Central hospitals face mounting pressure — high patient volumes, demanding clinical workloads, and a growing need for faster, more accurate diagnoses. As the healthcare sector increasingly turns to AI to support clinical decision-making, the push for technological self-reliance has never been more urgent.
To meet this moment, E Hospital joined forces with VEM.AI to build a specialized AI platform purpose-built for clinical diagnosis and medical knowledge management. Deployed on FPT GPU Cloud powered by NVIDIA HGX H100, the platform slashed model training time from nearly 10 hours to just 30 minutes — dramatically accelerating iteration and deployment cycles.
At its core, the platform combines a 120-billion parameter model with multiple community healthcare models, fine-tuned on real clinician data to ensure both diagnostic accuracy and practical clinical relevance. Built for continuous 24/7 operation, it integrates seamlessly into hospital workflows without interruption.
The results speak for themselves: faster diagnoses, shorter patient wait times, and measurably improved clinical efficiency — marking a significant leap forward in AI self-reliance for central-level healthcare institutions.
Challenges
| Slow experimentation cycles
Long training time (~10 hours), limiting rapid model testing. |
| Limited domain-specific performance
Existing models did not fully meet specialized clinical needs across departments. |
| Cost optimization pressure
Previously relied on foreign cloud providers, but with performance trade-offs. |
| External operational risks
Dependence on foreign cloud led to suboptimal speed and potential service disruption with no local support. |
Solutions
| High-performance training on FPT GPU Cloud
Accelerated model training cycles on NVIDIA HGX H100 GPU Cloud, reducing iteration time significantly. |
| Large-scale & multi-model training
Train a 120B parameter model and combine it with multiple community healthcare models during training to improve accuracy. |
| Data-driven customization
Fine-tuned proprietary data to build an independent model. |
| Accelerated operations & support
Local infrastructure enabled faster processing with 24/7 direct-to-expert support. |
Benefits
| ~95% faster training cycles
Reduced training time from ~10 hours to ~30 minutes. |
| Higher model accuracy
Improved performance through multi-model training and real-world data. |
| 24/7 stable operation
Ensures continuous development and testing without interruption. |
