Tensormesh
KV-cache management platform that lowers LLM inference cost and latency
About Tensormesh
Tensormesh develops a platform that manages the KV cache for LLM inference across GPU memory, host memory and storage so GPUs avoid recomputing prior work. It deploys on Kubernetes and is associated with the open-source LMCache team.
Common use cases
- LLM inference optimization
- Reducing GPU spend
- Long-context serving
- Agent workloads with repeated prompts
Products and services
Tensormesh Platform
Multi-tier KV cache with cache-aware placement and sharing between prefill and decode servers.
Demonstrator
Free tool to test cache hit-rate gains on a user's own prompts.
Key features
- KV cache management
- Multi-tier caching
- Kubernetes operator
- Cache sharing
- Lower time-to-first-token
- Inference cost reduction
Integrations
- Kubernetes
- vLLM
- LMCache
- NVIDIA GPUs
Tensormesh alternatives
FAQs
Who is Tensormesh designed for?
Teams serving LLMs in production that want to cut inference cost and latency.
How is Tensormesh delivered?
Delivery model: Cloud (SaaS), Hybrid.
How is Tensormesh priced?
Contact sales; free demonstrator.
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