Train Neural Networks Across Multiple GPUs
As neural networks grow larger—often exceeding billions of parameters—it becomes increasingly difficult to train or run inference on a single GPU due to memory constraints. Fortunately, several frameworks and strategies now support automatic model parallelism and multi-GPU scaling, allowing large models to be split and processed efficiently across multiple devices.
In this blog post, we’ll explore key tools and techniques that enable this, especially in the PyTorch ecosystems.
Why Automatic Multi-GPU Handling Matters
Training or running inference on large models poses several challenges:
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Memory limitations: Some models simply cannot fit into a single GPU’s memory.
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Scalability: Even if they fit, training time can be drastically reduced by leveraging multiple GPUs.
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Ease of use: Manually splitting models across GPUs is error-prone and hard to maintain.
🚀 Key Frameworks and Tools
1. PyTorch FSDP (Fully Sharded Data Parallel) ⭐️
PyTorch's Fully Sharded Data Parallel (FSDP) allows sharding of model parameters, gradients, and optimizer states across GPUs. It enables training of models that do not fit into any single GPU, and supports both training and inference.
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
model = MyLargeModel()
model = FSDP(model)
✅ Best for large models without manual model splitting
✅ Supports mixed precision and checkpointing
✅ Requires PyTorch 1.11+
2. DeepSpeed by Microsoft
DeepSpeed is a powerful library designed for training extremely large models. Its core feature, ZeRO (Zero Redundancy Optimizer), comes in multiple stages:
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ZeRO Stage 1: Shards optimizer states
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Stage 2: Adds gradient sharding
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Stage 3: Shards model parameters as well
This makes it possible to scale models up to hundreds of billions of parameters, using only consumer-grade hardware with enough GPUs.
import deepspeed
model_engine, optimizer, _, _ = deepspeed.initialize(model=model, config="ds_config.json")
✅ Excellent performance for huge models
✅ Seamless Hugging Face integration
✅ Flexible, production-ready
3. Pipeline Parallelism (PyTorch)
Pipeline parallelism splits the model by layers across GPUs, where data flows through each GPU like an assembly line. PyTorch offers this through torch.distributed.pipeline.sync.
Best suited for sequential architectures like Transformers.
✅ Efficient for very deep networks
❌ More complex to implement and tune
🧩 Comparison Summary
| Framework/Tool | Type | Best For | Notes |
|---|---|---|---|
| FSDP (PyTorch) | Parameter Sharding | Large models, memory issues | Native in PyTorch |
| DeepSpeed | ZeRO Optimization | Massive-scale training | HuggingFace compatible |
| Pipeline Parallelism | Layer-wise Split | Transformer-style models | Needs tuning |
| Megatron-LM | Full stack scaling | Research, hyperscale models | High complexity |
| TensorFlow Strategies | Replication/Async | TF users, cloud setups | Less control over sharding |
