Learning Roadmap
How to Become a AI Latency Optimization Engineer
A step-by-step, phase-based learning path from beginner to job-ready AI Latency Optimization Engineer. Estimated completion: 6 months across 3 phases.
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Foundations: ML Systems & Profiling
6 weeksGoals
- Understand the end-to-end lifecycle of an ML model from training to inference.
- Learn to use core profiling tools to identify bottlenecks (CPU, GPU, memory, I/O).
- Gain basic proficiency in PyTorch for inference scripting.
Resources
- NVIDIA Deep Learning Institute courses on Inference Optimization
- PyTorch official tutorials on TorchScript and profiling
- Book: 'High Performance Browser Networking' by Ilya Grigorik (for system thinking)
MilestoneYou can deploy a simple model via TorchServe or Triton, profile it with a load test, and identify the primary latency component (e.g., data loading, GPU kernel).
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Core Optimization Techniques
8 weeksGoals
- Master quantization techniques (PTQ, QAT) and their trade-offs.
- Understand model parallelism (tensor, pipeline) and its impact on latency.
- Learn the architecture and configuration of major inference servers.
Resources
- Documentation for TensorRT and TensorRT-LLM
- Research papers on quantization (e.g., GPTQ, AWQ)
- Open-source code of vLLM for studying PagedAttention
MilestoneYou can take a large model (e.g., LLaMA-7B), quantize it, and serve it with a 2x+ throughput improvement vs. the baseline on a single GPU.
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Advanced Systems & Hardware Co-design
10 weeksGoals
- Write custom CUDA kernels for specific attention or FFN layers.
- Design speculative decoding or other pipeline-parallel strategies.
- Perform full cost-performance optimization across a cluster.
Resources
- CUDA programming guides and NVIDIA's CUTLASS library
- Papers on speculative decoding (e.g., DeepMind's Medusa, Google's SpecInfer)
- Cloud provider whitepapers on AI accelerator instances
MilestoneYou can architect and justify a full serving solution for a 70B+ parameter model, including hardware selection, parallelism strategy, and caching, meeting a predefined SLA.
Practice Projects
Apply your skills with hands-on projects. Ordered by difficulty.
LLM Inference Optimization Challenge
AdvancedTake a 7B parameter model and serve it to achieve the highest possible tokens-per-second on a constrained budget (e.g., a single T4 GPU). Implement and compare techniques like quantization, batching strategies, and custom attention kernels.
Build a Latency-Monitored API Gateway for AI Models
IntermediateCreate a lightweight API gateway that routes requests to a model server, implements circuit breaking, collects detailed latency metrics (TTFT, TBT, total), and serves a real-time Grafana dashboard.
KV-Cache Optimization Simulation
AdvancedWrite a simulation program that models different KV-cache management strategies (FIFO, LRU, PagedAttention) for an LLM serving system. Analyze their impact on memory usage and throughput under various request patterns.
Speculative Decoding Proof-of-Concept
BeginnerImplement a simple version of speculative decoding using a small and large HuggingFace model (e.g., distilgpt2 and gpt2). Measure the acceptance rate and overall speedup for short text generation tasks.
Ready to Start Your Journey?
Prep for interviews alongside your learning — it reinforces every concept.