☁️ Build multimodal AI applications with cloud-native stack
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☁️ Build multimodal AI applications with cloud-native stack
Open-source inference platform for language, vision, embedding, reranking, image, audio, and video models. Deploy with multiple engines across heterogeneous hardware, from a laptop to a distributed cluster. Serve models through OpenAI-compatible APIs.
Containers for machine learning
A framework for efficient model inference with omni-modality models
Statistical Machine Intelligence & Learning Engine
A flexible, high-performance serving system for machine learning models
Standardized Distributed Generative and Predictive AI Inference Platform for Scalable, Multi-Framework Deployment on Kubernetes
A GPU cluster manager for high-performance AI model serving (vLLM, SGLang) and on-demand SSH-accessible GPU instances.
Dockerized OpenAI-compatible wrapper for Kokoro-82M text-to-speech w/multiplatform CPU, AMD, NVIDIA GPU PyTorch; multi-speaker, clone-tuning, caption timestamps, SSML, websockets, optional readalong web UI
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
TranslatedOffline translation model server with low resource consumption, fast speed, and private deployment capability.
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, TensorOpera AI (https://TensorOpera.ai) is your generative AI platform at scale.
Hopsworks - Data-Intensive AI platform with a Feature Store
Detect the sentiment captured in short pieces of text
Generate a summarized description of a body of text