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Learning Roadmap

How to Become a AI Inference Optimization Engineer

A step-by-step, phase-based learning path from beginner to job-ready AI Inference Optimization Engineer. Estimated completion: 8 months across 5 phases.

5 Phases
32 Weeks Total
High Entry Barrier
Advanced Difficulty
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  1. Foundations: Deep Learning & Systems Fundamentals

    6 weeks
    Goals
    • Understand transformer architecture internals and computational graphs
    • Learn GPU architecture fundamentals (SMs, memory hierarchy, warp scheduling)
    • Master Python profiling tools and basic benchmarking methodologies
    Resources
    • Fast.ai 'Practical Deep Learning' course
    • NVIDIA CUDA C++ Programming Guide (selected chapters)
    • Karpathy's 'Neural Networks: Zero to Hero' series
    • PyTorch documentation: Profiler and TorchScript
    Milestone

    You can profile a PyTorch model, identify the slowest layers, and explain GPU memory usage breakdown

  2. Inference Serving & Quantization

    6 weeks
    Goals
    • Deploy models with vLLM and Triton Inference Server
    • Apply INT8 and INT4 quantization using GPTQ and AWQ
    • Understand and configure batching strategies and KV-cache management
    Resources
    • vLLM documentation and source code
    • Hugging Face Optimum library tutorials
    • GPTQ and AWQ original papers
    • Triton Inference Server documentation and model analyzer
    Milestone

    You can quantize a 7B LLM, serve it with vLLM, and demonstrate 3x throughput improvement with <1% quality loss

  3. Advanced Optimization & CUDA

    8 weeks
    Goals
    • Learn TensorRT optimization pipeline and custom plugin development
    • Write basic CUDA kernels for attention and activation functions
    • Implement speculative decoding and continuous batching from scratch
    Resources
    • NVIDIA TensorRT Developer Guide
    • CUDA by Example (Sanders & Kandrot)
    • FlashAttention papers (Dao et al.)
    • vLLM source code for PagedAttention implementation
    Milestone

    You can build a custom TensorRT engine with fused operations and write a simple CUDA kernel that outperforms naive PyTorch

  4. Production Systems & Cost Optimization

    6 weeks
    Goals
    • Design multi-model inference architectures with autoscaling
    • Build comprehensive benchmarking and monitoring pipelines
    • Master inference cost modeling and hardware selection strategies
    Resources
    • AWS SageMaker inference documentation
    • NVIDIA Nsight Systems hands-on tutorials
    • Industry case studies from Anyscale, Databricks, and Mosaic ML blog posts
    • Cloud GPU pricing calculators and utilization analysis frameworks
    Milestone

    You can design and defend an inference architecture for a production LLM system serving 10K+ RPS with full cost and latency analysis

  5. Specialization & Industry Leadership

    6 weeks
    Goals
    • Specialize in a domain: large-scale LLM serving, edge deployment, or multi-modal inference
    • Contribute to open-source inference frameworks
    • Develop expertise in emerging hardware (TPUs, custom ASICs, neuromorphic chips)
    Resources
    • Research papers from MLSys, OSDI, and NeurIPS systems tracks
    • Open-source contributions to vLLM, TensorRT-LLM, or SGLang
    • GTC and inference-focused conference recordings
    • Edge deployment frameworks: ONNX Runtime Mobile, Core ML, TFLite
    Milestone

    You can architect inference systems across heterogeneous hardware, publish optimization case studies, and mentor junior engineers

Practice Projects

Apply your skills with hands-on projects. Ordered by difficulty.

LLM Quantization Benchmark Suite

Beginner

Build a benchmarking framework that evaluates the same LLM across FP16, INT8 (GPTQ), and INT4 (AWQ) precision levels, measuring latency, throughput, memory usage, and accuracy on standardized benchmarks like MMLU.

~25h
Model quantizationBenchmarking methodologyHuggingFace Transformers

vLLM vs. TensorRT-LLM Serving Comparison

Intermediate

Deploy the same model on both vLLM and TensorRT-LLM, build a load testing harness using Locust, and produce a detailed comparison report covering throughput, latency percentiles, GPU utilization, and configuration complexity.

~40h
Inference serving frameworksLoad testingPerformance analysis

Custom CUDA Kernel for Grouped Query Attention

Advanced

Implement a custom CUDA kernel for Grouped Query Attention (GQA) optimized for a specific GPU architecture, compare against PyTorch's built-in implementation, and integrate it as a TensorRT plugin.

~80h
CUDA programmingAttention mechanismsTensorRT plugins

Speculative Decoding Pipeline

Advanced

Implement a speculative decoding pipeline where a small 1B draft model proposes tokens verified by a 13B target model, measuring speedup across different acceptance rate configurations.

~50h
Speculative decodingAutoregressive generationPyTorch internals

Multi-Model GPU Sharing Server

Advanced

Build an inference server that hosts 3-5 different models on the same GPU cluster with dynamic model loading/unloading, request routing, and memory-aware scheduling to maximize GPU utilization.

~60h
GPU memory managementDynamic model loadingRequest routing

Inference Cost Optimization Dashboard

Intermediate

Build a real-time dashboard that tracks cost-per-thousand-tokens, GPU utilization, idle time, and requests-per-dollar across your inference fleet, with alerts when cost efficiency drops below thresholds.

~35h
Cost modelingMonitoring and observabilityPrometheus/Grafana

Edge LLM Deployment Pipeline

Intermediate

Optimize and deploy a 3B parameter LLM to a Jetson Orin or Apple Silicon device, achieving acceptable latency for interactive use cases, with automated quantization and compilation pipeline.

~45h
Edge deploymentModel compressionGGUF/llama.cpp

FlashAttention from Scratch

Advanced

Implement the FlashAttention algorithm in CUDA from the original paper, validate correctness against standard attention, and benchmark memory usage and speed improvements on different sequence lengths.

~70h
Algorithm implementationCUDA memory hierarchyAttention mechanisms

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