Anyscale

Founded 2019

Scale distributed model training and data workloads across multi-cloud GPU clusters

You can use Anyscale to build, train, and deploy large artificial intelligence models on distributed computing infrastructure. Built on the open-source Ray framework, it allows engineering teams to coordinate compute jobs across multiple cloud providers like AWS, Azure, and Google Cloud. You can run multimodal data preparation pipelines, compute batch vector embeddings across GPU clusters, and execute distributed model training with libraries like PyTorch and vLLM. It provides cluster management tools, dynamic hardware allocation, and centralized monitoring to run large-scale computing workloads efficiently without building custom infrastructure.

What Anyscale does

Launch distributed PyTorch training jobs across multiple GPUsGenerate batch vector embeddings from text files stored in cloud storageFilter and curate large video datasets based on model detection scoresPool GPU compute instances across different cloud providersDeploy post-training reinforcement learning loops for language modelsInspect GPU utilization and memory consumption during cluster workloadsConfigure access permissions and single sign-on for data teamsDistributed model training across multi-node GPU clustersMultimodal data curation pipelines for text, video, and audioBatch vector embedding generation running in parallelUnified GPU pooling across AWS, GCP, Azure, and CoreWeaveNative support for Ray ecosystem libraries like Ray Train and Ray DataHardware allocation for mixed CPU, GPU, and TPU setupsAccess management with enterprise single sign-on and audit logging