Product
The complete model optimization platform
Quantize, prune, distill, and benchmark — all through a single API. No PhD required. Upload a model, get back a lighter one.
Capabilities
Six optimization engines, unified
Post-Training Quantization
Convert FP32 weights to INT8 or INT4 without retraining. Dynamic and static calibration supported. Preserves accuracy within 0.3-0.5% on standard benchmarks.
Structured Pruning
Remove entire neurons, channels, or attention heads — not just individual weights. The resulting model runs faster on standard hardware without sparse-matrix support.
Knowledge Distillation
Automatically train a smaller student model using your large model as teacher. Our pipeline handles data generation, training, and validation end-to-end.
Layer Fusion & Graph Optimization
Merge consecutive operations (Conv+BN+ReLU), eliminate redundant nodes, and optimize computation graphs for target hardware.
Energy & Latency Profiling
Measure real energy consumption (Joules per inference), latency percentiles, throughput, and memory footprint — before and after optimization.
Accuracy Validation
Automatic validation suite runs your test dataset against the optimized model, reports accuracy delta, and blocks deployment if regression exceeds your threshold.
Architecture
How the optimization pipeline works
When you submit a model, our engine analyzes its architecture, identifies optimization opportunities, and applies the best combination of techniques for your target deployment environment.
Real Results
Before & after optimization
ResNet-50 optimized for edge deployment (INT8 quantization + structured pruning)
Compatibility
Works with every major framework
PyTorch
Native .pt/.pth support
TensorFlow
SavedModel & Keras .h5
ONNX
Universal interchange format
JAX
Flax & Haiku checkpoints
Hugging Face
Transformers auto-detect
TensorRT
NVIDIA optimized export