AI System Performance Optimization

Design rigorous AI model benchmarks and evaluation frameworks to measure performance, track regressions, and guide optimization decisions.

Systematically reduce AI API and inference costs through model selection, caching strategies, prompt compression, and intelligent routing.

Maximize AI workload performance on GPUs, TPUs, and specialized accelerators through hardware-aware tuning, kernel selection, and memory optimization.

Identify AI model performance bottlenecks using GPU profiling, memory tracing, and operator-level analysis to guide targeted optimizations.

Expert guidance on model quantization techniques — INT8, INT4, GPTQ, AWQ, GGUF — to compress AI models without sacrificing accuracy.

Design high-throughput AI serving systems that scale under load — covering load balancing, replica management, and concurrency optimization.

Expert in KV cache tuning for transformer models — maximize memory efficiency, reduce recomputation overhead, and improve serving throughput.

Reduce LLM inference latency with expert strategies for batching, quantization, caching, and deployment architecture tuning.

Optimize AI prompts to reduce token consumption, cut API costs, and improve response quality without changing model or infrastructure.

Implement and tune speculative decoding for LLM inference — select draft models, configure acceptance rates, and achieve significant latency gains.