Model Optimization
Bridging the Gap: Transforming Research Models into Production Engines.
Speed is a Feature, not an Afterthought
A researcher is satisfied with 99% accuracy at 10 seconds per image. In production, latency over 100ms means failure. Optimization transforms massive, slow neural networks into sleek, fast engines that run on smartphones or cloud servers for 1/10th of the operational cost.
1. The Optimization "Trinity"
Quantization
Converting 32-bit Floating Point (FP32) math to 8-bit Integer (INT8).
Result: 4x smaller model size, 2x-4x speed increase with <1% accuracy loss.
Pruning
Removing redundant connections (near-zero weights). We implement Structured Pruning to delete entire channels.
Result: Significantly fewer calculations and optimized GPU memory usage.
Knowledge Distillation
Training a tiny Student model (DistilBERT) to mimic the logic of a massive Teacher.
Result: 40% smaller model retaining 97% of original capabilities.
2. Domain-Specific Execution
Computer Vision (CV)
- Layer Fusion: Merging Conv-BatchNorm-ReLU into a single "Super-Kernel" via TensorRT.
- Resolution Scaling: Dynamic input resizing to the smallest accurate resolution.
- Backbone Swapping: Migrating to EfficientNetLite for mobile deployment.
NLP & Generative AI
- KV Caching: Saving the math of conversation history for 10x faster token generation.
- Flash Attention: Hardware-aware algorithms to optimize GPU memory reads.
- Dynamic Batching: Efficient sentence grouping to eliminate GPU idle time.
3. Specialized Runtime Engines
| Runtime Engine | Optimization Target | Target Hardware |
|---|---|---|
| NVIDIA TensorRT | Maximum Throughput | NVIDIA GPUs / Cloud Clusters |
| ONNX Runtime | Cross-Platform Compatibility | Universal (CPU, GPU, Mac) |
| OpenVINO | Edge Efficiency | Intel Silicon (Laptops/IoT) |
| TFLite | Ultra-Low Power | Android & iOS Hardware |
Production-Ready AI
Download our "Model Optimization Whitepaper" for clinical production deployments.
Download Optimization Guide (.docx)