Introducing LLMKube: Kubernetes for Local LLMs

Today, we're excited to introduce LLMKube - an open-source Kubernetes operator that brings production-grade orchestration to local LLM deployments. If you've ever struggled to deploy AI workloads in air-gapped environments, edge locations, or regulated industries, LLMKube is for you.

The Problem

The AI revolution has a connectivity problem. While cloud-based LLM APIs like OpenAI and Anthropic are incredible, they're off-limits for huge swaths of the economy:

• Defense and Government: Classified environments can't send data to external APIs
• Healthcare: HIPAA compliance makes cloud APIs risky for PHI data
• Manufacturing: Factory floors often have poor or no connectivity
• Financial Services: Data sovereignty requirements prohibit external processing

Local LLMs solve the connectivity problem, but create a new one: how do you run them in production? Most teams resort to fragile Jupyter notebooks or hand-rolled deployment scripts. There's no standardization, no observability, and no way to enforce SLOs.

The Solution: Treat Intelligence as a Workload

We realized that AI inference isn't fundamentally different from any other workload. It needs:

• Declarative configuration
• Automated deployment and scaling
• Health checks and self-healing
• Observability and metrics
• SLO enforcement
• Security and compliance features

Sound familiar? That's exactly what Kubernetes provides for microservices. So we built LLMKube as a Kubernetes operator that extends the platform with AI-specific primitives.

How It Works

LLMKube introduces two Custom Resource Definitions (CRDs):

1. Model

Define which LLM you want to use:

apiVersion: inference.llmkube.dev/v1alpha1
kind: Model
metadata:
  name: phi-3-mini
spec:
  source: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf/...
  hardware:
    accelerator: cuda

2. InferenceService

Deploy an inference endpoint:

apiVersion: inference.llmkube.dev/v1alpha1
kind: InferenceService
metadata:
  name: phi-3-inference
spec:
  modelRef: phi-3-mini
  replicas: 3

That's it. LLMKube handles downloading the model, creating Kubernetes Deployments, exposing Services, and providing OpenAI-compatible API endpoints.

What's Next

This is just the beginning. Our roadmap includes:

• SLO Enforcement: Automatic scaling and failover to meet latency targets
• Model Sharding: Distribute large models across multiple nodes
• eBPF Observability: Deep token-level tracing and PII detection
• TEE Support: Secure enclaves for sensitive workloads
• Natural Language Deployment: "Deploy a 70B model with P99 latency under 2s"

Get Started

LLMKube is open source (Apache 2.0) and ready to try:

• View on GitHub
• Read the docs
• Join our community

We'd love to hear your feedback and learn about your use cases. Let's build the future of AI infrastructure together.