2026/01/23
MLOps Teams: Accelerating Training and Inference Pipelines with Elastic GPU Pools
A customer-story playbook for shrinking GPU queue time, separating training from inference, and shipping models faster.
“Our models were ready. The GPUs weren’t.”
An MLOps team told us this after a rough quarter. They had a clean pipeline: training jobs, evaluation, deployments, rollback hooks—the works. But their release cadence kept slipping for a frustrating reason:
the GPU queue was the bottleneck.
When retraining kicked off, inference slowed down. When inference traffic spiked, experiments stalled. The team didn’t need “more process.” They needed their compute to match how the pipeline actually behaves.
What was going wrong (and why it’s so common)
In mixed MLOps environments, three failure modes show up repeatedly:
- Shared GPU pools become accidental priority systems. Whoever submits first wins.
- Training and inference fight over the same headroom. One causes SLO pain; the other causes iteration delays.
- Burst traffic breaks reliability. The pipeline looks stable—until a product launch or an A/B test doubles demand.
The change that unlocked the pipeline
TensorFusion helped the team reorganize GPU capacity around pipeline stages, not around org charts.
