Tensormesh, the creators of LMCache, the world's number one open-source KV caching technology, today announced Tensormesh Platform, the data management layer for AI inference. This multi-tier, scalable KV cache management software deploys across infrastructure with an enterprise Kubernetes deployment enabling enterprises to optimize efficiency, cost, and quality of every inference they run.
"LLM workloads recompute the same context endlessly. That wastes GPU time and inflates time-to-first-token," explained Junchen Jiang, Tensormesh CEO. "Tensormesh Platform addresses these costly problems, bringing production-grade KV cache reuse to enterprise on-prem inference stacks and making it operational at fleet scale."
Tensormesh Platform includes management capabilities enterprises need to run KV cache in production including security protecting cached data across every tier, observability providing real-time visibility into cache performance, resilience with high availability, policy management controlling storage sharing and eviction, and multi tenancy letting organizations serve multiple teams with isolated data.
"Tensormesh delivered strong results in our P2P KV cache transfer and resiliency benchmarks across multiple models. We're excited to see them continue pushing the ecosystem forward," said Patrick Lundquist, senior software engineer at Nebius.
Enterprises deploying Tensormesh Platform also benefit from tiered caching managed across HBM, CPU and storage layers automatically, prefix caching reusing computed KV cache for opening part of prompt, NonPrefix Caching CacheBlend reusing blocks anywhere in prompt ideal for RAG, peer-to-peer KV transfer fetching directly from any peer, and advanced cache controls allowing pin, lookup, move, compress and clear via controller APIs.
"Customers are under tremendous pressure both to drive efficiency through AI and to manage the costs of AI infrastructure that powers such capabilities," said Rhys Oxenham, general manager of AI at SUSE. "The combination of Tensormesh and our SUSE AI Factory built on our open infrastructure platform empowers organizations to do both."
"AI has moved beyond a model challenge. It is now an infrastructure and application-development challenge," said Paul Nashawaty, principal analyst with theCUBE and ECI Research. "Our AppDev research shows nearly two-thirds of enterprises deploy applications daily or multiple times a day, and 66% of developers report frustration when AI solutions are almost right but need more iteration and debugging. That translates directly into wasted compute, developer friction, and rising enterprise costs. GPU utilization, not GPU ownership, determines the economics of AI. Tensormesh gives enterprises a way to improve utilization and reduce unnecessary inference now, before escalating AI costs force the decision."
About Tensormesh
Tensormesh is a technology company specializing in AI memory infrastructure and enterprise-grade software, and also known as the leader in smart AI-native data management and caching-accelerated inference optimization for enterprise AI. Founded by faculty, Ph.D. researchers, and alumni from the University of Chicago, UC Berkeley, and Carnegie Mellon, and led by Junchen Jiang, University of Chicago faculty member and co-creator of LMCache, Tensormesh builds on years of academic research in distributed systems and AI infrastructure. The company has raised $28.5 million in total funding and is backed by Valley Capital Partners, NVentures, AMD Ventures, CoreWeave, Laude Ventures, and more.
