain processing requirements, notably those related to GenAI workloads. This calculated inclusion demonstrates a dedication to provide strong and adaptable no-compromise AI acceleration solutions.

Technical Details Increasing Client Success

The XE9680 architecture fosters scalability and dependability, since it is engineered to flourish at temperatures as high as 35°C. The configuration options for the server are enhanced with the inclusion of Intel Gaudi 3 accelerators. This has eight PCIe Gen 5.0 slots for increased connection and bandwidth, up to 32 DDR5 memory DIMM slots for increased data throughput, and 16 EDSFF3 storage drives for greater data storage options. Combining two up to 56 core 4th Generation Intel Xeon Scalable processors, the XE9680 is designed to perform exceptionally well in complex AI and ML tasks, giving it a competitive advantage in data processing and analysis.

Strategic Developments for AI Understanding

With the addition of more accelerators, the PowerEdge XE9680 surpasses the capabilities of traditional hardware and becomes an indispensable tool for companies looking to use AI to get deep data insights. By combining cutting-edge processing power with an effective, air-cooled architecture, this system redefines AI acceleration and produces quick, actionable insights that improve business results.

Technological Transparency Promotes Innovation

Performance features that are essential for generative AI workloads are brought to the table by the Intel Gaudi 3 AI accelerator. These features include 128 GB of HBMe2 memory capacity, 64 custom and programmable tensor processor cores (TPCs), 3.7 TB of memory bandwidth, and 96 MB of on-board static random-access memory (SRAM). The strong structure of model libraries and collaborations optimize the Gaudi3’s open ecosystem. With its development tools, existing codebases may shift with ease, requiring just a few lines of code to migrate.

Specialized Networking and Video Decoder Features

With the Intel Gaudi3 accelerator added, the PowerEdge XE9680 offers new networking features that are embedded into the accelerators via six OSFP 800GbE ports. These links eliminate the requirement for external NICs to be installed in the system by enabling direct connections to an external accelerator fabric. This tries to reduce the overall cost of ownership and complexity of an infrastructure in addition to simplifying it. Additionally, the specialized media decoders Intel Gaudi 3 are made for AI vision applications. These can handle heavy pre-processing jobs, which speeds up the translation of video to text and improves the efficiency of AI applications for businesses.

With the Intel Gaudi 3, the Dell PowerEdge XE9680 represents a revolutionary advancement in AI development.

A turning point in AI computing has been reached by Dell and Intel’s partnership, which is embodied in the Dell PowerEdge XE9680 with the Intel Gaudi 3 AI accelerator. It provides an innovative solution that anticipates the requirements of the industry going ahead while meeting the demands of AI workloads now. Through this relationship, technology experts will have access to cutting-edge tools for innovation that will push the envelope in AI research and establish new benchmarks for computational excellence and efficiency.

Are you prepared to take off with the Gaudi Accelerator? Through their Intel Developer Cloud offering, a limited number of clients may now start testing Intel’s accelerators thanks to a partnership between Dell and Intel. Find out more.

In the latest Forrester Wave study, Dell was named as a leader in artificial intelligence. Dell provides complete solutions for IT and data scientists to use AI and increase productivity, which creates end-to-end GenAI results. Dell can be your go-to counsel in accelerating your AI goals, regardless of where you are in the process.

Availability:

Intel is already sending samples to prospective clients, however wide availability is anticipated in Q3 2024.

In Q4 2024, PCIe add-in cards a popular form factor for AI accelerators are expected to be released.

All things considered, Intel Gaudi 3 seems to be a formidable competitor in the market for AI accelerators, especially for companies wishing to use generative AI technology. Because of its emphasis on effectiveness, efficiency, and transparency, it has the potential to revolutionize this quickly developing area.