Place, share, and reuse KV cache across GPU memory, CPU memory, and external storage without changing the way your teams run inference.
L0GPU memoryL1CPU memoryL2External storagePlatform overview
Tensormesh Platform cuts AI inference compute costs by intelligently managing KV cache across GPU memory, CPU memory, and external storage.
It adapts to prefix or non-prefix caching, prefill/decode disaggregation, and cross-node cache sharing to deliver best-in-class cache hit rates and time to first token. Helm chart installation, web UI, and CLI support setup and ongoing operations.
The operational realities that decide whether inference makes it to production.
Keep cached inference state inside the environments you control.
Control where reusable state lives and when it is evicted.
Reuse context instead of recomputing it on every request.
Serve repeated context from the fastest available tier.
Reduce repeated compute and the energy cost of inference.
What’s inside Tensormesh Platform
A modular platform. A production-ready release for KV cache management and prompt caching, a shared platform core for operations and multi-tenancy, and infrastructure compatibility across every stack we’ve seen.
Interfaces
Data & Cache Mgmt
Tensormesh Shared Platform Core
Infrastructure Compatibility
The release delivers KV cache control, prefix + non-prefix prompt caching, prefill/decode disaggregation, and P2P KV cache sharing — accessible through the operator UI and CLI. Underneath, the shared platform core provides the K8S operator, security, observability and multi-tenancy. Everything runs on any Kubernetes cluster, any accelerator, any storage, and any inference engine.
Why Tensormesh Platform
Tensormesh Platform is designed for the teams putting AI inference into production — where cache miss rate, time to first token, and operational cost decide whether a workload ships or stalls.
Multi-tier KV cache
Manage KV state intelligently across GPU memory, CPU memory, and external storage — with the platform deciding what lives where, based on your workload and hit-rate targets.
Hit-rate first
Deliver best-in-class cache hit rates and time to first token by combining prefix caching, non-prefix caching for dense models, and P2P KV cache sharing across GPU nodes.
Advanced configurations
Advanced inference patterns — P/D disaggregation, KV cache sharing across nodes — are first-class capabilities, not one-off engineering projects layered on top of a stock cache.
Day 1 and Day 2
Helm chart–based installation gets you live quickly. A web UI paired with a command-line interface gives operators and platform teams the tools they need for both Day 1 setup and Day 2 operations.
$ helm install tensormesh
✓ operator ready
✓ cache policy applied
→ open operator UI
Get started
Talk to us
Talk to our team about deploying Tensormesh Platform alongside your existing inference engines, storage, and observability stack.
Try it Serverless
Validate performance benefits and cost reduction on your own prompts — no infrastructure to set up. Launch the Tensormesh Platform Demonstrator and see the results in minutes.