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About Modal Labs
Run generative AI models, large-scale batch jobs, job queues, and much more.
Bring your own code — we run the infrastructure.
Perfect for bread-and-butter compute workloads
AI Inference
Fine-tuning
Batch Processing
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Supercomputing scale
100x more resources than AWS Lambda: CPU, GPU, RAM, and disk.
Serverless pricing
Pay only for resources consumed, by the second, as you spin up containers.
Powerful data structures
Define distributed queues and key-value stores with a single line of Python.
Modal Labs key features
- High-performance AI infrastructure designed for running inference, training, and batch processing with sub-second cold starts and instant autoscaling.
- Composable Modal SDK allows developers to manage their cloud environment in Python, specifying logic and hardware in one place.
- Elastic cloud capacity enables instant autoscaling from 0 to 1000+ GPUs without commitments or capacity planning.
- Out-of-the-box observability features provide integrated logging and visibility into functions, sandboxes, and containers for robust application development.
- Supports multi-modal inference and training, allowing for various AI workloads including LLMs, audio, image/video generation, and reinforcement learning.
Modal Labs use cases
- Run inference, training, and batch processing for AI applications with sub-second cold starts and instant autoscaling.
- Deploy and scale inference for various models, including LLMs, audio, and image/video generation.
- Fine-tune open-source models on single or multi-node clusters with minimal setup.
- Programmatically create secure, ephemeral environments for running untrusted code in sandboxes.
- Execute parallel hyperparameter sweeps and manage thousands of concurrent training trajectories efficiently.
Useful for
- High-performance AI infrastructure optimized for inference, training, and batch processing.
- Instant autoscaling and sub-second cold starts for efficient resource management.
- Integrated observability tools for monitoring and debugging production-ready applications.
- Support for multi-modal inference and fine-tuning across various AI models and frameworks.
- Secure, isolated sandboxes for running untrusted code and managing concurrent AI workloads.
Price
- Pricing starts at $30 per month for access to AI infrastructure.
- Offers free compute options for users.
- Provides elastic cloud capacity with instant autoscaling from 0 to 1000+ GPUs.
- No commitments or capacity planning required for GPU access.
- Includes out-of-the-box observability tools for production-ready applications.
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