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AI infrastructure startup Upscale AI completes $190 million financing at a valuation of $2 billion, led by Premji Invest, with participation from Nvidia and others

According to Techstartups, AI infrastructure startup Upscale AI announced the completion of a new round of financing totaling $190 million, with investors including NVIDIA and Salesforce Ventures. After this round of financing, the company's valuation reached $2 billion.This round of financing is part of its Series A extension, bringing Upscale AI's total financing to $500 million, led by Premji Invest, with new investors such as Temasek and Seligman Ventures participating, while existing shareholders including Maverick Silicon, Mayfield, Prosperity7 Ventures, StepStone Group, and Tiger Global continue to invest.Upscale AI focuses on building network infrastructure for large-scale AI clusters, attempting to solve the "computing power interconnection bottleneck" problem in AI training and inference. The company is developing a full-stack AI network architecture covering chips, systems, and software, aiming to create an open standard network interconnection solution suitable for large-scale AI workloads.The company stated that as the scale of AI clusters continues to expand, network performance is becoming a key limiting factor affecting GPU utilization and training efficiency. Inefficient interconnection can directly lead to wasted computing power and increased training costs. Currently, Upscale AI has begun collaborations with several hyperscale cloud providers and "new cloud" infrastructure providers for evaluation and deployment, but has not disclosed specific client information.Analysts point out that investment in AI infrastructure is spreading from the model layer to the underlying hardware and network layers, with network interconnection becoming one of the most关注的 key infrastructure tracks following GPUs.

Analysis: Chinese AI companies such as Zhipu and MiniMax have high valuation multiples, with sales multiples exceeding those of their American counterparts by dozens of times

According to an analysis by Tommy, there is a significant gap in valuation and revenue conversion for Chinese open-source AI companies, with their price-to-sales ratio (P/S) far exceeding that of leading counterparts in the United States.Data shows that Zhipu, which developed the GLM 5.2 model, currently has a market value of approximately $137 billion, but its revenue for the fiscal year 2025 is about $107 million, resulting in a price-to-sales ratio as high as 1280 times; MiniMax has a market value of about $23 billion, with a price-to-sales ratio of approximately 290 times. In contrast, the valuations of leading AI laboratories in the United States are more solid, with OpenAI (valued at about $852 billion) and Anthropic (valued at about $965 billion) having price-to-sales ratios of only 34 times and 21 times, respectively.It is believed that due to overseas users' concerns about data privacy, they are unwilling to send data directly to China, resulting in the massive demand for Chinese AI companies not being converted into actual API revenue, leading to significant profit loss to overseas third-party inference service providers (such as OpenRouter, etc.). To support their current high valuations, Chinese AI companies urgently need to prove their data non-retention mechanisms and capture the market at low prices, or explore revenue-sharing and initial licensing collaborations with overseas inference platforms to expand their actual revenue scale.
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