Highlights
This blog introduces the benchmark study conducted by Open Innovation AI* using Intel® Gaudi® 2 AI accelerator and summarizes the key findings under the following highlights:
- Exceptional Performance Metrics: Intel Gaudi 2 AI accelerator significantly reduces latency, improves throughput, and ensures rapid time to first token (TTFT) in handling large language models (LLMs) like Llama-3.1B and Falcon-3.10B, delivering robust performance across real-world AI applications.
- Designed for Complex Workloads: Purpose-built to manage the computational demands of LLMs, Intel Gaudi 2 AI accelerator excels in scenarios such as chatbots and retrieval augmented generation (RAG), showcasing energy efficiency and scalability while processing inputs up to 3,000 tokens.
- Scalable and Cost-Effective: The open, vendor-agnostic platform of the Intel Gaudi 2 AI accelerator allows organizations to scale AI workloads seamlessly, minimizing both capital expenses (CapEx) and operating expenses (OpEx) and setting the stage for future advancements with Intel® Gaudi® 3 AI accelerator.
The rise of LLMs and multimodal AI systems is redefining the boundaries of what AI technologies can achieve. These cutting-edge models excel in natural language understanding, image recognition, and cross-modal intelligence but also present various computational challenges. Training and deploying models with billions of parameters demands immense computational power and energy-efficient, scalable, and cost-effective infrastructure.
Intel Gaudi 2 AI accelerator was developed precisely to address these challenges. Purpose-built for deep learning workloads, Intel Gaudi 2 AI accelerator combines high performance, energy efficiency, and scalability, enabling organizations to manage the growing demands of advanced AI applications without compromising on cost or flexibility. By offering an open, vendor-agnostic platform, Intel Gaudi 2 AI accelerator empowers enterprises to avoid lock-in while accelerating AI innovation.
This benchmark study, conducted by Open Innovation AI, highlights the exceptional performance of Intel Gaudi 2 AI accelerator in two specific scenarios: chatbots and RAG. Open Innovation AI, based in the UAE, is a technology company that specializes in developing advanced solutions for managing AI workloads. By testing Intel Gaudi 2 AI accelerator on popular models like Llama-3.1B and Falcon-3.10B, the study provides valuable insights into why these benchmarks are critical for real-world applications. Here’s a detailed breakdown in five key points.
Popularity and Real-World Relevance
Llama and Falcon models are among the most popular LLMs in the AI ecosystem. Their versatility makes them suitable for various applications, from chatbots to content generation. By benchmarking Intel Gaudi 2 AI accelerator against these models, the study ensures relevance to real-world use cases.
This alignment with widely adopted models demonstrates the readiness of Intel Gaudi 2 AI accelerator for practical, day-to-day AI challenges enterprises and developers face.
The Importance of Working with Large Models
Llama-3.1B and Falcon-3.10B, with billions of parameters, represent significant computational challenges. These large-scale models demand extensive memory management and processing power, making them ideal for testing hardware scalability.
The ability of Intel Gaudi 2 AI accelerator to handle such massive workloads highlights its robustness and adaptability for projects requiring high computational intensity, whether in training or inference.
Rigorous Test Conditions and Performance Metrics
The benchmark study focuses on critical performance metrics that directly impact real-world applications. These include:
- TTFT measures how quickly a model can generate the first token in response to user input. TTFT is crucial for real-time applications like chatbots as it directly affects user satisfaction. Intel Gaudi 2 AI accelerator reduces TTFT, enables faster responses, and enhances the overall experience.
- Latency refers to the total time it takes to process a request and return a result. Low latency ensures seamless interaction and the ability to handle more users simultaneously in high-demand scenarios like chatbots and RAG. Intel Gaudi 2 AI accelerator minimizes latency, making it highly suitable for intensive workloads.
- Throughput evaluates the number of concurrent tasks a system can handle effectively. This is particularly important for applications like RAG, which require processing large volumes of data. Intel Gaudi 2 AI accelerator achieves high throughput, proving its ability to scale for enterprise-level deployments.
These metrics validate the hardware’s technical capabilities and underline its suitability for handling demanding, real-world scenarios efficiently and precisely.
Compatibility with Open Source Models and Cost Efficiency
Open source models like Llama and Falcon are gaining traction as cost-effective alternatives to commercial solutions. Their flexibility and accessibility make them an attractive choice for organizations of all sizes. The seamless compatibility of Intel Gaudi 2 AI accelerator with these models provides an added advantage, enabling businesses to achieve high performance without breaking their budgets.
This compatibility ensures that Intel Gaudi 2 AI accelerator is not only technically advanced but also economically viable for diverse AI workloads.
Alignment with Popular Use Cases
The benchmark focuses on two critical AI applications: chatbots and RAG.
- Chatbots: From customer support to sales, chatbots are indispensable in modern businesses. Intel Gaudi 2 AI accelerator effectively reduces latency and TTFT, ensuring smooth, responsive real-time interactions.
- RAG: This method enables models to retrieve external data to improve AI-generated responses, making it suitable for applications such as knowledge management and content creation. The high throughput and processing efficiency of Intel Gaudi 2 AI accelerator make it an excellent choice for RAG-based solutions.
These use cases represent some of the most in-demand AI applications today, and the outstanding performance of Intel Gaudi 2 AI accelerator in these areas cements its position as a top choice for enterprises and developers.
Conclusion
Through its performance in real-world benchmarks, Intel Gaudi 2 AI accelerator has proven itself a powerful and efficient platform for large language models. It excels in scenarios like chatbots and RAG and is compatible with popular open source models like Llama and Falcon. Thus, Intel Gaudi 2 AI accelerator sets a new standard for deep learning hardware.
After building on the foundation established by Intel Gaudi 2 AI accelerator, Intel Gaudi 3 AI accelerator sees an increase in HBM memory to 128GB and now supports PCIe 5.0. Additionally, Intel Gaudi 3 AI accelerator uses the TSMC* 5nm process to provide improved area density and significantly enhanced power efficiency, elevating AI performance to new heights. These advancements showcase Intel's ongoing commitment to pushing the limits of AI accelerator technology, making Intel Gaudi products an ideal option for organizations looking to efficiently and cost-effectively scale their AI workloads.
For more details, read the white paper (PDF).