AI Inference Optimization

We apply quantization (FP32 to FP16 or INT8) with calibration datasets to minimize accuracy loss. ONNX Runtime or TensorRT graph optimization fuses layers and eliminates redundant operations. Knowledge distillation creates smaller student models for latency-critical paths. Each technique trades off accuracy, latency, and memory — we benchmark rigorously before shipping.

TensorRT compiles models into GPU-optimized execution plans — typically 2-5x faster than framework inference. We build TensorRT engines for your specific GPU architecture (Ampere, Hopper) with optimized precision modes. For CPU inference, we configure ONNX Runtime with OpenVINO or Intel MKL-DNN backends. The compiled model gets version-tagged alongside the original.

Dynamic batching in Triton Inference Server groups incoming requests into GPU-efficient batches — improving throughput 3-8x with minimal latency increase. We configure batch window sizes, preferred batch sizes, and maximum queue lengths based on your latency SLOs. For sequence models, we set up continuous batching to handle variable-length inputs efficiently.

Every optimization gets benchmarked: latency distributions (P50/P95/P99), throughput at various concurrency levels, accuracy metrics against a held-out test set, and GPU memory usage. You get a comparison report showing before/after metrics so the tradeoffs are transparent. We don't ship optimizations that break model quality.