efficient inference
**Efficient Inference and Model Serving** — Efficient inference transforms trained deep learning models into production-ready systems that deliver low-latency predictions at scale while minimizing computational costs and energy consumption.
**Quantization for Inference** — Post-training quantization converts 32-bit floating-point weights and activations to lower precision formats like INT8, INT4, or even binary representations. GPTQ and AWQ provide weight-only quantization methods that maintain quality with 3-4 bit weights for large language models. Activation-aware quantization calibrates scaling factors using representative data to minimize quantization error. Mixed-precision strategies apply different bit widths to different layers based on sensitivity analysis.
**KV-Cache Optimization** — Autoregressive generation requires storing key-value pairs from all previous tokens, creating memory bottlenecks for long sequences. PagedAttention, implemented in vLLM, manages KV-cache memory like virtual memory pages, eliminating fragmentation and enabling efficient batch processing. Multi-query attention and grouped-query attention reduce KV-cache size by sharing key-value heads across attention heads. Sliding window attention limits cache to recent tokens for streaming applications.
**Batching and Scheduling** — Continuous batching dynamically adds and removes requests from processing batches as they complete, maximizing GPU utilization compared to static batching. Speculative decoding uses a small draft model to propose multiple tokens that the large model verifies in parallel, achieving 2-3x speedups for autoregressive generation. Iteration-level scheduling optimizes the interleaving of prefill and decode phases across concurrent requests.
**Serving Infrastructure** — Model serving frameworks like TensorRT, ONNX Runtime, and Triton Inference Server optimize computation graphs through operator fusion, memory planning, and hardware-specific kernel selection. Model parallelism distributes large models across multiple GPUs using tensor and pipeline parallelism. Edge deployment requires additional optimizations including model distillation, pruning, and architecture-specific compilation for mobile and embedded processors.
**Efficient inference engineering has become as critical as model training itself, determining whether breakthrough research models can deliver real-world value at costs and latencies that make practical applications economically viable.**