The Technology team guided TensorMesh, Inc. in $4.5 million in seed funding led by Laude Ventures. Tensormesh's technology eliminates redundant computation in AI inference, reducing latency and GPU spend by up to 10x while giving enterprises full control of their data and infrastructure. TensorMesh is the first commercial platform to optimize caching for large-scale AI inference, pairing LMCache-inspired techniques with enterprise-grade usability, security, and manageability. The company's focus on preserving and reusing the KV cache allows for a more efficient processing of complex inputs, leading to substantial cost savings and improved performance. TensorMesh's technology is designed to be cloud-agnostic, allowing for deployment as a SaaS product or standalone software, giving customers control over their own infrastructure.
Tensormesh is the AI infrastructure optimization company enabling up to 10x faster inference efficiency for large language models and genetic systems while keeping full control of data and deployment. Founded by faculty and researchers from the University of Chicago, UC Berkeley, and Carnegie Mellon, TensorMesh commercializes state-of-the-art research to eliminate GPU waste and latency. The software captures and reuses intermediate data other systems discard, delivering breakthrough performance on infrastructure customers own and control.
The Goodwin deal team was led by Craig Schmitz, Chris Garcia, Haley Schubert-Nieh, Jim Riley, Lizzy Song, and Maggie Wong with invaluable assistance from Natalia Yaroshevskiy.
For more information on the deal, please read the press release.