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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Open Model Engine (OME) — Kubernetes operator for LLM serving, GPU scheduling, and model lifecycle management. Works with SGLang, vLLM, TensorRT-LLM, and Triton
| Date | Stars |
|---|---|
| 2026-07-24 | 481 |
| 2026-07-25 | 482 |
| 2026-07-28 | 482 |
| 2026-07-30 | 482 |
| 2026-07-31 | 482 |
| 2026-08-06 | 482 |
Today
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This week
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Momentum
35.0
growth rate 0.00%/day
# OME (Open Model Engine) — Kubernetes Operator for LLM Serving
[](https://goreportcard.com/report/sigs.k8s.io/ome)
[](https://github.com/ome-projects/ome/releases/latest)
[](https://ome-projects.github.io/ome/docs/reference/ome.v1beta1/)
[](LICENSE)
[](https://deepwiki.com/ome-projects/ome)
<div align="center">
<a href="https://github.com/ome-projects/ome">
<img src="site/assets/icons/logo-clear-background.png" alt="OME Logo" width="300" height="auto" style="max-width: 100%;">
</a>
</div>
## What is OME?
OME (Open Model Engine) is a Kubernetes operator for enterprise-grade management and serving of Large Language Models (LLMs). It optimizes the deployment and operation of LLMs by automating model management, intelligent runtime selection, efficient resource utilization, and sophisticated deployment patterns.
Read the [documentation](https://ome-projects.github.io/ome/docs/) to learn more about OME capabilities and features.
## Features Overview
- **Model Management:** Models are first-class citizen custom resources in OME. Sophisticated model parsing extracts architecture, parameter count, and capabilities directly from model files. Supports distributed storage with automated repair, double encryption, namespace scoping, and multiple formats (SafeTensors, PyTorch, TensorRT, ONNX). See the [supported models reference](config/models/SUPPORTED_MODELS.md) for a comprehensive list of pre-configured models including Llama, Qwen, DeepSeek, Gemma, Phi, and 80+ other model families.
- **Intelligent Runtime Selection:** Automatic matching of models to optimal runtime configurations through weighted scoring based on architecture, format, quantization, parameter size, and framework compatibility.
- **Optimized Deployments:** Supports multiple deployment patterns including prefill-decode disaggregation, multi-node inference, and traditional Kubernetes deployments with advanced scaling controls.
- **Resource Optimization:** Specialized GPU bin-packing scheduling with dynamic re-optimization to maximize cluster efficiency while ensuring high availability.
- **Runtime Integrations:** First-class support for [**SGLang**](https://github.com/sgl-project/sglang) - the most advanced inference engine with cache-aware load balancing, multi-node deployment, prefill-decode disaggregated serving, multi-LoRA adapter serving, and much more. Also supports [**vLLM**](https://github.com/vllm-project/vllm) for high-throughput inference and Triton for general model inference.
- **Accelerator Management:** Hardware-aware scheduling through AcceleratorClass resources that define GPU capabilities, discovery patterns, and cost information. Enables intelligent accelerator selection with policies like BestFit, Cheapest, or MostCapable.
- **Web Console:** Modern web interface for managing models, serving runtimes, and inference services with real-time updates and HuggingFace model search integration.
- **Kubernetes Ecosystem Integration:** Deep integration with modern Kubernetes components including [Kueue](https://kueue.sigs.k8s.io/) for gang scheduling of multi-pod workloads, [LeaderWorkerSet](https://github.com/kubernetes-sigs/lws) for resilient multi-node deployments, [KEDA](https://keda.sh/) for advanced custom metrics-based autoscaling, [K8s Gateway API](https://gateway-api.sigs.k8s.io/) for sophisticated traffic routing, and [Gateway API Inference Extension](https://gateway-api-inference-extension.sigs.k8s.io/) for standardized inference endpoints.
- **Automated Benchmarking:** Built-in performance evaluation through the BenchmarkJob custom resource, supporting configurable traffic patterns, concurrent load teExcerpt of 7,614 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c9cb0a415aebf514, topic:llm-inference, topic:model-serving, topic:vllm
matched fp:c9cb0a415aebf514, topic:llm, topic:llama, topic:qwen