What is Salad?
SaladCloud is the world's largest distributed GPU cloud network, offering access to over 60,000 daily active GPUs at prices as low as $0.02 per hour. By leveraging a decentralized network of individual GPU owners, SaladCloud provides AI/ML compute resources at up to 90% lower cost compared to traditional hyperscalers like AWS, Azure, or Google Cloud. This makes it an ideal solution for cost-sensitive workloads such as AI inference, batch processing, molecular dynamics, and rendering.
The platform exclusively uses NVIDIA RTX and GTX series GPUs, ensuring high-performance AI-enabled compute. SaladCloud's strict GPU selection policy guarantees that only capable hardware is onboarded. Security is a top priority: containers are encrypted in transit and at rest, and run in isolated environments on each node, ensuring data privacy and consistent execution regardless of the underlying machine.
Workloads are deployed via Docker containers, and SaladCloud's proprietary orchestration engine, SCE, simplifies container development and management. Users containerize their model and inference server, select the desired GPU types and quantity, and SaladCloud handles the rest—including scaling, scheduling, and ensuring uninterrupted GPU time. However, note that GPUs on SaladCloud behave like spot instances: they may have longer cold start times and can be interrupted. The maximum vRAM available is 24 GB, and workloads requiring extremely low latency are not suitable.
SaladCloud is perfect for businesses that need affordable, scalable GPU compute for production AI/ML models, especially those with flexible latency requirements. It is also an excellent choice for high-volume batch processing and research workloads. By tapping into a distributed network, SaladCloud democratizes access to GPU compute, reducing dependency on centralized cloud providers and their pricing models. Whether you are a startup, a research lab, or an enterprise, SaladCloud offers a cost-effective alternative without compromising on performance or security.
Use Cases
Key Features
decentralized GPU network 60 000+ daily GPUs low cost compute NVIDIA RTX/GTX GPUs encrypted containers isolated execution environments Docker container deployment proprietary orchestration engine automatic scalingWho is it for?
AI/ML engineers, data scientists, researchers, rendering studios, bioinformatics teams, startups, cost, sensitive enterprises
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