Ray Scalability Envelope Note: This document is a WIP. This is not a scalability guarantee (yet). Distributed Benchmarks All distributed tests are run on 64 nodes with 64 cores/node. Maximum number of nodes is achieved by adding 4 core nodes. Dimension Quantity # nodes in cluster (with trivial task workload) 1000+ # actors in cluster (with trivial workload) 10k+ # simultaneously running tasks 10k+ # simultaneously running placement groups 1k+ Object Store Benchmarks Dimension Quantity 1 GiB object broadcast (# of nodes) 50+ Single Node Benchmarks. All single node benchmarks are run on a single m4.16xlarge. Dimension Quantity # of object arguments to a single task 10000+ # of objects returned from a single task 3000+ # of plasma objects in a single ray.get call 10000+ # of tasks queued on a single node 1,000,000+ Maximum ray.get numpy object size 100GiB+