---
title: AI serving & long context | SpinForge
url: https://spin-forge.com/solutions/ai-serving
description: Connect expert routing, cache placement, compression, kernel tuning and evaluation around your accepted serving workload.
---

AI SERVING & LONG CONTEXT [The full offering ](https://spin-forge.com/platform)

# More useful
compute, per request.

Connect expert routing, cache placement, compression, kernel tuning and evaluation around your accepted serving workload.

[Discuss the workload](https://spin-forge.com/contact?product=solution-ai-serving)

RELEVANT CAPABILITIES

## A starting point.
Not a compulsory bundle.

These are possible combinations. The supported interfaces, execution route and final acceptance model are agreed for the deployment.

[Application

### SpinRoute

Expert-routing and load-balancing software for mixture-of-experts AI serving.](https://spin-forge.com/spinroute) [Application

### SpinMem

KV-cache and memory-tier placement software, supplied with a service interface and a vLLM integration hook.](https://spin-forge.com/spinmem) [Application

### SpinKernel

GPU kernel autotuning and code-generation software for supported Triton tile configurations.](https://spin-forge.com/spinkernel) [Application

### SpinProof

AI-performance assessment software that profiles a workload, prioritizes optimization changes and checks their effect on model quality.](https://spin-forge.com/spinproof) [Application

### SpinCache

KV-cache compression and direct-decode software for supported long-context LLM serving.](https://spin-forge.com/spincache) [AI INFRASTRUCTURE

### KX MemoryForge

A multi-tier LLM KV-cache runtime for compressing, offloading and restoring cache in a supported memory and storage environment.](https://spin-forge.com/kx-memoryforge) [AI INFRASTRUCTURE

### AgentForge

An operating layer for generating, scheduling, validating, storing and replaying agentic-AI workloads across CPU/GPU infrastructure.](https://spin-forge.com/agentforge)

FROM PROBLEM TO PRODUCTION

## Scope the work.
Measure the outcome.

Agree the original workload, constraints, quality and comparison baseline. Choose the supported implementation and review the complete end-to-end result.

[Relevant engineering program](https://spin-forge.com/engineering/inference) [Deployment options](https://spin-forge.com/deployment)