Warning
Under Active Development. This project is incomplete and may contain breaking changes. It is not yet recommended for production use.
Feedback and contributions are welcome - see CONTRIBUTING.md.
Modern AI workloads require KV cache infrastructure that delivers speed, scale, and resilience.
KV Cache Runner (KVCR) reimagines KV cache as a system-wide distributed resource: available across memory and storage tiers, resilient by design, and managed holistically to unlock system-level optimizations and maximize end-to-end performance.
KVCR is agentic-native and built to accelerate AI workloads of every kind.
KVCR is at its most powerful when working in tandem with a KV-aware request router. The router knows where the KV cache resides and can provide the selected worker with hints about where to retrieve it. The cache can be sourced locally or from remote peers, within or across memory and storage tiers, or from any combination of these sources.
KVCR leaves local KV cache offloading to host memory under the engine's control. It focuses on system-level optimizations enabled by this architecture, including cross-node KV cache sharing, KV-aware request load balancing, and KV cache prefetching — all guided by the router’s system-wide view. By maximizing KV cache reuse and overlapping cache onboarding with computation, KVCR reduces redundant work and improves prefill efficiency.
KVCR is resilient by design. KVCR-Guard is a sidecar KVCR process operating in active-passive mode and can outlive an engine failure. In such an event, KVCR-Guard remains available as a remote source of KV cache, minimizing disruption to serving in the face of failures.
KVCR provides a flexible policy interface that allows its behavior to be customized for different workloads.
For more details, see the design document.
kvcr.api: Contains the user-facing API for configuring KVCR and interacting with its core.kvcr.policy: Contains the policy interface for customizing KVCR's behavior for different workloads.
KVCR is vendor and framework agnostic, with no dependency on a specific request router. The list below summarizes framework integrations that are complete or in progress. We plan to extend support to additional frameworks and routers in the future.
-
TRT-LLM
-
vLLM
-
SGLang
-
Dynamo KV Router
-
TRT-LLM
-
vLLM
-
SGLang
Public E2E availability is pending an upcoming vLLM PR containing the KVCR integration. The quick start describes the planned container workflow and the public source information it will require.
For local development, API lifecycle guidance, validation, integration, and KVCR service usage, see the developer guide.
See PROVENANCE.md for the immutable source commit, which modules were extracted from it, and which were written in this repository.
KVCR is released under the Apache License 2.0. The full license text is in LICENSE.
KVCR package source files carry an SPDX Apache-2.0 identifier and the NVIDIA copyright notice. The vLLM integration files planned for the public quick-start build retain their Apache-2.0 contributor headers.
The public E2E quick start is pending an upcoming vLLM PR containing six
integration files. They are not stored in this repository or included in the
nvidia-kvcr wheel. Until the PR exists, the Dockerfile uses explicit pending
placeholders and stops before fetching source. Its exact public revision will
be recorded in PROVENANCE.md when available.
KVCR declares runtime dependencies on msgspec, pyzmq, and nixl. Each is
installed from its own distribution under its own license. None of them are
redistributed by this repository or bundled into the nvidia-kvcr wheel, which
packages only src/kvcr.
This project accepts external contributions. See CONTRIBUTING.md for the workflow.
Contributions require a Developer Certificate of Origin sign-off
(git commit -s), whose full text is reproduced in that file and which is
enforced on every pull request by the dco CI workflow. Participation is
governed by CODE_OF_CONDUCT.md.
To report a security vulnerability, do not open a public issue — follow SECURITY.md.
