Organizations in healthcare, finance, manufacturing, and elsewhere are experimenting with generative AI but have yet to bring their projects to fruition. Today, the Intel Gaudi 3 accelerator emerges as a crucial breakthrough, combining exceptional performance with an open architecture to unlock new possibilities and drive progress.
Intel Gaudi 3
With the Intel Gaudi 3 AI accelerator, the company introduced several key changes over its prior generation. As a result, AI deployments can benefit from improved performance, better memory usage and support, and greatly improved efficiency and connectivity. Finally, the Gaudi offers open community-based software for the best possible compatibility with other systems.
The Advantage is in the Architecture
Intel Gaudi 3 superior performance comes thanks to the synergy of three separate AI engines operating in parallel: the Matrix Multiplication Engine (MME), Tensor Processor Cores (TPCs), and Networking Interface Cards (NICs). These components comprise what Intel calls its AI-dedicated compute engine. This processing design dramatically accelerates deep learning tasks while enabling seamless scalability, resulting in faster and more efficient AI computations.
Key improvements that the Intel Gaudi 3 provides over its prior generation include:
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2x AI compute (FP8)
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4x AI compute (BF16)
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2x network bandwidth
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1.5x memory bandwidth
The combined output of these components allows for up to 64,000 parallel operations. As a result, Gaudi 3 systems enjoy high computational efficiency and can thus drive complex matrix operations, which is key to deep learning operations.
Greater Memory Capacity
With 128 gigabytes of HBMe2 memory capacity and 96 megabytes of on-board static random access memory, Intel Gaudi 3 is uniquely suited to handle the data throughput needed by multi-modal and large language models. With so much capacity per processor, organizations can achieve more processing with fewer Gaudi 3s than prior generations, an advantage that spells increased performance and lower data center costs.
In terms of memory capacity, the Intel Gaudi offers the following advantages over the Gaud 2:
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1.5x faster HBM bandwidth (3.7 TB/s versus 2.46 TB/s)
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1.33x larger HBM capacity (128 GB versus 96 GB)
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Double the on-die SRAM bandwidth (12.8 TB/s versus 6.4 TB/s)
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Greater than 2x on-die SRAM capacity (96 MB versus 28 MB)
Given the compute-intensive nature of AI training scenarios, Gaudi 3's increased HBM bandwidth and compute ratio can result in immediate gains in compute power and overall performance.
Smooth and Efficient Scaling
Deep learning training tends to occur on multiple interconnected devices. The Intel Gaudi 3 comes equipped with twenty-four 200 gigabit (Gb) Ethernet ports, offering network connectivity in a flexible and open standard that accommodates on-premise and cloud-based deployments. The Gaudi 3 network interface controller (NIC) also implements the Remote Direct Memory Access over Converged Ethernet version 2 (RoCE) protocol for the best possible server collaboration. Unlike other vendors that lock in customers with proprietary fabrics, Intel Gaudi 3 enables efficient and easy scaling up to large clusters. Therefore, organizations can allow their AI to grow from one node to many to accommodate the needs of virtually any model.
Increased Efficiency
The peripheral component interconnect express (PCIe) add-in card gives Intel Gaudi 3 a full-height form factor at 600 watts with 3.7TB bandwidth and 128 GB memory capacity. Due to its high efficiency and lower power usage, this new form factor is ideal for inference and retrieval-augmented generation (RAG), inference, and fine-tuning. Finally, with the jump to 5nm architecture over the prior generation’s 8nm design, Gaudi 3 allows for higher compute density within the data center and lower energy costs.
Open Software Development
Intel has embraced the GenAI community by integrating Gaudi 3’s software with the PyTorch framework and offering optimized, community-based models to the Hugging Face community. As a result, developers can more easily port across hardware types and code at higher levels of abstraction.
Deploying Intel Gaudi 3
The real power of this architecture becomes apparent in practical deployments. Organizations can implement the key network topology for their needs, from tightly coupled clusters for intensive AI training to distributed systems spanning multiple data centers. Leveraging RoCE, organizations can ensure ultra-low latency communication between accelerators, which makes it ideal for parallel processing in large language model training and other demanding AI workloads.
Accelerate Your AI Journey with UNICOM Engineering and Intel Gaudi 3
The Intel Gaudi 3 AI accelerator represents a breakthrough in AI computing, delivering exceptional performance, scalability, and cost-effectiveness for today's demanding AI and workloads.
UNICOM Engineering, an Intel Titanium Level OEM partner, brings decades of expertise in implementing cutting-edge AI and HPC solutions. Our team of specialists excels at customizing and deploying high-performance infrastructure that maximizes the capabilities of Intel Gaudi 3 technology. We work closely with you to understand your requirements and architect a solution that delivers optimal performance for your AI workloads.
Contact UNICOM Engineering today for more information regarding deploying the Intel Gaudi 3.