文本到视频革命需要庞大的 GPU 库

AI 生成视频的出现引发了 GPU 需求的激增，估计需要 72 万个高端 Nvidia H100 GPU 来支持 TikTok 和 YouTube 等平台上的创作者社区。此类模型的训练过程需要巨大的计算能力，甚至超过了 GPT-4 和静态图像生成。随着采用率的增长，推理需求将进一步超过训练，需要大量的硬件投资才能使文本到视频生成成为主流。

Text-to-Video Revolution Hinges on a Vast GPU Arsenal: Millions Required

文本到视频的革命取决于庞大的 GPU 库：需要数百万

The groundbreaking capabilities of text-to-video generators, exemplified by OpenAI's Sora, have sparked a surge of excitement and investment in Artificial Intelligence (AI). However, the realization of mainstream AI-generated video hinges on the availability of an unprecedented number of specialized processing units known as Graphics Processing Units (GPUs).

以 OpenAI 的 Sora 为代表的文本转视频生成器的突破性功能引发了人们对人工智能 (AI) 的热情和投资。然而，主流人工智能生成视频的实现取决于数量空前的专用处理单元（称为图形处理单元（GPU））的可用性。

Estimates by Factorial Funds reveal that a staggering 720,000 high-end Nvidia H100 GPUs would be necessary to cater to the vast creator communities of platforms like TikTok and YouTube. The gargantuan scale of this computational demand is evident in Sora's training process, which requires up to 10,500 powerful GPUs for a month and can generate only about 5 minutes of video per hour per GPU during inference.

Factorial Funds 的估计显示，为了满足 TikTok 和 YouTube 等平台庞大的创作者社区的需求，需要 720,000 个高端 Nvidia H100 GPU。这种计算需求的巨大规模在 Sora 的训练过程中显而易见，一个月需要多达 10,500 个强大的 GPU，并且在推理过程中每个 GPU 每小时只能生成约 5 分钟的视频。

As illustrated by the data, training text-to-video models demands significantly more computing power than training massive language models like GPT4 or even generating static images. Moreover, as AI models like Sora gain wider adoption, the computation required for inference - the process of using the trained model to generate new videos - will eclipse the computation required for initial training.

数据表明，训练文本转视频模型比训练 GPT4 等大规模语言模型甚至生成静态图像需要更多的计算能力。此外，随着像 Sora 这样的人工智能模型得到更广泛的采用，推理所需的计算（使用经过训练的模型生成新视频的过程）将超过初始训练所需的计算。

To put this into perspective, Nvidia shipped approximately 550,000 H100 GPUs in 2023. Statistics from Statista indicate that the twelve largest customers utilizing Nvidia's H100 GPUs collectively possess 650,000 of these cards, with tech giants Meta and Microsoft accounting for 300,000 of them.

从这个角度来看，Nvidia 在 2023 年出货了大约 55 万张 H100 GPU。Statista 的统计数据显示，使用 Nvidia H100 GPU 的 12 大客户总共拥有 65 万张此类卡，其中科技巨头 Meta 和微软占据了 30 万张。

Assuming a cost of $30,000 per card, the astronomical sum of $21.6 billion would be required to procure the GPUs necessary to make Sora's vision of AI-generated text-to-video mainstream. This figure nearly matches the entire market capitalization of AI tokens at present.

假设每张卡的成本为 30,000 美元，则需要 216 亿美元的天文数字来采购必要的 GPU，以使 Sora 的人工智能生成文本到视频的愿景成为主流。这个数字几乎与目前AI代币的总市值相当。

The procurement of such a vast quantity of GPUs presents a formidable challenge due to supply constraints and escalating demand for AI-driven applications. Notably, Nvidia is not the sole player in the GPU market. Rival chipmaker AMD offers competitive products, with investors generously rewarding the company for its innovations, propelling its stock from the $2 range in 2012 to over $175 today.

由于供应限制和人工智能驱动应用程序的需求不断上升，采购如此大量的 GPU 是一项艰巨的挑战。值得注意的是，Nvidia 并不是 GPU 市场的唯一参与者。竞争对手芯片制造商 AMD 提供具有竞争力的产品，投资者慷慨地奖励该公司的创新，推动其股价从 2012 年的 2 美元区间升至如今的 175 美元以上。

Alternative options exist for outsourcing computing power to GPU farms, such as Render (RNDR) and Akash Network (AKT). However, the majority of GPUs on these networks consist of retail-grade gaming GPUs, significantly less potent than Nvidia's server-grade H100 or AMD's comparable offerings.

存在将计算能力外包给 GPU 场的替代选择，例如渲染 (RNDR) 和 Akash 网络 (AKT)。然而，这些网络上的大多数 GPU 都是零售级游戏 GPU，其性能明显低于 Nvidia 的服务器级 H100 或 AMD 的同类产品。

Despite these challenges, the tantalizing promise of text-to-video generation, as envisioned by Sora and other protocols, will necessitate a herculean hardware investment. While the potential for revolutionizing the creative workflow in industries like Hollywood is undeniable, it is crucial to temper expectations regarding the imminent mainstream adoption of AI-generated video.

尽管存在这些挑战，但正如 Sora 和其他协议所设想的那样，文本到视频生成的诱人前景将需要巨大的硬件投资。虽然彻底改变好莱坞等行业创意工作流程的潜力是不可否认的，但降低对人工智能生成视频即将成为主流的预期至关重要。

At present, the industry faces a pressing need for a substantial increase in chip production to meet the burgeoning demand for AI-driven applications. Until this supply gap is addressed, the widespread availability of AI-generated video will remain an elusive goal.

目前，业界迫切需要大幅增加芯片产量，以满足人工智能驱动应用不断增长的需求。在解决这一供应缺口之前，人工智能生成视频的广泛可用性仍将是一个难以实现的目标。