When people first hear multiple GPUs, it’s easy to think:
More GPUs = faster LLM.
But that’s not always the case.
The real question is:
Why do we need multiple GPUs in the first place?
There are mainly two problems:
📌 The model fits on one GPU, but we need to serve more users.
📌 The model is too large to fit on a single GPU.
And that leads us to three important techniques:
1️⃣ Data Parallelism → Copy the model
Run a complete copy of the same model on multiple GPUs and distribute incoming requests among them.
Goal: Higher throughput
2️⃣ Tensor Parallelism → Split the layer
Split computations inside a model layer across multiple GPUs.
Because the GPUs need to communicate frequently, high-speed GPU interconnects such as NVLink and NVSwitch become very important.
Goal: Run larger models and parallelize computation
3️⃣ Pipeline Parallelism → Split the layers
Different groups of model layers run on different GPUs.
For example:
GPU 0 → Layers 1–4
GPU 1 → Layers 5–8
GPU 2 → Layers 9–12
The output of one stage is passed to the next, almost like an assembly line.
Goal: Scale very large models across GPUs and nodes
The easiest way to remember all three:
Data Parallelism → Copy the model
Tensor Parallelism → Split the layer
Pipeline Parallelism → Split the layers
And for very large deployments, these techniques can also be combined using Hybrid Parallelism.
📌 Want to learn these concepts from an interview perspective?
I’m starting a 90-Day Intensive Program:
Cracking the GenAI Interview for DevOps, SRE, Platform & Forward-Deployed Engineers
We’ll connect traditional infrastructure engineering with the skills increasingly important for modern AI infrastructure roles:
✔️ Generative AI & LLM Engineering
✔️ GPU & AI Infrastructure
✔️ Kubernetes for AI workloads
✔️ Python & DSA
✔️ System Design
✔️ DevOps & SRE
✔️ Interview-style troubleshooting scenarios
The goal is not just to learn AI terminology, but to understand how these systems actually work and how to explain and troubleshoot them during interviews.
📌 Morning Batch: https://lnkd.in/gEGWguSE
📌 Evening Batch: https://lnkd.in/gdqXHS_g
📌 Self-paced Batch: https://lnkd.in/gVTSSZZH
🎥 YouTube video
English: https://lnkd.in/gmUnipkh
Hindi: https://lnkd.in/giCH6BNP
