As the AI infrastructure boom continues to accelerate, companies are under growing pressure to maximize the efficiency of their GPU resources, particularly during inference—the process of running trained AI models. Tensormesh, a startup emerging from stealth this week, is tackling this challenge head-on with a $4.5 million seed funding round led by Laude Ventures, joined by angel investment from database pioneer Michael Franklin. The company is building a commercial version of LMCache, an open-source utility originally developed and maintained by Tensormesh co-founder Yihua Cheng. LMCache has already gained traction in the open-source AI community for its ability to cut inference costs by up to 10 times. Its adoption has been embraced by major players including Google and Nvidia, underscoring its technical value. At the heart of Tensormesh’s innovation is the key-value cache (KV cache), a critical component in processing complex AI queries. Traditional systems discard the KV cache after each inference request, even though it contains valuable information that could be reused. According to Tensormesh CEO Junchen Jiang, this is a major inefficiency—comparable to hiring a brilliant analyst who forgets everything after answering one question. Tensormesh’s solution preserves and reuses the KV cache across queries, enabling faster and more efficient inference without requiring additional GPU memory. The system intelligently manages data across multiple storage layers—balancing speed and cost—so that the same GPU infrastructure can handle significantly more work. This approach is especially impactful for interactive AI applications like chat interfaces and agentic systems, where models must continuously reference expanding conversation histories or action logs. By retaining and repurposing cached data, Tensormesh reduces redundant computation and lowers latency. While companies could theoretically build such systems in-house, the technical complexity is substantial. Jiang notes that some organizations have assembled teams of 20 engineers and spent months developing custom solutions. Tensormesh aims to simplify this process by offering a plug-and-play product that delivers high performance without the engineering overhead. “We’ve seen teams struggle for months to implement efficient KV cache reuse,” Jiang said. “With our system, they can achieve the same result in days, with far less effort.” By turning academic research into a scalable product, Tensormesh is positioning itself at the intersection of performance, cost, and practicality—key factors in the next phase of AI infrastructure evolution.