Distillation
Teacher-student model compression for 3-5x inference speedup while retaining accuracy.
Distillation compresses large teacher models into smaller, faster student models optimized for deployment. FORGE combines teacher-student distillation with quantization, pruning, and speculative decoding to achieve 3-5x inference speedups while maintaining accuracy above configurable thresholds.
Compress and optimize models for deployment-grade performance.
What's Included
Teacher-Student Distillation
Transfer knowledge from large teacher models to compact student architectures optimized for inference.
Quantization
INT8 and mixed-precision quantization for reduced memory footprint and faster computation.
Model Pruning
Structured and unstructured pruning to eliminate redundant parameters without accuracy loss.
Speculative Decoding
Use small draft models to accelerate inference from larger models with guaranteed output quality.
Inference Benchmarking
Comprehensive latency, throughput, and accuracy evaluation across target hardware platforms.
Specs & Parameters
Use Cases
Edge AI Deployment
Compress models for inference on tactical hardware with constrained compute and memory.
High-Volume Inference
Optimize throughput for production systems handling thousands of concurrent requests.
Latency-Sensitive Systems
Achieve sub-100ms inference for real-time decision support and control systems.
Ready for Distillation?
Typical engagement: 3-5 weeks. From scoping to deployment, FORGE handles the full pipeline.