Qure.ai Locks In Predictable GPU Costs and a Zero-Downtime Path to Production AI
Started from a month-long GPU benchmark to 10x infrastructure scale-up plan, Qure.ai is building its LLM training and diagnostic AI stack on AceCloud.
Downtime during UAT/Dev migration
POC to validated production readiness
Types benchmarked (L4, L40S, H200, RTX PRO 6000, A30)
Overview
Qure.ai needed a GPU cloud partner that could prove price-to-performance economics and platform stability before it would commit production healthcare workloads to the migration.
Moving our LLM training to AceCloud was one of the better infrastructure decisions we made. The stability has directly improved our delivery timelines.
Use Case
Builds AI software that helps radiologists and clinicians detect diseases from X-rays, CT scans, and other medical imaging. Impact on 45M+ lives across 105+ countries.
Runs GPU-intensive workloads for both diagnostic model inference and large language model training, demanding consistent performance across workload types.
Operates across multiple providers. UAT and Dev earlier on E2E, production currently on AWS.
Challenges
As a fast-growing health-tech company, Qure.ai expects its infrastructure usage to scale significantly in the coming period. But unpredictable cloud billing made it hard to plan that growth with confidence. Cost was one of the clearest blockers to committing further spend to any single provider.
Training LLMs and running diagnostic models for healthcare use cases demands consistent GPU performance across workload types, not a one-time benchmark, but performance that holds as workloads scale. And because these systems touch clinical workflows, any migration had to happen without service disruption.
Solutions
On-demand access to a broad GPU Fleet (L4, L40S, H200, RTX PRO 6000, and A30 GPUs) let Qure.ai benchmark real performance across its actual workload mix, not a single reference configuration.
UAT and Dev environments moved from E2E (or previous vendor) with zero downtime. De-risking the case for moving production workloads next.
A predictable infrastructure cost structure replaced the billing uncertainty that had been blocking Qure.ai’s scale-up plans.
A month-long proof of concept benchmarked seamless performance across multiple GPU types alongside platform stability. It gave Qure.ai the evidence it needed to move forward.
