![]() Unlike Nvidia and AMD, Intel led with their 5 th Gen Xeon AI accelerated CPU. Intel made its presence at the shoot-out known shortly thereafter. 14, 2023, Intel introduced its 5th Gen Intel Xeon Scalable processors, delivering increased performance per watt and lower total cost of ownership across critical workloads for artificial intelligence, analytics, networking, security, storage and high performance computing. displays a 5th Gen Intel Xeon processor during an event in March 2023. Sandra Rivera, Intel executive vice president and general manager of the Data Center and AI Group. Additionally, the latest release adds support for key generative AI features like FlashAttention, HIPGraph and vLLM. Emphasizing its open source approach, AMD asserts an 8x AI performance increase on the same MI300 hardware when compared to previous generation software. Providing air cover across the platform, the latest ROCm 6 platform was also announced. Compared with the previous generation MI250A running FP32 HPC and AI workloads, this delivers approximately 1.9x performance-per-watt improvement. In a world where these types of high performance GPUs are in the 10s of thousands of dollars apiece and manufacturing capacity is extremely limited, this could represent enough of an advantage for AMD to help establish them as a viable second source for AI GPUs.Īlso based on CDNA3, the MI300A leverages 3D packaging and the 4 th Gen AMD Infinity Architecture to integrate the GPU cores with AMDs Zen4 CPU cores and 128 GB of HBM3 memory in a single package. With these performance and capacity improvements, AMD asserts that the MI300X is the only GPU capable of running Llama2 70B on a single accelerator greatly simplifying and reducing the number of GPUs required for a given workload. These performance improvements translate to 192 GB of HBM3 memory capacity and 5.3 TB/s peak memory bandwidth. 7B and 13B versions of Llama 2 result in slightly lower but still impressive 3.7x and 4x performance improvements.īased on AMDs latest CDNA3 architecture, the MI300X is touted as delivering 40% more compute units, 1.5x more memory capacity and 1.7x more peak theoretical memory bandwidth when compared to its predecessor, the MI250X. Finally, these improvements, along with optimized communication efficiency and chunk sizes for tensor and pipeline parallelism, result in the claimed 4.2x faster Llama2 performance increase as measured in Tensor Core usage from 201 TFLOPS per A100 up to 836 TFLOPS per H200 for Llama 2 70B pre-training and supervised fine-tuning. Additionally, Large Language Model (LLM) support was improved with optimizations for rotary positional embedding operations and Swish-Gated Linear Unit functions. This performance improvement is achieved by the addition of mixed-precision support for the model optimizer, which improves model capacity requirements as well as effective memory bandwidth by 1.8x. This latest release runs on Nvidia’s H200 GPUs which the company claims can deliver up to 4.2x faster Llama 2 pre-training and supervised fine-tuning performance in terms of TFLOPS per GPU compared to the previous release running on A100s.
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