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Cerebrium

Machine learning made easy with serverless GPU infrastructure.
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What is Cerebrium?

Cerebrium is a serverless GPU infrastructure platform designed for machine learning. It allows users to run machine learning models in the cloud with scalability and high performance, paying only for the resources they consume. The platform offers a range of GPU types, including H100, A100, A5000, and others, providing flexibility for different workloads. Cerebrium supports infrastructure as code, enabling users to specify their environments in code and eliminating the need to manage S3 buckets. Additionally, it provides volume storage for storing files or model weights, and integrates with secure credentials for frameworks and platforms. The platform offers hot reloading, allowing developers to change a line of code and see the result immediately on a GPU container, promoting rapid iteration. Cerebrium also includes observability features for real-time logging and monitoring, cost breakdowns, alerts, and performance profiling.

 


 

⚡Top 5 Cerebrium Features:

  1. Serverless GPU Infrastructure: Run machine learning models in the cloud scalably and performantly.
  2. GPU Variety: Select from over 8 different GPU types, including H100’s, A100’s, and A5000’s.
  3. Infrastructure as Code: Specify your environments in code and let Cerebrium create it for you.
  4. Observability: Real-time logging and monitoring with alerts, logs, and performance profiling.
  5. Scalability: Scale without worrying about latency or redundancy, with a 99.99% uptime and minimal failure rates.

 


 

⚡Top 5 Cerebrium Use Cases:

  1. Deploying Prebuilt Models: Access to pre-built models for various tasks like image generation, text classification, etc.
  2. Streaming Endpoints: Stream output back to your users as soon as results are ready.
  3. Hot Reload: Change a line of code and see it live on a GPU container for faster iteration.
  4. Cost Breakdowns: See your cost breakdown per model per minute, separated across GPU, CPU, and memory.
  5. Deploy in Your Own Infrastructure: Meet your data requirements by deploying on your own infrastructure (Alpha).
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