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AI Infrastructure & Serving

Explore infrastructure for serving models, managing inference and deploying AI workloads. Compare official resources, requirements and limitations.

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AI Infrastructure & Serving projects

Apache PredictionIO Apache PredictionIO is an open-source machine-learning server and stack for developers and data scientists to build predictive engines, deploy them as web services, and evaluate competing engine variants.

T Trainer Trainer is a Kubernetes-native framework that coordinates distributed AI model training and LLM fine-tuning through TrainJob and Runtime APIs.

D Dstack Dstack is an open-source control plane for provisioning and orchestrating GPU compute across GPU clouds, Kubernetes, virtual machines and on-premises clusters.

M Mooncake Mooncake is an open-source, C++ LLM-serving platform built around a disaggregated architecture centered on KV-cache handling. Its repository describes it as the serving platform for Kimi.

T TensorPool CLI TensorPool CLI is a Python command-line interface for running machine-learning training jobs and accessing on-demand multi-node GPU clusters.

V Vs Mlrt Vs Mlrt is an open-source collection of VapourSynth machine-learning filter runtimes for CPU and GPU inference, with plugins based on OpenVINO, ONNX Runtime, TensorRT, TensorRT-RTX, MIGraphX and NCNN/Vulkan, plus a Python wrapper for selecting bundled models and backends.

C Clearml — clearml ClearML is an open-source Python MLOps/LLMOps toolkit for tracking machine-learning experiments, managing data, orchestrating pipelines and workloads, scheduling jobs, and supporting model-serving workflows.

Bento BentoML is open-source infrastructure for deploying, optimizing, scaling and managing AI/ML model inference and custom inference pipelines in production, including self-hosted deployments.