**英特尔Gaudi 2芯片在AI作图训练领域展现卓越性能,超越英伟达H100**
据热点科技报道,人工智能领域的领先开发商Stability AI最近发布了一份详尽的报告,揭示了英特尔的Gaudi 2计算卡在AI图像生成训练方面展现出的显著优势。在对比测试中,Gaudi 2芯片在特定参数设置下,其效率显著超过了英伟达的顶级产品。
报告指出,以2B参数的MMDiT模型为例,训练深度设定为24,并采用BFloat16的混合精度,Gaudi 2计算卡的峰值性能可达到每秒训练1254张图片。而在256容量下,这一速度降低至每秒927张。相比之下,英伟达的H100-80GB计算卡每秒只能处理595张图片,而A100-80GB则为381张。这意味着,Gaudi 2在相同容量下相比H100的训练速度提升了55%,其性能是A100的2.43倍。
这一结果显示,英特尔的Gaudi 2芯片在人工智能领域,特别是高密度、高效率的图像训练任务上,已确立了显著的竞争优势。这一突破性进展对于需要大量计算资源的AI开发者和研究者来说,无疑提供了新的选择和可能性。随着AI技术的快速发展,硬件性能的提升将直接影响到模型训练的效率和产出,因此,Gaudi 2的出色表现可能改写AI计算领域的格局。
英语如下:
**News Title:** “Intel’s Gaudi 2 Chip Challenges NVIDIA: 55% Faster AI Image Generation Training”
**Keywords:** Intel Gaudi 2, AI Image Generation, Performance Boost
**News Content:**
According to HotTech, leading AI developer Stability AI has recently released a comprehensive report highlighting the exceptional performance of Intel’s Gaudi 2 compute card in AI image generation training, outperforming NVIDIA’s H100.
The report reveals that under specific parameters, the Gaudi 2 chip surpasses NVIDIA’s flagship product. For instance, with a 2B parameter MMDiT model, a depth setting of 24, and using BFloat16 mixed precision, the Gaudi 2 achieves a peak performance of training 1,254 images per second. At a capacity of 256, this rate drops to 927 images per second. In contrast, NVIDIA’s H100-80GB handles 595 images per second, while the A100-80GB manages 381. Consequently, Gaudi 2 offers a 55% faster training speed compared to H100 at the same capacity and performs 2.43 times better than the A100.
This indicates that Intel’s Gaudi 2 has established a significant competitive edge in the AI domain, particularly in high-density, high-efficiency image training tasks. This breakthrough advancement presents new options and possibilities for AI developers and researchers who require substantial computational resources. As AI technology rapidly evolves, enhancements in hardware performance directly impact the efficiency and output of model training. Thus, Gaudi 2’s impressive performance could reshape the landscape of AI computing.
【来源】https://www.itheat.com/view/45945.html
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