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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 Q4 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.
Runs GPU-intensive workloads for both diagnostic model inference and LLM training.
Operates across multiple providers. UAT/ Dev earlier on E2E, production on AWS.
Challenges
Unpredictable cloud billing made it hard to plan the growth that Qure.ai was expecting. Cost stayed high, making it harder to commit to a single provider.
They needed consistent GPU performance, not a one-time benchmark. 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).
UAT and Dev environments moved from E2E with zero downtime.
A predictable infrastructure cost structure replaced the billing uncertainty.
A month-long proof of concept benchmarked seamless performance across multiple GPU types alongside platform stability.
