Tensormesh builds and sells Tensormesh Inference, a SaaS inference platform that uses KV caching to store and reuse computed model state to avoid redundant GPU computation. The platform delivers up to 10x reductions in latency and GPU spend by serving repeated context from cache and reports cache hit rates, token-level cost breakdowns, and other performance metrics in real time. Tensormesh offers serverless, OpenAI-compatible API access for immediate use and reserved deployments for enterprises needing dedicated capacity and SLAs. The company also maintains and contributes to the open-source LMCache project (over 8,000 GitHub stars) and highlights integrations across vLLM, SGLang, TensorRT, and multiple cloud vendor offerings. Tensormesh is led by CEO Junchen Jiang and was founded by faculty, PhD researchers, and alumni from the University of Chicago, UC Berkeley, and Carnegie Mellon. The company has raised $24.5 million in total funding and markets a pricing model that bills cached input tokens at $0 for serverless deployments, with well-optimized customers regularly achieving cache hit rates above 70%.

Total raised

$25M

Funding rounds

2

Latest round

Seed

Latest activity

May 2026