SaladCloud customers save up to 80% on AI model training. Experience affordable orchestration and earn more inferences per dollar on Salad Container Engine, now with integrated GPU support available on every container instance.
Developing cutting-edge AI models often requires expensive GPU processing. Training runs can cost thousands, leaving little room to iterate and optimize. Distributed data parallel (DDP) architectures can accelerate the training process, but networking GPUs or sourcing cloud resources can be costly.
Salad Container Engine (SCE) leverages latent resources from privately-owned GPUs to reduce modeling costs by up to 80%. Our fully managed container platform features dedicated edge processing, globally geodistributed nodes, and integrations for most standard Kubernetes workflows.
Just ask our customers. The company shown at the right trains specialized NLP models with PyTorch on public web data to perform various tasks, such as predicting price fluctuation on ecommerce platforms.
Before deploying on Salad, their team trained models on one AWS p3.8xlarge EC2 instance with four V-100 GPU accelerators at an on-demand rate of $12.24/hr. Each training run took roughly 24 hours to complete, bringing the daily cost of compute to $293.76. The team averaged 12 runs and around $3,525 per month.
The team was eager to improve performance by testing different model architectures in successive iterations, and to try creating additional models for experimental applications. Unfortunately, the costs associated with their AWS configuration prevented them from increasing output or experimenting.
By switching to Salad Container Engine (SCE), they quickly realized an 80% reduction in overall training cost during runs of the same duration. These savings afforded new opportunities to experiment. In subsequent trials, their team conducted four times as many runs, increased net model performance, and validated new specialized modeling products.