KV-cache compression and direct-decode software for supported long-context LLM serving.
See what it solvesTHE PRODUCT, IN PRACTICE
Long documents, code histories and retrieval conversations increase the cache a model must retain while generating responses. SpinCache addresses that footprint with a compact KV-cache representation and supported direct decode. Streaming updates and the Transformers integration connect the cache tier to a continuing serving workload rather than treating compression as a one-time offline task.
Retaining more conversation or document context can make the KV cache a limiting part of a serving deployment.
SpinCache compresses the stored keys and values to reduce their footprint within the supported model and cache configuration.
A compact offline representation is not enough if the serving path cannot use it as the context changes.
Supported direct decode and streaming append let the compact cache participate in the ongoing generation workflow.
A setting that saves memory may change model quality or introduce runtime overhead.
The product’s configurable scope supports deployment-specific trade-offs. Evaluate the chosen setting on your model, and pair it with SpinProof when that assessment is required.
The product scope below describes the capability and outputs to confirm for your release. Custom integration, operated delivery and additional engineering are separately agreed.
KV-cache compression for the supported models and settings.
Direct use of the compact cache in the product’s decode integration.
Append support for context that grows during the serving session.
The supplied cache and attention integration for the qualified environment.
SpinCache is fully available with CLI, MCP and headless access. Use the interface that fits your terminal, your agents or your automated workflow.
Run the product from a terminal or script, alongside the tools your team already uses.
Get CLI accessConnect MCP-compatible clients and agents to the product’s exposed capabilities.
Get MCP accessOperate without a graphical interface in unattended, scheduled or pipeline-driven work.
Set up headless accessProduct-specific inputs, hardware requirements and result guarantees still apply. An access mode does not change your license or deployment scope.
Explore all product interfacesA team wants to compare a compact cache with its current long-context serving configuration.
Illustrative use of the supported scope, not a customer case study or a measured result.Choose the supported model, representative documents and quality limits.
Integrate the cache tier and run the same serving workload.
Compare cache memory, useful concurrency, latency and output quality together.
Explore the product workflow, from your input to a result you can use.
Attention keys and values accumulate as the context grows.
Interactive explanation. This illustration does not execute customer workloads or display measured performance.
LLM-serving teams building document applications, code agents and retrieval workflows with supported Transformers models.
CLI, MCP and headless access are all available. Run from a terminal or script, connect an MCP-compatible client, or use the product without a graphical interface. Choose the mode during onboarding and receive its setup instructions. Runtime requirements and supported operations remain product-specific.
No universal compression ratio or quality-preservation claim applies to every model. SpinCache addresses representation and decode; SpinMem addresses placement and KX MemoryForge has a separate offload-and-restore runtime.
The agreed SpinCache release, its supported interfaces and outputs, the applicable checks and the support stated in your order. The plan defines users, workflow, environment and capacity. Source-code transfer, unrestricted redistribution and bespoke engineering are not automatically included.
Request a scoped engineering or operated-service proposal. It identifies what our team delivers, where the workload runs, infrastructure costs, acceptance requirements and ongoing support. Your software subscription alone does not activate a hosted runtime.
Explore engineeringAgree the model or workload, current baseline, environment, required output and acceptance tests. Evaluate the selected release against those conditions. A product description or a test count does not establish a gain on a different customer workload.
Plan an integrationOne named production deployment. Shared engine rights for this application included.
Scope & packagesBring a representative workload and the result your team needs. We’ll define the product, deployment and engineering scope around that task.
Activate this capability inside a qualified ai serving workflow. Compatible methods and required shared checking are included once within that workflow; the product remains independently purchasable.
Capacity, release, environment, result contract and operating responsibilities are specified before activation. Choosing an additional method is not permission to add an unrelated workload or unqualified deployment.
Explore AI Serving Choose workflow capacityCommission the model, integration and acceptance work needed for your task. Existing products stay independently available.
Use the software directly or ask us to scope the implementation.
One capability. A connected workflow.
A team to put it into practice.