Thunder Compute provides a cost-effective GPU cloud platform enabling users to self-host AI/ML workloads. Utilizing virtualized GPUs hosted on AWS/GCP, it significantly reduces expenses by 80%. Perfect for AI/ML development, prototyping, and inference, Thunder Compute's quick setup and powerful hardware ensure users can execute their tasks efficiently and affordably.
Thunder Compute Core Features
Instance templates
VS Code integration
CLI management
Thunder Compute Pro & Cons
The Cons
No public open-source code repository available
Limited information on support for mobile or browser extensions
Potential dependency on cloud infrastructure availability and network reliability
The Pros
Significantly lower GPU prices compared to AWS (up to 80% savings)
Seamless integration with VS Code for native AI/ML development
Flexible GPU instances with customizable specs, storage, and snapshots
Simple setup process without need for SSH keys or CUDA installs
Support for persistent environments for consistent development
