其SN50芯片专为推理设计，功耗低且无需水冷，宣称推理速度比基于GPU的云服务快16倍。 Upper90此前曾为Crusoe的GPU采购提供融资，如今转向押注开源模型推理基础设施。
General Compute，一家 AI 推理云创业公司，从科技投资公司 Upper90 获得了 4 亿美元贷款。这可能是首个以推理专用芯片作为抵押的交易——这些芯片旨在快速高效地运行已经训练好的 AI 模型，而不是用于最初构建模型的昂贵芯片。
这笔融资是市场对 AI 工具和代币价格担忧作出反应的最新信号，市场正在转向能够以比前沿实验室最新大语言模型更低成本运行开源模型的基础设施。
由首席执行官 Finn Puklowski 创立的 General Compute 在五月份筹集了 1500 万美元的种子轮资金：https://techcrunch.com/2026/05/28/has-the-hunt-for-ai-compute-uncovered-the-next-cerebras/，用于围绕 SambaNova（一个英特尔支持的芯片制造商）的硅芯片构建推理新云。（新云专为 AI 工作负载而设计，不像 AWS 或 Azure 等传统超大规模云提供的通用基础设施。）
公司的 SN50 芯片专为推理设计。它们功耗低，不需要昂贵的水冷系统，这意味着它们可以比 GPU 在更多类型的数据中心中更快部署。General Compute 表示，新芯片的推理速度将比基于 GPU 的云快 16 倍。
挑战在于获得大量这些芯片，尤其当你是一家全新的公司时。
Upper90 联合创始人兼首席执行官、前高盛量化交易员 Billy Libby 有一套解决方案：2021 年，他的公司为专注能源的数据中心创业公司 Crusoe 提供 GPU 购买融资，他认为这是第一笔以先进芯片价值为抵押的贷款。
当时传统贷款机构回避此类交易，因为 GPU 折旧存在风险和不确定性。但随着 CoreWeave 将芯片抵押贷款发展为商业模式，并成为一场重磅 IPO 的基础，这类融资已经变得普遍。
“当我们作为首批为 Nvidia GPU 提供融资的人时，市场效率低下，”Libby 告诉 TechCrunch。“我们作为早期参与者，确实可以组合一些东西，并在一定程度上因风险获得回报。”
现在 GPU 相对来说已经比较被理解，甚至可能被过度购买：https://techcrunch.com/2026/07/09/can-ai-answer-the-3-trillion-question/，Upper90 正转向像 General Compute 这样的公司，以把握下一波 AI 热潮。“我们认为开源模型将会非常重要，因此去年我们去寻找一家专注于推理的公司，”Libby 说。“并不是每个人都需要超级计算机，但他们确实需要推理和 AI。”
这一理念正变得越来越强烈，提供访问开源模型的公司，如 OpenRouter 和 Fireworks，都以巨额估值完成了新一轮融资。像 Kimi 的 K3 这样的新模型：https://techcrunch.com/2026/07/16/moonshots-upcoming-kimi-3-is-expected-to-close-the-gap-with-anthropics-opus-4-8/ 已经证明可以在编码基准测试中与 Anthropic 和 OpenAI 的最新发布竞争。而像 Groq 和 Cerebras 这样的新芯片制造商也同样吸引了收购方和公开市场的关注。
General Compute 能够获取 Nvidia 生态系统之外的芯片，这一点非常重要。另一家 AI 基础设施公司 TensorWave，也在押注与 AMD 的合作关系。随着越来越多 Nvidia 替代方案的出现，没有绑定 Nvidia 合同的计算提供商可能在提供成本高效的推理服务方面具有优势。
“有一堆芯片开始实现规模化，它们拥有惊人的总拥有成本（TCO），或操作速度远快于 Nvidia，但它们的买家并不多，”Puklowski 说。“通过与 Upper90 合作，这不仅仅是‘一个很酷的新创公司获得了一些资金来购买计算资源’。这实际上是资本开始自我组织以及 Nvidia 垄断地位分散的第一个信号。”
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General Compute, an AI inference cloud startup, has landed a $400 million loan from Upper90, a tech investment firm. It might be the first deal to put up inference-specific chips as collateral — chips built to run already trained AI models quickly and efficiently, rather than the more expensive chips used to build the models in the first place.
The financing is the latest signal that markets are responding to concerns over the price of AI tools and tokens by turning to infrastructure that runs open source models more cheaply than the newest LLMs from frontier labs.
Founded by CEO Finn Puklowski, General Compute raised a $15 million：https://techcrunch.com/2026/05/28/has-the-hunt-for-ai-compute-uncovered-the-next-cerebras/ seed round in May to build an inference neocloud around silicon from SambaNova, an Intel-backed chipmaker. (Neoclouds are purpose-built for AI workloads, unlike the general-purpose infrastructure offered by traditional hyperscalers like AWS or Azure.)
The company’s SN50 chips are designed for inference. They’re power-efficient and don’t require expensive water-cooling systems, which means they can be deployed more quickly than GPUs across a larger variety of data centers. General Compute says the new chips will provide 16 times faster inference than GPU-based clouds.
The challenge is getting a lot of these chips, especially when you’re a brand-new company.
Upper90 co-founder and CEO Billy Libby, a former Goldman Sachs quantitative trader, had a playbook for this: In 2021, his firm financed GPU purchases by Crusoe, the energy-focused data center startup, which he believes was the first loan against the value of advanced chips.
Traditional lenders eschewed such deals at the time because of the risks and uncertainties around GPU depreciation. But as CoreWeave made chips-backed loans into a business model and then the basis of a blockbuster IPO, this kind of financing has become common.
“When we financed Nvidia GPUs as the first group to do that, the market was inefficient,” Libby told TechCrunch. “We could really put together something as an early participant, and kind of get compensated for the risk.”
Now that GPUs are comparatively well understood and perhaps over-bought：https://techcrunch.com/2026/07/09/can-ai-answer-the-3-trillion-question/, Upper90 is turning to companies like General Compute to ride the next wave of the AI boom. “We think open source models are going to be important, and we went and looked for a player last year that was in inference,” Libby said. “Everyone doesn’t need a supercomputer, but they do need inference and AI.”
That thesis has been growing stronger, with companies that provide access to open models, like OpenRouter and Fireworks, raising new rounds at huge valuations. New models like Kimi’s K3：https://techcrunch.com/2026/07/16/moonshots-upcoming-kimi-3-is-expected-to-close-the-gap-with-anthropics-opus-4-8/ have proven to compete with the latest releases from Anthropic and OpenAI on coding benchmarks. And new chipmakers like Groq and Cerebras have drawn interest from acquirers and public markets alike.
General Compute’s ability to access chips outside of Nvidia’s ecosystem matters for the same reason. TensorWave, another AI infrastructure company, is making a similar bet on a partnership with AMD. As more alternatives to Nvidia emerge, compute providers that aren’t locked into Nvidia deals may have an advantage in providing cost-efficient inference.
“There are a bunch of chips that are starting to scale that have amazing [total cost of ownership], or that can operate much faster than Nvidia, but there’s not too many buyers for them,” Puklowski said. “By getting together with Upper90, this is not just, ‘a cool startup got some money to buy some compute.’ Like, this is the first signal of capital organizing itself and the fragmenting of Nvidia’s monopolistic dominance.”
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