LLMs are massive systems — running them efficiently requires a mix of math, systems, and GPU-level design. This roadmap breaks down five pillars of optimization that every AI engineer should understand.
- Disaggregated Serving: split prefill and decode for specialized scaling
- Parallelisms: distribute model and compute across GPUs
- Optimizing Model Weights: compress with quantization, pruning, distillation, MoE
- Optimizing Attention: reduce O(N²) cost with FlashAttention and MQA
- Model Serving: accelerate runtime with batching, speculative decoding, and fused kernels