LLM 是否碰壁了？微軟執行長薩蒂亞·納德拉 (Satya Nadella) 在 Microsoft Ignite 2024 上解決了這一熱門問題，並以令人耳目一新的坦誠態度進行了討論

“關於我們是否已經在縮放定律上碰壁，存在很多爭論。它會繼續下去嗎？歸根結底要記住的是，這些都不是物理定律。”

Microsoft Ignite 2024 saw Microsoft chief Satya Nadella weigh in on the hot-button issue of whether LLMs have hit a wall.

Microsoft Ignite 2024 見證了微軟執行長 Satya Nadella 就法學碩士是否陷入困境這一熱門議題進行了權衡。

“There’s a lot of debate on whether we have hit the wall with scaling laws. Is it going to continue? The thing to remember, at the end of the day, is that these are not physical laws. They are just empirical observations that held true, much like how Moore’s Law did for a long time,” he said.

「關於我們是否因縮放定律而碰壁，存在著許多爭論。還會繼續嗎？歸根究底，要記住的是，這些並不是物理定律。它們只是真實的經驗觀察，就像摩爾定律長期以來的作用一樣，」他說。

Nadella welcomed the skepticism and debates, calling them beneficial to push innovation in areas such as model architectures, data regimes, and systems architecture. He also discussed OpenAI’s new scaling law, which focuses on test-time computing, and how it will be integrated into features like Copilot Think Deeper, powered by OpenAI’s o1.

納德拉對懷疑和辯論表示歡迎，稱它們有利於推動模型架構、資料機制和系統架構等領域的創新。他還討論了 OpenAI 的新縮放法則，該法則專注於測試時計算，以及如何將其整合到由 OpenAI 的 o1 提供支援的 Copilot Think Deeper 等功能中。

In a recent earnings call, NVIDIA chief Jensen Huang said that OpenAI o1 had introduced a new scaling law called ‘test-time scaling’, which consumed a lot of computing resources. Microsoft is working closely with NVIDIA to scale test-time computing for OpenAI.

在最近的財報電話會議上，NVIDIA 執行長黃仁勳表示，OpenAI o1 引入了一種名為「測試時間縮放」的新縮放法則，該法則消耗了大量運算資源。 Microsoft 正在與 NVIDIA 密切合作，以擴展 OpenAI 的測試時計算。

Nadella emphasized the importance of maximizing value in the most efficient way. “Last month, we introduced new clusters with H200s that became available. We’re very excited about it,” said Nadella. He added that with their stack optimization between H100 and H200, Azure can deliver performance for everything from inference to training.

納德拉強調了以最有效的方式實現價值最大化的重要性。 「上個月，我們推出了帶有 H200 的新集群，現已上市。我們對此感到非常興奮，」納德拉說。他補充說，透過 H100 和 H200 之間的堆疊優化，Azure 可以為從推理到訓練的各個方面提供性能。

Efficiency Wars: Tokens, Watts, and Dollars

效率之戰：代幣、瓦特和美元

“Tokens per watt plus dollar is the best way to think about the new currency of performance,” said Nadella, adding that Microsoft will continue to build new data center intelligence factories.

納德拉表示：「每瓦代幣加美元是考慮新性能貨幣的最佳方式。」他補充說，微軟將繼續建造新的資料中心智慧工廠。

Nadella introduced a new metric that reflects the efficiency of generating tokens, considering both energy consumption (measured in watts) and cost (measured in dollars). This means that for every unit of energy (watt) used and every dollar spent, a certain number of tokens are produced.

納德拉引入了一個新的指標，該指標反映了生成代幣的效率，同時考慮了能源消耗（以瓦特為單位）和成本（以美元為單位）。這意味著每使用一個單位的能源（瓦特）和每花費一美元，就會產生一定數量的代幣。

Despite the progress, NVIDIA has yet to solve the inferencing challenge. Acknowledging the difficulties involved, Huang shared that their goal is to produce tokens at low latency.

儘管取得了進展，NVIDIA 尚未解決推理挑戰。黃承認其中有困難，並表示他們的目標是低延遲地生產代幣。

“Inference is super hard. And the reason…is that you need the accuracy to be high…You need the throughput to be high so that the cost can be as low as possible. But you also need the latency to be low. And computers that are high-throughput and have latency are incredibly hard to build,” he said.

「推理非常困難。原因是…您需要高準確度…您需要高吞吐量，以便成本盡可能低。但您還需要較低的延遲。高吞吐量和有延遲的計算機非常難以構建，」他說。

“Our hopes and dreams are that, someday, the world will do a ton of inference,” said Huang, adding that there will be thousands of AI-native start-ups that will generate tokens.

黃說：「我們的希望和夢想是，有一天，世界將進行大量的推理。」他補充說，將有數千家人工智慧原生新創公司將產生代幣。

Microsoft also announced the preview of NVIDIA Blackwell AI infrastructure on Azure.

微軟也宣佈在 Azure 上預覽 NVIDIA Blackwell AI 基礎架構。

“Blackwell is

「布萊克威爾是