Accelerating Radiology AI Triage with Shared GPU Resources
A healthcare case study on improving imaging turnaround time while keeping GPU costs predictable.
Customer Profile
A hospital group processing over 1.2 million imaging studies annually. The AI triage system flagged urgent CT and X-ray cases to reduce clinician workload.
The Business Problem
- Unstable throughput during morning peaks.
- Model cold starts caused 2–3 minute delays for urgent cases.
- GPU spending volatility in quarterly budgeting.
Baseline:
| Metric | Baseline |
|---|---|
| Triage P95 latency | 2.5–3.2 min |
| GPU utilization | 24–30% |
| Urgent case turnaround | 45–55 min |
| GPU cost variance | ±25% / quarter |
TensorFusion Solution
- Warm-cache model shards to eliminate cold starts.
- GPU pooling across hospitals with strict data locality.
- Priority preemption for emergency scans.
- Chargeback by department to stabilize budgeting.
Results
| Metric | Before | After |
|---|---|---|
| Triage P95 latency | 3.0 min | 45 sec |
| GPU utilization | 27% | 66% |
| Urgent case turnaround | 50 min | 22 min |
| GPU cost variance | ±25% | ±8% |
“We cut urgent triage time in half and gained budget predictability. That mattered more than raw speed.” — Radiology Operations Lead
Why It Works in Healthcare
Healthcare workloads are time-critical and compliance-heavy. TensorFusion preserves data locality while maximizing compute efficiency.
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