Generative AI for Business: 10x Performance Improvement

Optimizing AI models is a balancing act between accuracy, latency, and cost. Start by profiling bottlenecks—layer-level timings, memory usage, and I/O overhead—to decide whether to optimize architecture, runtime, or hardware. Small architectural tweaks like replacing expensive operations or reducing sequence lengths can yield immediate wins.

Quantization and pruning compress models without drastic accuracy loss. Post-training quantization is low effort; quantization-aware training delivers higher fidelity. Structured pruning removes redundant channels and pairs well with modern runtimes like ONNX Runtime or TensorRT to unlock hardware acceleration.

Knowledge distillation creates a smaller student model guided by a larger teacher, preserving performance while slashing inference time. Combine distillation with caching and dynamic batching to squeeze maximum throughput from GPUs and CPUs alike. Measure with realistic workloads to avoid surprises in production.

Operational excellence finishes the job: autoscale on real demand signals, pin critical services to reserved capacity, and set SLOs on p95/p99 latency. Observability around cold starts, cache hit rates, and kernel launch times ensures optimizations translate to user-perceived speed.