ChainOpera AI - White Paper
ChainOpera AI White Paper
ChainOpera AI White Paper
  • ChainOpera AI: The Blockchain AI Operating System for AI Agents and Applications
  • Overview
    • One Liner
    • What's ChainOpera AI?
    • Why ChainOpera AI?
    • Ecosystem
      • Co-creators
      • Co-owners
      • Platform and Framework Partners
      • AI Hardware: DeAI Phones, Wearable Devices, and Robots
      • TensorOpera GenAI Platform
      • TensorOpera FedML Platform
  • ChainOpera AI OS
    • Flagship Mobile App - AI Terminal
    • AI Agent and App Ecosystem
    • AI Agent Society
    • Federated AI OS
    • Federated AI Platform
  • ChainOpera AI Protocol
    • Overview
    • Multilateral Value Network
    • Co-ownership of AI
    • Co-Creation and The Contribution Model
    • Token Utility
    • Burn and Mint Equilibrium Model
    • Governance
    • Proof of Intelligence
    • Evolution to an L1 AI Chain
  • -
  • Roadmap
  • Team
  • OPEN SOURCE
    • FedML Federated/Distributed Machine Learning Library
  • RESEARCH
    • Research Publication
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  1. Overview
  2. Ecosystem

TensorOpera GenAI Platform

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Last updated 4 months ago

TensorOpera® AI () is an independent C-corp company in the US. At the same time, it contributes part of ChainOpera AI Foundation.

TensorOpera® AI is the next-gen cloud service for LLMs & Generative AI. It helps developers to launch complex model training, deployment, and federated learning anywhere on decentralized GPUs, multi-clouds, edge servers, and smartphones, easily, economically, and securely.

Highly integrated with , TensorOpera AI provides holistic support of three interconnected AI infrastructure layers: user-friendly MLOps, a well-managed scheduler, and high-performance ML libraries for running any AI jobs across GPU Clouds.

A typical workflow is showing in figure above. When developer wants to run a pre-built job in Studio or Job Store, TensorOpera®Launch swiftly pairs AI jobs with the most economical GPU resources, auto-provisions, and effortlessly runs the job, eliminating complex environment setup and management. When running the job, TensorOpera®Launch orchestrates the compute plane in different cluster topologies and configuration so that any complex AI jobs are enabled, regardless model training, deployment, or even federated learning. TensorOpera®Open Source is unified and scalable machine learning library for running these AI jobs anywhere at any scale.

In the MLOps layer of TensorOpera AI

  • TensorOpera® Studio embraces the power of Generative AI! Access popular open-source foundational models (e.g., LLMs), fine-tune them seamlessly with your specific data, and deploy them scalably and cost-effectively using the TensorOpera® Launch on GPU marketplace.

  • TensorOpera® Job Store maintains a list of pre-built jobs for training, deployment, and federated learning. Developers are encouraged to run directly with customize datasets or models on cheaper GPUs.

In the scheduler layer of TensorOpera AI

  • TensorOpera® Launch swiftly pairs AI jobs with the most economical GPU resources, auto-provisions, and effortlessly runs the job, eliminating complex environment setup and management. It supports a range of compute-intensive jobs for generative AI and LLMs, such as large-scale training, serverless deployments, and vector DB searches. TensorOpera® Launch also facilitates on-prem cluster management and deployment on private or hybrid clouds.

In the Compute layer of TensorOpera AI

  • TensorOpera® Deploy is a model serving platform for high scalability and low latency.

  • TensorOpera® Train focuses on distributed training of large and foundational models.

  • TensorOpera® Federate is a federated learning platform backed by the most popular federated learning open-source library and the world’s first FLOps (federated learning Ops), offering on-device training on smartphones and cross-cloud GPU servers.

  • TensorOpera® Open Source is unified and scalable machine learning library for running these AI jobs anywhere at any scale.

https://TensorOpera.ai
TensorOpera open source library