YellowDog × HelixML
A validated solution now available for running GenAI workloads on scarce GPU hardware across multiple regions: faster, cheaper, and without re-engineering existing pipelines.
GPU scarcity is slowing down GenAI
The demand for high-performance NVIDIA GPUs has fundamentally outpaced supply. For GenAI engineering teams, this level of scarcity is a direct barrier to development velocity, and the problem is compounded when cloud providers limit access to preferred hardware within specific regions. When demand within those regions makes the target hardware generally unavailable, GPU scarcity becomes an engineering obstacle that slows GenAI both in development and when operating at scale.
YellowDog solves this challenge by giving teams reliable access to a wider range of preferred hardware across multiple regions. Its solution orchestrates workloads intelligently across a global cloud footprint, removing the single-region constraint that turns scarcity into a bottleneck.
But provisioning the right compute is only part of the solution. GenAI workloads run on pipelines optimised for specific hardware, and ensuring they continue to run correctly across changing underlying compute requires its own layer of intelligence. YellowDog partnered with HelixML to provide compute continuity for GenAI workloads covering LLM inference, fine-tuning, and agentic engineering sessions. The result is something that did not previously exist: an intelligent compute layer that adapts to wherever capacity exists and maps existing pipelines to use it. This allows GenAI teams to escape the burden of continual infrastructure management. Access to preferred GPUs becomes a reliable and dynamic process, freeing the teams to focus on model development.
One orchestration layer. One pipeline. Any region. Any accelerator.
The partnership between YellowDog and HelixML gives GenAI engineering teams something that didn’t previously exist: the ability to run existing GenAI pipelines on any available hardware accelerator, across a multi-region global compute footprint, with Spot pricing built in from the start. The result is genuine hardware flexibility and meaningfully lower compute costs, without re-engineering a single pipeline to get there.
YellowDog removes the single-region constraint by searching across a global cloud footprint to find available capacity for the target hardware. Teams get access to preferred GPUs wherever they exist. If the target hardware is unavailable across all regions, YellowDog automatically provisions the next preferred accelerator family, with Helix ensuring the correct pipeline sessions run on it without interruption. This delivers real pipeline portability: teams are no longer tied to a single region or architecture. Hardware scarcity is no longer an engineering problem.
The diagram below shows how GenAI teams use YellowDog to access multi-region capacity. Teams build their pipelines once and the hardware adapts around them. If H100 capacity is unavailable, YellowDog’s intelligent provisioning automatically locates the next preference, NVIDIA A100, NVIDIA L4/A10G, or optionally cross-cloud.
Five steps to confidently run workloads on multi-region hardware
YellowDog’s five-step playbook allows Gen AI teams to identify and evaluate new hardware options. Each step produces a concrete deliverable, so engineering effort is only committed once confidence is production-grade.
Accelerate your GenAI development
YellowDog works with any GenAI engineering team to identify, validate, and onboard the right hardware across a global compute footprint. To find out how your team can get reliable access to preferred hardware and accelerate your GenAI workloads, get in touch with us.
