Nvidia vs Intel: A Deep Dive into Intel’s AI Accelerator Gaudi 3

Nvidia vs Intel: A Deep Dive into Intel’s AI Accelerator Gaudi 3

Intel is making significant strides in AI, potentially becoming a formidable competitor to Nvidia. In April, at the Intel Vision 2024 event in Phoenix, Arizona, the company introduced the initial architectural details of its third-generation AI accelerator, Gaudi 3.

Previous iterations of Gaudi received some criticism, which Intel claims have been addressed in this latest version. Gaudi 3 focuses on performance with large language models (LLMs) and boasts substantial improvements. Meanwhile, discussions around Nvidia’s upcoming GPU, the Blackwell B200, continue. Gaudi 3 is scheduled for launch in the third quarter of 2024, with samples already being sent to customers.

The Arrival of Intel's Gaudi 3 AI Accelerator

Developed using a cutting-edge 5nm process, Gaudi 3 combines power and efficiency to enhance AI acceleration significantly. It sets new industry benchmarks and enables the creation of innovative AI and ML applications.

Intel asserts that the Gaudi 3 AI accelerator is generally 40% faster than Nvidia's H100.

The Gaudi 3 AI Accelerator represents a major leap in AI processing capabilities, boasting a 40% speed increase over its prominent competitor, the Nvidia H100. This improvement stems from a carefully refined design that optimizes data flow and computational efficiency, allowing for more effective handling of complex AI tasks.

The accelerator's enhanced architecture is the key to this remarkable performance boost. It enables faster processing of AI and ML algorithms, significantly reducing data analysis and model training times. This highlights Gaudi 3’s superior computational efficiency and power and establishes a new standard for AI accelerators.

Features of Intel Gaudi 3 Accelerator

  1. AI-Dedicated Compute Engine: The Intel Gaudi 3 accelerator, designed for large-scale AI computing, is built on a 5nm process, offering substantial improvements over its predecessor. This transition to a more advanced manufacturing technology enables a denser and more efficient design, significantly boosting the accelerator’s overall performance.

    It features a dedicated AI Compute Engine with 64 AI-specific and programmable Tensor Processor Cores (TPCs) and eight Matrix Multiplication Engines (MMEs). Each MME can perform an impressive 64,000 parallel operations, enhancing computational efficiency and supporting multiple data types, including FP8 and BF16.

  2. Memory Boost for LLM Capacity Requirements: With 128GB of HBMe2 memory capacity, 3.7TB of memory bandwidth, and 96MB of onboard SRAM, the Gaudi 3 accelerator offers substantial memory for processing large generative AI datasets. This leads to improved workload performance and data center cost efficiency, which is particularly beneficial for large language and multimodal models.

  3. Efficient System Scaling for Enterprise GenAI: Equipped with twenty-four 200Gb Ethernet ports, the Gaudi 3 accelerator supports flexible and open-standard networking, efficiently scaling large compute clusters. This design eliminates vendor lock-in from proprietary networking fabrics and scales efficiently from a single node to thousands, meeting the extensive requirements of generative AI models.

  4. Open Industry Software for Developer Productivity: Intel Gaudi software integrates with the PyTorch framework and offers optimized Hugging Face community-based models. This allows generative AI developers to work at a high level of abstraction for ease of use and productivity and facilitates easy model porting across different hardware types, enhancing developer efficiency.

  5. Gaudi 3 PCIe: The Gaudi 3 PCIe add-in card introduces a new form factor optimized for high efficiency with lower power consumption. It is ideal for fine-tuning, inference, and retrieval-augmented generation (RAG) workloads. It delivers high efficiency and performance with a full-height form factor of 600 watts, 128GB memory capacity, and 3.7TB per second bandwidth.

Impact of Intel Gaudi 3 on the AI Accelerator Market

The introduction of Gaudi 3 signifies a major shift in AI hardware dynamics, challenging existing standards and advancing performance and efficiency. This development necessitates a reassessment of strategies across the industry, influencing future AI technology innovations.

Intel’s Position in the Market After Gaudi 3 Launch

The launch of Gaudi 3 has significantly strengthened Intel’s position in the AI accelerator market. This new product not only showcases Intel’s technological expertise but also solidifies its role as a leading innovator, setting new benchmarks in AI acceleration.

Potential Challenges for Nvidia and Strategic Responses

Gaudi 3 presents potential challenges for Nvidia, necessitating strategic responses. Nvidia will need to speed up its innovation cycle to improve its offerings and cost-efficiency, ensuring it can maintain market leadership and meet customer demands in a rapidly evolving landscape.

Use Cases and Applications

The influence of Gaudi 3 extends beyond market dynamics, impacting a wide range of use cases and applications.

  1. Enhancing Large Language Models (LLMs) Gaudi 3 offers significant improvements in managing extensive datasets and complex algorithms, which are crucial for advancing natural language processing and generative AI and fostering the development of more sophisticated AI systems.

  2. Impact on AI Research and Development Gaudi 3’s capabilities empower researchers to address more complex problems and explore new areas in AI. Its efficiency and power unlock opportunities for groundbreaking discoveries and innovations across various AI fields.

  3. Applications in Healthcare, Finance, and More Gaudi 3’s versatile capabilities benefit multiple sectors, including healthcare and finance. In healthcare, it can accelerate diagnostic algorithms and personalized medicine approaches. It enhances real-time fraud detection and algorithmic trading models in finance, offering transformative AI-driven solutions.

Developer and Industry Reception

  1. Initial Developer Feedback Initial developer feedback has been overwhelmingly positive, highlighting Gaudi 3’s enhanced capabilities and potential to drive more efficient and powerful AI applications. Developers are particularly excited about the improved performance metrics and the opportunities these advancements bring for AI research and development.

  2. Partner and OEM Support Intel has received substantial support from partners and OEMs for Gaudi 3, reflecting confidence in its market potential and technological advancements. This broad collaboration indicates widespread interest and applicability of the AI accelerator in addressing complex computational needs across industries.

Comparing Gaudi Chip Generations

Gaudi 3 builds on the foundation laid by Gaudi 2, enhancing various aspects. Unlike Gaudi 2’s single-chip design, Gaudi 3 features two identical silicon dies connected by a high-speed link, doubling the architecture. Each die includes a 48-megabyte cache memory at its center, surrounded by four matrix multiplication engines and 32 tensor processor cores. These elements are interconnected with memory, complemented by media processing and network infrastructure.

Intel has transitioned from TSMC’s 7nm process used in Gaudi 2 to a 5nm process for Gaudi 3, allowing for hardware improvements. Gaudi 3 now features 4 Matrix Math Engines and 32 Tensor Cores, compared to Gaudi 2’s 2 Matrix Math Engines and 24 Tensor Cores. The tensor cores in Gaudi 3 remain similar, functioning as 256-byte-wide VLIW SIMD units.

Intel claims this configuration doubles the AI compute power of Gaudi 2, leveraging 8-bit floating point infrastructure essential for training transformer models. Furthermore, computations using the BFloat16 number format see a fourfold performance increase.

Gaudi 3 LLM Performance vs. Nvidia Hopper Series

Intel has consistently emphasized performance over specifications with the Gaudi accelerators, and Gaudi 3 is no exception. At the Vision event, Intel aimed to impress business clients with benchmark performance figures.

The Gaudi team directly compared their benchmarks with Nvidia’s reported figures to provide an unbiased comparison. However, these projections are not from assembled systems, as Intel is unlikely to have 8192 Gaudi 3 units available for testing.

In comparison to the H100, Intel suggests Gaudi 3 could surpass it by up to 1.7 times in training Llama2–13B in a 16-accelerator cluster at FP8 precision. Even though the H100 is nearly two years old, significantly outperforming it in training would be a notable achievement for Intel if confirmed.

Additionally, Intel anticipates Gaudi 3 delivering 1.3 to 1.5 times the inference performance of the H200/H100, with up to 2.3 times the power efficiency. However, Gaudi 3 occasionally falls short of H100 in certain inference workloads, especially those without 2K outputs, meaning it doesn’t achieve complete dominance. There are also other benchmark results that Intel does not emphasize.

Intel is one of the few major hardware manufacturers providing MLPerf results recently. Despite Gaudi 3’s actual performance, Intel has transparently published results for industry-standard tests.

Looking forward, with Moore’s Law in mind, the main question is the technology the next Gaudi version, Falcon Shores, will use. So far, Intel has used TSMC technology while developing its foundry business. Next year, Intel will offer its 18A technology to foundry customers and use 20A internally, introducing the next generation of transistor technology called nanosheets, with power delivery from the backside. TSMC plans to adopt this combination in 2026.

Conclusion

Intel’s Gaudi 3 AI Accelerator marks a significant advancement in artificial intelligence, demonstrating substantial improvements in processing power, memory bandwidth, and energy efficiency. The developer's widespread acclaim and strong partnership backing underscore its transformative potential. Gaudi 3 exemplifies Intel’s commitment to pushing the boundaries of AI acceleration, playing a pivotal role in shaping the future of AI applications.

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