خريطة استغلال وحدات معالجة الرسوميات (GPU)

GPUs are the most expensive line item in most AI budgets, and most teams use a fraction of what they pay for. This roadmap teaches you to find where the GPU sits idle, fix it, scale to multiple GPUs, then serve models efficiently and monitor them in production.

One resource per step, practical and highly rated, with one certificate-bearing course.

Prerequisite: Python and one model trained in PyTorch. If you're not there yet, start with the Basic MLOps Engineer Roadmap. When you finish, the Model Optimization and LLMOps specializations in the MLOps Roadmap for Seniors are the natural next step.

Phase 0 — Foundations (Month 1)

Before optimizing anything, understand how a GPU runs your code and why it sits idle.

PyTorch

Tensors, autograd, nn.Module, training loops, moving data and models to the GPU.

• PyTorch "Learn the Basics" tutorial

How GPUs execute deep learning

Compute-bound vs memory-bound vs overhead-bound. This mental model drives every optimization later.

• Making Deep Learning Go Brrrr From First Principles (Horace He)

Phase 0 project

Train a small model on a free GPU (Colab or Kaggle). Log GPU utilization and memory with nvidia-smi during training and write down what limits your throughput.

Phase 1 — Single-GPU Training Efficiency (Month 2)

Squeeze one GPU before buying more. This is where most of the easy wins are.

Profiling

Find the real bottleneck (data loading, kernel launches, memory) before changing code.

• PyTorch Profiler recipe

Mixed precision

FP16/BF16 training: faster math, half the activation memory.

• PyTorch Automatic Mixed Precision recipe

Data loading & memory tuning

DataLoader workers, pinned memory, batch size, torch.compile, gradient accumulation.

• PyTorch Performance Tuning Guide

Memory-efficient fine-tuning

LoRA and QLoRA: fine-tune large models on a single GPU by training small adapters over (quantized) frozen weights.

• Hugging Face PEFT docs

Phase 1 project

Take your Phase 0 model and profile it. Apply mixed precision and data-loading fixes, then fine-tune a 1B+ parameter model with QLoRA on one GPU. Report GPU utilization, throughput, and peak memory before vs after in your README.

Phase 2 — Multi-GPU & Distributed Training (Month 3-4)

When one GPU is not enough: data parallelism, sharding, and how large models are trained.

Distributed training with PyTorch (certificate)

Splitting data and model copies across devices with Accelerate and Trainer.

• Efficient AI Model Training with PyTorch (DataCamp)

Scaling to large models

Data, tensor, pipeline, and sequence parallelism; ZeRO/FSDP; communication overhead.

• The Ultra-Scale Playbook (Hugging Face)

Post-training at scale

SFT and preference optimization (DPO, GRPO) on multiple GPUs with DeepSpeed/FSDP-backed trainers.

• Hugging Face TRL docs

Going deeper: GPU kernels (bonus)

Writing and optimizing CUDA/Triton kernels, FlashAttention, and profiling real workloads.

• GPU MODE lectures (GitHub)

Phase 2 project

Fine-tune a small transformer with Accelerate and TRL on 2+ GPUs (Kaggle or a cloud instance). Measure scaling efficiency from 1 to N GPUs and explain where it falls short of linear.

Phase 3 — GPU Inference & Serving (Month 5)

Training is a batch job. Serving is latency, throughput, and cost per request.

LLM inference fundamentals

Prefill vs decode, TTFT and TPOT, KV cache, batching, latency vs throughput trade-offs.

• LLM Inference Handbook (BentoML)

Default serving engine

Continuous batching, PagedAttention, prefix caching, OpenAI-compatible API.

• vLLM docs

Alternative serving engine

Prefix reuse (RadixAttention) and structured generation. Learn where it beats your default.

• SGLang docs

NVIDIA-optimized serving

Maximum performance on NVIDIA hardware, at the cost of more complexity.

• TensorRT-LLM docs

Quantization

Fit bigger models on smaller GPUs and cut cost per token. Know what you quantized and what quality changed.

• Hugging Face Quantization overview

Phase 3 project

Build a KV cache calculator (layers, KV heads, head dim, dtype, batch, context in; memory and max concurrency out). Then serve an open-weights LLM with vLLM and check your estimate against real VRAM usage.

Phase 4 — Benchmarking & GPUs in Production (Month 6)

Measure under realistic load, then run GPU workloads reliably and watch what they cost.

LLM benchmarking

TTFT, TPOT, p95 latency, and throughput under realistic traffic. Never report tokens/sec without the workload definition.

• GuideLLM (GitHub)

GPU containers

Running GPU workloads in Docker.

• NVIDIA Container Toolkit docs

GPUs on Kubernetes

Scheduling, resource requests, and sharing GPUs across pods.

• Kubernetes: Schedule GPUs

GPU monitoring

Utilization, memory, temperature, and errors into Prometheus and Grafana.

• NVIDIA DCGM Exporter (GitHub)

Phase 4 project

Compare FP16 vs a quantized model on vLLM and SGLang, benchmarking both under load. Deploy your best setup on Kubernetes with a GPU request and build a Grafana dashboard showing GPU utilization and memory. Document engine choice and quantization quality in your README.

Honest Advice

• Measure before you optimize. Every change should come with a before/after number.
• Utilization is not efficiency. A GPU at 100% can still be wasting time. Track throughput and cost per sample or token.
• Don't buy GPUs to fix a data-loading problem. Profile first.
• Choose serving engines by constraints, not preference. Hardware, latency target, model support, and team skill decide.
• End every phase with a GitHub project and a README with your numbers. Your portfolio matters more than any certificate.
• Tools change, concepts last. Learn the memory and communication model, not just one library's API.