Innovators
1 Curated Dialogues
陈茜
Key Views & Dialogues
E247 | A Conversation with 盛颖: xAI, the Romance of Infra, SGLang, Open Source, Equality, and “甄嬛传”
RadixArk’s $100M seed round and SGLang’s deployment across hundreds of thousands of GPUs show open-source inference gaining unusual commercial and strategic backing. SGLang generates tens of trillions of tokens daily for Google, Microsoft, Nvidia, and xAI. Inference providers are growing in sync as AI spending shifts from training toward inference and proprietary-data customization, while RadixArk’s longer-term outcome depends on scaling its team beyond an open-source engine toward next-generation AI.
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- Inference is a game with no losers. 盛颖’s framing: inference providers keep multiplying, but “none has been diminished by the existence of competition; everyone is growing in sync.” The host runs through the market tape: a company reportedly formed by core members of vLLM, Inferact (name as heard), raised $150M at an $800M valuation; Vast (name as heard) raised $300M at a $5B valuation; Fireworks is valued at $4B; and Together is seeking $1B in new funding. He identifies 2 major shifts: AI now has to “make money and cash in,” moving market effort from training toward inference; meanwhile, AI-native companies are running into the limits of prompt engineering and need to customize with proprietary data, with some established AI companies expanding into RL.
- RadixArk announced a $100M seed round led by Axial in May, drawing an unusually broad group of backers. Nvidia’s investment arm, AMD, Intel CEO 陈立武, OpenAI co-founder John Schulman, and others all participated. Its underlying asset, SGLang, “has never been marketed or sold,” yet now runs on hundreds of thousands of GPUs worldwide and generates tens of trillions of tokens every day for Google, Microsoft, Nvidia, and xAI.
- The company’s lifeline is not SGLang. SGLang is fully open source and belongs to a community apparently associated with LMSYS rather than to the company: “There is no private fork. We never intended to create a difference here and extract value from it.” RadixArk’s end mission is “next-generation AI”—not necessarily a model, and “not something that already exists in the world today.” Inference is only the starting point and a prerequisite. Baseten and Fireworks are both users and potential rivals; his preference is that “all of them become our allies.”
- His account offers a rare inside view of xAI. Early xAI was “full of talent, with absolutely no politics,” a description that sounds like “two different companies” compared with the public narrative of its co-founders leaving en masse. His diagnosis applies to any fast-scaling AI company: xAI failed to navigate the transition from running on people to running on institutions. “If you don’t pay attention, it will be broken.” He left 2 months before his one-year cliff so the open-source community would not stall, giving up his first-year vesting.
- Day-zero compatibility is an exercise in market-driven economics. Importance is determined by market psychology—“you launch a product and I rush to buy it on day 1.” DeepSeek V4 introduced enough architectural innovation that “most of the code was basically rewritten,” and it was the first DeepSeek large model to support RL. As for latent-space inference, such as Meta’s possibly Coconut, he is unfazed: “The world changes and structures change; that is part of writing an inference engine. The real difficulty has always come back to the people and the team.”
- The differentiator is taste. “Infra is not a support role; in my eyes, infra itself is the product.” RadixArk reverses the priorities of big tech: a training job can pause, and benchmark scores are not urgent. Urgency belongs only to solving deep problems systematically. Taste means: “My system doesn’t merely run; I know how it runs—whether it is well-designed or a shoddy construction project.”
- Equality is the through line. He learned from CSDN and online problem banks, so open source feels like air; gender inequality is captured by the fact that “every win of mine has to be explained,” and the only real fix is for the top 0.001% to 1% to genuinely hold power. His end-state for AI is similar: closed source can exist, but it cannot be centralized—“everyone should have the equal ability to make their own AI, while the AI they make belongs to them.”