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Description

Lium serves as a platform where users can rent NVIDIA GPUs on an hourly basis, with various providers listing their machines, leading to competitive pricing driven by the marketplace dynamics. Customers have the flexibility to rent either an individual GPU or an entire 8-GPU node, and they can initiate a container in approximately one minute, gaining access through SSH and Jupyter. Current hourly rates are set at $0.16 for an RTX 3090, $0.27 for an RTX 4090, $0.49 for an RTX 5090, and $4.23 for an H200, with billing occurring on a per-second basis, meaning a job that lasts 12 minutes will only charge for those 12 minutes. To facilitate deployment, Lium offers a command line interface and a Python SDK, along with a free public API that allows users to check live pricing and availability. New users are welcomed with a $5 credit, and the service operates without the need for contracts or minimum commitments. This platform is specifically designed to accommodate machine learning tasks, including training, fine-tuning, inference, and batch processing, particularly when traditional hyperscaler pricing models may not be favorable. By providing such accessible options, Lium empowers developers and researchers to efficiently utilize powerful GPU resources as needed.

Description

Fal represents a serverless Python environment enabling effortless cloud scaling of your code without the need for infrastructure management. It allows developers to create real-time AI applications with incredibly fast inference times, typically around 120 milliseconds. Explore a variety of pre-built models that offer straightforward API endpoints, making it easy to launch your own AI-driven applications. You can also deploy custom model endpoints, allowing for precise control over factors such as idle timeout, maximum concurrency, and automatic scaling. Utilize widely-used models like Stable Diffusion and Background Removal through accessible APIs, all kept warm at no cost to you—meaning you won’t have to worry about the expense of cold starts. Engage in conversations about our product and contribute to the evolution of AI technology. The platform can automatically expand to utilize hundreds of GPUs and retract back to zero when not in use, ensuring you only pay for compute resources when your code is actively running. To get started with fal, simply import it into any Python project and wrap your existing functions with its convenient decorator, streamlining the development process for AI applications. This flexibility makes fal an excellent choice for both novice and experienced developers looking to harness the power of AI.