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What is ML Infrastructure?

Machine learning models don’t live in notebooks.

To power real products, they need infrastructure: GPUs, networks, storage, schedulers, observability, deployment pipelines, and people who understand how all of this fits together.

This guide, Infra for AI – ML Infrastructure & GPU Networking, focuses on that layer.

It explains:

  • How GPUs, networks, and systems interact in training and inference
  • How design decisions in topology, routing, and RDMA affect throughput, latency, and cost
  • How an engineer with a networking / systems background can grow into an ML infrastructure role

Who this guide is for​

This guide is aimed at:

  • Network / systems engineers moving into ML infra / GPU networking
  • ML engineers who want to understand what actually happens below the framework
  • Infra / platform engineers building or operating GPU clusters for training and inference

If you already care about packets, queues, jitter, and failure modes, you’re in the right place.

What is ML infrastructure?​

Very roughly, ML infrastructure is everything that lets teams:

  1. Train models at scale
  2. Serve models reliably at low latency
  3. Observe, debug, and iterate on those systems

That includes:

  • Hardware: GPUs, CPUs, NICs, switches, NVMe, PCIe, NVLink, Infiniband/RoCE
  • Orchestration: Kubernetes, Slurm, custom schedulers, resource managers
  • Distributed compute: data / model / pipeline parallelism, collectives (AllReduce, AllGather…)
  • Networking: L2/L3, ECMP, QoS, RDMA, congestion control, routing design
  • Storage & data: datasets, caching, streaming, feature stores
  • Serving: online inference, batch inference, load balancing, rollout strategies
  • Observability: metrics, logs, traces, profiling, GPU / network telemetry

This guide emphasizes the intersection of ML and networking.

Structure of this guide​

The docs are organized into a few tracks:

  • Networking Basics for ML

    • Why networking matters for ML workloads
    • Latency, bandwidth, jitter, tail behavior
    • TCP vs RDMA at a high level

  • GPU Networking

    • PCIe, NVLink, NVSwitch
    • Infiniband vs RoCE
    • How NCCL uses the topology

  • ML Infrastructure

    • Distributed training communication patterns
    • AllReduce, AllGather, ReduceScatter, Broadcast
    • What traffic really looks like in a job

  • Observability

    • GPU metrics, network metrics, queue depths
    • Detecting bottlenecks
    • Debugging “training is slow” or “latency is spiky”

  • Career & Transition

    • Mapping networking skills to ML infra
    • What to learn next
    • Interview themes and how to talk about this work

How to read this guide​

You don’t have to read everything in order.

  • If you’re new to ML infra: start with Networking Basics for ML.
  • If you already know networking: jump straight to GPU Networking 101 and Distributed Training & Communication.
  • If you’re job-focused: read Transitioning from Networking to ML Infra and ML Infra Interview Themes.

Next: Why Networking Matters for ML.