Inference Engines
An inference engine is the computational system responsible for executing large language models (LLMs) after they have been trained. Unlike training, which involves adjusting model weights, inference focuses on taking user input and producing output efficiently. The core challenge in LLM inference is managing the computational and memory demands of processing tokens sequentially, where each token generation requires forward passes through the entire model.
Memory Management and Optimization
Modern inference engines employ memory mapping techniques to handle the large parameter sets of contemporary LLMs, which often contain billions of weights. Key optimization strategies include key-value (KV) caching, which stores intermediate computations to avoid redundant calculations during token generation, and quantization, which reduces the precision of model weights to decrease memory footprint and increase computational speed. Techniques like continuous batching allow multiple inference requests to be processed simultaneously, improving overall throughput.
Performance Considerations
Inference engines must balance latency and throughput depending on deployment requirements. Latency—the time to generate the first token or complete response—matters for interactive applications, while throughput—the number of tokens generated per unit time—is crucial for high-volume scenarios. Specialized inference frameworks like vLLM, TensorRT-LLM, and llama.cpp implement these optimizations at varying levels, with different trade-offs between memory efficiency, speed, and ease of deployment across CPUs, GPUs, and specialized hardware accelerators.
Source Notes
- 2026-04-22: LLM Inference: Engines, Memory Mapping, and Performance Optimization · ▶ source