Tensormesh Platform is a Data Management Layer for AI Inference. The platform enables a multi-tier scalable KV Cache that rapidly reduces miss rate and significantly cuts GPU recompute, addressing AI data challenges related to efficiency, cost, and quality. The solution becomes the foundation of the enterprise’s AI intelligent data, available to operators and agents to manage, analyze, and optimize.
Tensormesh Platform
Automatically places cache across GPU memory, host memory and external storage to maximize hit rate at the lowest cost.
Every placement, eviction and sharing decision is optimized for cache hit rate; the biggest lever on inference cost.
One server reads the prompt and builds the cache. Another uses it to write the answer. Tensormesh passes the cache between them directly, so no work is repeated.
Deploy on Kubernetes with the accelerators, storage and inference engine you already use. No lock-in.
Kubernetes operator and Helm charts, with observability, security and multi-tenancy built in.
$ helm install tensormesh \
tensormesh/platform
✓ operator ready
✓ observability on
✓ tenants isolated
Enterprise
The platform is designed around the constraints that decide whether inference makes it to production.
Deploy inside your own infrastructure, so cached context stays in your environment.
Control what is cached, where it lives and when it is evicted.
Reuse context instead of recomputing it, so the same GPUs serve more requests.
Serve repeated context from the fastest tier available to cut time-to-first-token.
Less recomputation means less energy spent on every request.
Tensormesh’s approach delivers a fundamental breakthrough in efficiency and is poised to become essential infrastructure for any company betting on AI.
Ion Stoica
Co-Founder
, Databricks
Tensormesh enabled distributed KV-cache sharing across servers — delivering performance that exceeded expectations.
RT
Rowan T.
CEO
The LMCache team rapidly adapts and delivers results that stabilize and optimize model hosting. It’s a major step forward for enterprise LLM performance.
PP
Prashant P.
Software Engineer
Our collaboration with LMCache accelerated our GDS open-source release and achieved a 41× reduction in time-to-first-token.
CF
Callan F.
Product Lead
We’ve seen major LLM efficiency and cost savings using the vLLM Production Stack from Tensormesh’s founders.
IB
Ido B.
CEO
reduction in
time-to-first-token
raised from AMD Ventures, CoreWeave, NVentures, and more
Get started
For platform teams
Run on Kubernetes with any accelerator, any storage, any inference engine.
For fast evaluation
Launch the demonstrator and see hit-rate gains on your own prompts.
