Distributed AI Workflows with Fuzzball and CIQ

This presentation by CIQ at AI Infrastructure Field Day 6 introduces FuzzBall as a solution for orchestrating distributed AI workflows. Brian Dawson from CIQ explains that modern compute stacks, especially for AI, require addressing multiple layers from hardware capitalization to reliable workload support, including orchestration, operation, and automation. FuzzBall sits in the orchestration layer, running on top of Kubernetes, to modernize traditional HPC organizations’ stacks and accelerate distributed workload deployment.

FuzzBall’s core capability is to let users run the same workflow on-premises, in public cloud, or on a GPU cloud without rewriting it, enabling federation across clusters and compute resources. Initially designed for batch HPC and stateless jobs, it has evolved to support both stateful and stateless AI workloads, addressing the challenge of unifying diverse fleets that may include HPC, general Kubernetes, and Gen AI demands. A key feature is the ability to describe a job, including compute, images, and storage, in a single workload file, making it portable. The solution also supports checkpointing, allowing for resilience and continuous tracking of jobs, and offers a workflow catalog for easy deployment of jobs via a “run” button.

Operationally, FuzzBall provides fault-tolerant scheduling, the ability to restart failed jobs, multi-tenancy, resource accounting, and support for both AMD and NVIDIA GPUs under a single control plane. It emphasizes operational hardening, including federation, auto-scaling, and managing Kubernetes certificates. CIQ aims to deliver a complete infrastructure stack that allows organizations to maximize Linux and GPU access, ensures built-in security, and provides an automated stack across diverse fleets, without demanding a full “AI factory” adoption. They encourage exploring their products, including Rocky Linux from CIQ Pro and CIQ Stratum Kubernetes Engine.