From Zero to Production: A Complete Learning Roadmap for Large Model Inference Engineering

This roadmap is for machine learning engineers, providing a complete practical learning path from neural network fundamentals to production-grade LLM services. It corely covers Transformer architecture, KV caching, quantization techniques, fine-tuning methods (LoRA/QLoRA), and inference optimization strategies (vLLM/SGLang, etc.). Through a project-driven approach, it helps developers master core inference engineering skills, suitable for those who want to switch to inference optimization or prepare for related job interviews.

This roadmap is maintained by ShaoZhi21 and originates from the GitHub repository inference-engineering (released on June 10, 2026). The design philosophy focuses on practicality (each project can be directly applied to work), progressive complexity (from basics to production grade), resource flexibility (supports platforms like Colab/RunPod), and optional content (choose based on needs). It aims to help working engineers systematically build inference engineering capabilities without affecting their full-time jobs.

The roadmap is divided into 4 core learning weeks plus Week 0 (basics):

• Week 0: PyTorch fundamentals (MNIST classifier project, optional quantization experiments/micrograd implementation);
• Week 1: Build GPT from scratch and KV caching (understand Transformer architecture, implement KV caching and compare performance);
• Week 2: Production-grade inference optimization (vLLM/SGLang deployment and benchmarking, test optimization levers like batching and quantization);
• Week 3: Fine-tuning and multi-LoRA services (LoRA/QLoRA fine-tuning, DPO optimization, multi-LoRA service deployment and evaluation).

The roadmap focuses on four key technologies:

1. KV Caching: Avoids redundant computation of attention key-value pairs, reducing the complexity of autoregressive generation from O(n³) to O(n²);
2. Quantization Techniques: FP16→INT8→INT4-AWQ, balancing memory usage, computation cost, and model accuracy;
3. Continuous Batching and PagedAttention: vLLM's PagedAttention improves GPU memory utilization, and combined with continuous batching increases throughput;
4. Multi-LoRA Services: Share the base model and dynamically load adapters to achieve large-scale personalized services.

Each phase's project is job-relevant:

• MNIST classifier: Builds PyTorch basic muscle memory;
• nanoGPT+KV caching: Master core inference optimization technologies;
• vLLM/SGLang benchmarking: Produces reports that are persuasive in interviews;
• Fine-tuning-service-evaluation loop: Simulates real work processes and demonstrates end-to-end capabilities.

For effective learning, it is recommended:

1. Start from Week 0 and do not skip basic projects;
2. Focus on Week 2 (production-grade optimization is most job-relevant);
3. Complete all projects to build a showcaseable engineering portfolio;
4. Participate in communities like vLLM/SGLang for support;
5. Record the learning process (blog/GitHub), track experiment results and insights.