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What is Run:ai?

Run:ai is a platform for managing the complete lifecycle of AI projects, from initial development through production deployment. It's built specifically for teams that need to train and run large machine learning models whilst making better use of their GPU hardware. The platform sits between your ML tools and your infrastructure, handling job scheduling, resource allocation, and optimisation tasks that would otherwise fall to platform teams or DevOps engineers. The core value is in GPU optimisation. Most organisations with GPUs see utilisation rates well below 100% because different teams have different scheduling needs, some jobs require full GPUs whilst others can share, and resource allocation becomes a manual coordination problem. Run:ai uses techniques like GPU fractioning (splitting one GPU across multiple jobs), oversubscription (running more jobs than GPUs available), and intelligent scheduling to keep GPUs working. For teams spending six figures on GPU infrastructure, even a 20-30% improvement in utilisation translates to significant cost savings. It works with existing ML tools and frameworks (PyTorch, TensorFlow, Ray, Spark, etc.) and supports deployment across different environments: cloud providers, on-premises data centres, and hybrid setups.

Key Features

GPU scheduling and optimisation through fractioning, oversubscription, and bin-packing algorithms

Dynamic resource management with fair-share scheduling and automatic quota adjustment

Support for distributed data processing frameworks including Ray, Spark, Dask, and Rapids

Multi-environment deployment across cloud, on-premises, and hybrid infrastructures

Cluster monitoring and governance with policy enforcement and access control

Integration with popular ML frameworks and NVIDIA AI Enterprise tools

Pros & Cons

Advantages

  • Significantly improves GPU utilisation rates in multi-team environments
  • Works with existing ML workflows and popular frameworks without disruption
  • Handles complex scheduling problems that would otherwise require manual coordination
  • Supports hybrid and multi-cloud deployments for flexible infrastructure
  • Provides visibility and governance across GPU infrastructure

Limitations

  • Requires reasonable infrastructure knowledge to set up and maintain effectively
  • Steep learning curve for teams unfamiliar with resource management concepts
  • Enterprise pricing is expensive for small teams or individual researchers
  • Implementation and integration work needed before seeing tangible benefits
  • Adds another platform layer to monitor, maintain, and troubleshoot

Use Cases

Allocating GPUs fairly across multiple ML teams in an organisation

Training large language models and computer vision models at scale

Maximising utilisation of expensive GPU hardware across multiple environments

Running batch ML workloads alongside interactive development work

Managing resource contention between different ML projects and teams