Build a Production-Grade LLM Inference Platform from Scratch: A Complete Hands-On Guide to vLLM-Inference-Lab

The vLLM-Inference-Lab, open-sourced by AWS Senior Engineering Manager Mohamed, is an LLM inference learning lab. It offers a complete 8-stage practical path—from local Ollama deployment to AWS cloud vLLM deployment, plus Prometheus/Grafana monitoring and auto-scaling—to help developers build a production-grade LLM inference platform from scratch.

With the rapid development of LLM technology, efficiently deploying and scaling inference services has become a core challenge for engineering teams. This project was initiated by Mohamed, a Senior Engineering Manager on AWS's Auto-Scaling team, to help developers build a complete production-grade LLM inference platform through hands-on practice. Mohamed’s career goal is to become a Cloud Inference Engineering Manager at Anthropic, and the project’s philosophy is "Build to understand, not to ship"—focusing on deepening technical principle understanding via building rather than just functional implementation.

The project adopts a progressive learning path, breaking complex infrastructure into manageable stages: Stage 1 starts with local Ollama to experience basic model services on Apple M4 chips; Stage 2 migrates to AWS cloud, deploying vLLM on g4dn.xlarge Spot instances (≈ $0.16/hour) and exploring continuous batching, FP8, and AWQ quantization. This approach lets learners gradually grasp the transition from local prototypes to production deployment, while quantitative experiments直观展示 how different compression strategies affect performance and resource usage.

The project’s core is an 8-stage EKS production platform plan: Stage 1 sets up the basic environment, using Karpenter instead of Cluster Autoscaler for flexible node scaling; Stage 2 builds an observability system integrating Prometheus, Grafana, and NVIDIA DCGM to monitor GPU utilization, memory usage, inference latency, etc.; Stage 3 uses KEDA for pod-level auto-scaling based on custom metrics and tests admission control; Stage4 compares scaling strategies (composite KV triggers, cold start optimization).

Stage5 introduces intelligent routing and inference optimization (cache-aware routing, prefix caching, speculative decoding); Stage6 handles multi-model services (model packaging, hierarchical fallback, CUDA checkpointing/restoration); Stage7 integrates cutting-edge tech: QLM predicts queue wait time via output length distribution to optimize scheduling, Mooncake’s SLO feasibility assessment and early rejection mechanism, Learning-to-Rank implements SJF-like scheduling and aging mechanisms to prevent starvation, and explores failed request retry strategies; Stage8 explores a decoupled inference architecture, separating pre-filling and decoding into independent instances for optimization.

The project emphasizes mapping LLM inference concepts to distributed systems/cloud computing concepts (e.g., KV cache ≈ warm instance pools, PagedAttention ≈ virtual memory paging, continuous batching ≈ city buses). Key metrics include TTFT, TBT, P99 latency, throughput, GPU utilization, and queue depth. Practical tips: Follow the "Research before building" principle (thorough research before each stage); code style requirements (comments explaining "why", small focused files); after completing a stage, self-explain first before seeking guidance.

vLLM-Inference-Lab is not just a technical project but a systematic learning framework. It breaks down LLM inference into manageable modules and helps developers build a complete understanding from theory to production via progressive practice. It is an invaluable open-source resource for engineers wanting to deeply understand LLM inference infrastructure.