🚀 Multi-GPU Inference: explained simply 🚀

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.

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