文本到视频的革命取决于惊人的计算能力：需要数百万个 GPU

文本到视频生成的前景激起了人们对人工智能代币的兴趣，但广泛采用将需要大量增加计算能力。据估计，支持 TikTok 和 YouTube 的创作者社区需要 72 万块高端 Nvidia H100 GPU，耗资 216 亿美元，远远超过 Meta 和微软等科技巨头目前拥有的资源。这突显了人工智能生成视频主流面临的重大硬件挑战和潜在限制。

Text-to-Video Revolution Hinges on Staggering Compute Power: Millions of GPUs Required

文本到视频的革命取决于惊人的计算能力：需要数百万个 GPU

The advent of text-to-video generation has ignited excitement within the crypto market, with AI tokens soaring following the unveiling of OpenAI's "Sora" demo. However, making this technology mainstream poses a formidable challenge, requiring an astronomical amount of compute power.

文本到视频生成的出现引发了加密货币市场的兴奋，随着 OpenAI 的“Sora”演示发布后，AI 代币飙升。然而，使这项技术成为主流面临着巨大的挑战，需要大量的计算能力。

The Sheer Number: Hundreds of Thousands of GPUs Needed

数量庞大：需要数十万个 GPU

A groundbreaking report from Factorial Funds estimates that a staggering 720,000 high-end Nvidia H100 GPUs would be necessary to support the content-creator communities on platforms like TikTok and YouTube. This number dwarfs the combined GPU arsenal of tech giants such as Microsoft, Meta, and Google.

Factorial Funds 的一份开创性报告估计，支持 TikTok 和 YouTube 等平台上的内容创作者社区需要 720,000 个高端 Nvidia H100 GPU。这个数字让微软、Meta 和谷歌等科技巨头的 GPU 库总和相形见绌。

Training vs. Inference: An Exponential Power Demand

训练与推理：指数级的功率需求

Training text-to-video models like Sora requires colossal compute power. According to Factorial Funds, Sora requires up to 10,500 GPUs for a month's training and generates a mere 5 minutes of video per hour per GPU during inference. As adoption grows, inference will surpass training, demanding even more computational resources to produce new videos.

训练像 Sora 这样的文本到视频模型需要巨大的计算能力。据 Factorial Funds 称，Sora 一个月的训练需要多达 10,500 个 GPU，并且在推理过程中每个 GPU 每小时仅生成 5 分钟的视频。随着采用率的增长，推理将超越训练，需要更多的计算资源来生成新视频。

Nvidia's Dominance, but Not a Monopoly

Nvidia 占据主导地位，但并非垄断

Nvidia reigns supreme as the leader in AI-specific GPUs, but it's not the only player. Rival AMD offers competitive products, and its stock has witnessed a meteoric rise in recent years. Alternative options exist for outsourcing compute power to GPU farms, but these networks largely rely on gaming GPUs, significantly less potent than Nvidia's server-grade offerings.

Nvidia 是人工智能专用 GPU 领域的领导者，但它并不是唯一的参与者。竞争对手AMD提供有竞争力的产品，近年来其股价飞速上涨。存在将计算能力外包给 GPU 农场的替代选择，但这些网络很大程度上依赖于游戏 GPU，其效力明显低于 Nvidia 的服务器级产品。

The Hardware Hurdle: A Call for More Chips

硬件障碍：需要更多芯片

The promise of text-to-video generation hinges on a herculean hardware investment. Nvidia, with its annual production capacity of 550,000 H100 GPUs, falls short of meeting the projected demand. Combined, the twelve largest users of H100 GPUs possess 650,000 of the cards, with Meta and Microsoft collectively holding 300,000.

文本到视频生成的前景取决于巨大的硬件投资。 Nvidia的H100 GPU年产能为55万片，但未能满足预期需求。 H100 GPU 的 12 大用户加起来拥有 650,000 张卡，其中 Meta 和 Microsoft 总共拥有 300,000 张。

A Multi-Billion Dollar Endeavor

耗资数十亿美元的努力

Acquiring the necessary number of H100 GPUs would incur a staggering cost of $21.6 billion, nearly matching the current market capitalization of AI tokens. Even if the financial hurdles could be overcome, the physical availability of these GPUs remains a significant constraint.

购买必要数量的 H100 GPU 将花费 216 亿美元的惊人成本，几乎与当前 AI 代币的市值相当。即使可以克服财务障碍，这些 GPU 的物理可用性仍然是一个重大限制。

Conclusion: Mainstream Adoption Remains Elusive

结论：主流采用仍然难以捉摸

The allure of text-to-video generation is undeniable, but its widespread adoption faces a formidable challenge in the form of massive compute power requirements. While the premise holds promise for revolutionizing creative workflows, expecting mainstream adoption anytime soon is unrealistic. The road to unleashing the full potential of this technology requires a substantial increase in chip production and a concerted effort to address the immense hardware demands.

文本到视频生成的吸引力是不可否认的，但其广泛采用面临着巨大的计算能力需求形式的巨大挑战。虽然这一前提有望彻底改变创意工作流程，但期望很快被主流采用是不现实的。要充分发挥这项技术的潜力，需要大幅增加芯片产量，并共同努力满足巨大的硬件需求。