Building an LLM Inference Engine from Scratch: A Complete Guide for Practitioners

This article explores the complete process of building an LLM inference engine from scratch, covering architecture design, core component implementation, performance optimization strategies, and deployment challenges. Building an inference engine by hand helps master the core principles of Transformers and enables deep optimization for specific scenarios. This article will systematically introduce key points from architecture to deployment, providing a guide for practitioners.

With the rapid development of LLMs, developers are starting to focus on the underlying implementation of inference. Although there are mature frameworks like vLLM and TensorRT-LLM, building by hand allows for an in-depth understanding of Transformer details and enables optimization for specific scenarios. This article aims to provide a complete path for building an inference engine.

The core modules of an inference engine include a model loader, tokenizer, inference core, decoding strategy, and KV cache manager. Key memory management strategies: weight quantization (FP16→INT8/INT4, e.g., GPTQ, AWQ), PagedAttention, and continuous batching to address memory-sensitive issues.

Transformer layer optimization: Self-attention uses FlashAttention (IO-aware to improve speed), sliding window (O(n×w) complexity), and sparse patterns; FFN uses GLU variants (SwiGLU) or MoE. Decoding strategies: Greedy (simple but monotonous), beam search (high accuracy), sampling (randomness control), and contrastive decoding (quality improvement).

Performance optimization: Operator fusion (Layernorm+Linear, Attention fusion), custom CUDA kernels (CUTLASS/Triton); multi-GPU parallelism (tensor, pipeline, sequence parallelism); speculative decoding (small model generates candidates, large model verifies, 2-3x speedup, e.g., Medusa/EAGLE).

Deployment considerations: Service-oriented architecture (request scheduling, dynamic batching, streaming output, auto-scaling); quantization deployment (accuracy evaluation, calibration dataset selection, mixed-precision strategy).

Cutting-edge trends: Hardware co-design (TPU/Trainium to optimize memory bandwidth), inference-training integration (online/continuous learning), and multimodal inference (support for image/audio/video inputs).

Building an LLM inference engine requires knowledge of algorithms, software engineering, and hardware. It is recommended to start with a simplified version, gradually add optimizations, and pay attention to open-source projects like vLLM/SGLang. There is significant room for optimization in inference engines, and more exploration directions lie ahead.