Several Silicon Valley entrepreneurs have shared correspondence with Huang Renxun, not for any specific purpose other than to illustrate what kind of person he is.

The individuals involved include the CEO of a robotics startup on the brink of bankruptcy, a small team developing local AI, and developers behind a top-tier open-source inference framework.

What they all have in common is this: at moments of extreme desperation or audacious ambition, they sent an email to Huang Renxun—and received a reply within minutes.

The fastest response occurred on March 4, at 6:09 p.m.

At that time, Simon Mo, co-founder of the open-source large-model inference framework vLLM, faced a critical crisis: his compute provider abruptly withdrew resources, leaving the team unable to access GB200 and GB300 cabinets based on the Blackwell architecture, threatening to halt development entirely.

With little to lose, Mo sent an email to Huang Renxun.

Screenshots show the message was sent at 6:09 p.m., and Huang’s reply arrived at exactly the same time—within less than 60 seconds. The response consisted of a single sentence: “How can we help?” By that night, the bottleneck had been resolved.

Typical corporate procurement cycles often span quarters; Huang’s response speed outpaced many automated replies.

Another example involves Exo Labs, a startup focused on distributed AI running locally.

In June, co-founder Alex Cheema conceived an ambitious idea during dinner: could local AI performance on NVIDIA hardware be improved tenfold?

Seven minutes after sending the email, Huang approved the proposal outright, allocating a dedicated “war room” at NVIDIA’s headquarters and allowing the small company to operate there for an entire month.

During the first week, a group of NVIDIA’s most senior engineers—including the creators of CUDA and Dynamo—moved into the war room to assist with optimization. In the end, the two teams jointly achieved a 9.6-fold improvement in local AI performance.

If the previous cases were technical collaborations, the story of Serve Robotics reads like a screenplay.

Days before Christmas 2023, a last-minute investor withdrawal left the company with insufficient funds even to pay salaries. CEO Ali Kashani was already consulting bankruptcy lawyers about winding down operations.

With no alternatives left, he sent Huang Renxun an email titled “Urgent.”

The next morning at 6 a.m., Huang called him directly while driving to work. That evening, a support plan was finalized over dinner with the NVIDIA team. Four months later, the company successfully went public on Nasdaq.

To outsiders, these stories may paint Huang Renxun as a technological savior. But beneath the sentimental veneer lies a shrewd business strategy.

In the AI industry, truly transformative technologies rarely emerge from boardrooms of Fortune 500 companies—they are often born in the minds of underfunded technologists.

Serve Robotics had no cash reserves but represented real-world applications of physical AI and autonomous delivery. vLLM has become an indispensable foundation for global large-model inference; losing such projects to competitors would entail irreversible strategic losses. Exo Labs’ efforts to optimize edge computing may appear to bypass expensive data center GPUs, but in reality, they are reinforcing NVIDIA’s dominance in decentralized ecosystems.

Spending mere minutes each day replying to unknown emails incurs almost zero cost—but effectively turns Silicon Valley into a vast early-warning radar network.

A single dinner, a temporary war room, and timely intervention during moments of vulnerability allow NVIDIA to extend its influence at the most critical junctures, securing future industry standards firmly within the CUDA ecosystem. This intuitive grasp of technological trends proves more valuable than any market research report.

That is why these entrepreneurs claim Huang Renxun is irreplaceable.

Modern business education excels at producing steady, risk-averse executives—proficient in financial statements, meticulous about processes, and never making mistakes—but who would never answer a phone call at 6 a.m. from a struggling startup on the verge of collapse.

Technical paths can be duplicated. Chip architectures can be imitated. But the raw, instinctive judgment of a trillion-dollar empire’s leader cannot be codified in an onboarding manual.

Original article: toutiao.com/article/1881762480778307/

Disclaimer: The views expressed in this article are those of the author alone.