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Use data parallelism when a complete copy of your model and its training state fits on each GPU. If replicated training state exhausts memory, consider sharded data parallelism such as PyTorch FSDP. If an individual layer needs to span GPUs, consider tensor parallelism; if distributing a model’s depth is more useful, consider pipeline parallelism. These approaches can be combined, and none is universally fastest: memory use, model shape, batch and sequence lengths, GPU hardware, and the interconnect all matter.
What is the difference between data and model parallelism?
Distributed training uses multiple devices to divide training work. The central distinction is what gets divided:
- Data parallelism divides the input examples. In the standard replicated form, each GPU holds the model, processes a different portion of a batch, and synchronizes gradients so the replicas stay consistent.
- Model parallelism divides work within a model. Tensor parallelism splits individual layers across devices; pipeline parallelism assigns different portions of model depth to stages.
PyTorch’s DistributedDataParallel (DDP) documentation describes DDP as a synchronous distributed-training wrapper. In this setup, workers process separate data and participate in gradient synchronization. DDP is therefore a straightforward baseline when the full model and its training state fit on each GPU.
The terms can be confusing because sharded data-parallel methods also divide model state. PyTorch FullyShardedDataParallel (FSDP) shards parameters and other training state across data-parallel workers, gathering what is needed for computation. It is still data parallelism; it does not mean each layer’s mathematical operation has been split as in tensor parallelism.
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How the main strategies compare
| Strategy | What is divided | Why consider it | Communication and constraints |
|---|---|---|---|
| Replicated data parallelism (DDP) | Input batch; each worker keeps a model replica | Increase training throughput when the model and training state fit on each GPU | Workers synchronize gradients; each GPU must hold the full replica |
| Sharded data parallelism (FSDP) | Parameters and other model state across data-parallel workers | Reduce the per-GPU memory burden of replicated state | Requires communication to gather and distribute sharded state; it does not split layer computation like tensor parallelism |
| Tensor parallelism (TP) | Parts of individual layers | Distribute a layer or its computation across devices | Layer-level communication and partitioning requirements affect efficiency |
| Pipeline parallelism (PP) | Model depth, by assigning layers or sections to stages | Distribute a deep model across devices | Activations move between stages; pipeline utilization and stage balance matter |
There is no universal speed or cost crossover among these strategies. PyTorch and NVIDIA documentation describe their mechanisms and guidance, not a single result that applies across GPU architectures, interconnects, model shapes, and training settings.
How to choose a strategy
- Check whether a full training replica fits. Account for parameters, gradients, optimizer state, activations, and the intended batch size—not just the model’s parameter count. If the full training setup fits on each GPU and you want to distribute examples, start with replicated data parallelism.
- If replicated state is the memory limit, try sharding. FSDP can shard parameters, gradients, and optimizer state across data-parallel workers. Review the current FSDP documentation for the installed PyTorch release because API names and behavior can change.
- If one layer needs multiple GPUs, evaluate tensor parallelism. Layer dimensions and the cost of communication between devices affect whether a particular partition is practical.
- If splitting model depth is appropriate, evaluate pipeline parallelism. Assign portions of the model to stages, then profile stage balance and pipeline utilization; activation transfers are part of the cost.
- Consider other dimensions for specific workloads. NVIDIA’s Megatron Core guide describes context parallelism for long sequences and expert parallelism for mixture-of-experts models. These are framework-specific options, not universal requirements.
- Compose strategies when one dimension is insufficient. Data, tensor, pipeline, context, and expert parallelism can be combined. Treat the configuration as a workload-specific design to benchmark, not as a guaranteed recipe.
This sequence is a starting point, not a substitute for measurement. Compare memory use and throughput at the intended batch and sequence lengths on the actual GPU and interconnect configuration. Communication costs and workload shape can change the practical result.
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What communication and hardware change
Data-parallel communication
With replicated DDP, workers synchronize gradients. PyTorch recommends the NCCL backend for GPU-based communication in its distributed communication documentation. Synchronization and network costs can limit scaling, so adding GPUs does not guarantee proportional speedup.
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Model-parallel communication
Tensor-parallel devices exchange information as part of layer computation; pipeline stages transfer activations as work moves through model depth. These communications are tied to the model’s computation, so layer dimensions, stage placement, and the interconnect matter. A model-parallel configuration may solve a memory or compute constraint while introducing communication and utilization trade-offs.
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Memory and workload shape
The relevant memory limit is the complete training workload, not weights alone. Batch size, sequence length, activations, optimizer state, and model architecture all influence what fits. The best choice also depends on GPU memory and the bandwidth and topology connecting devices. The cited framework guides do not establish one universal threshold at which a particular parallelism strategy becomes faster.
Combining parallelism: an illustrative configuration
NVIDIA’s current Megatron Core Parallelism Strategies Guide gives an illustrative LLaMA-3 70B configuration across 64 GPUs: tensor parallelism 4, pipeline parallelism 4, context parallelism 2, and data parallelism 2. The dimensions multiply to 64. This is an example configuration from that guide, not a universal GPU requirement, independent benchmark, or prediction of performance for another system.
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When parallelism dimensions are composed, evaluate them together: the per-GPU memory footprint, communication within and between groups, and the resulting workload throughput all depend on the specific configuration and hardware.
PyTorch and Megatron Core implementation notes
PyTorch DDP and FSDP
PyTorch’s current stable DDP documentation covers synchronous distributed training, and its communication documentation describes backends including NCCL. PyTorch’s stable FSDP page documents the sharding wrapper. Consult the documentation matching your installed PyTorch version before adopting code or configuration; do not assume an API example written for another release is interchangeable.
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PyTorch’s FSDP introduction, published March 14, 2022 and updated November 15, 2024, explains the motivation for sharding parameters, gradients, and optimizer state, and discusses optional CPU offload: Introducing PyTorch Fully Sharded Data Parallel (FSDP) API. That article provides background; the stable API documentation is the more appropriate reference for current usage.
Megatron Core requirements are framework-specific
The NVIDIA Megatron Core installation page reviewed for this article lists NVIDIA Turing architecture or later as recommended hardware, FP8 support on Hopper, Ada, or Blackwell GPUs, Python 3.10 or later, and PyTorch 2.6.0 or later. These are requirements stated for Megatron Core on that page, not general requirements for distributed training or PyTorch DDP. Check the current Megatron Core installation guide when selecting a release, since requirements may change.
What distributed training does—and does not—promise
Parallelism distributes data, state, or computation; the cited documentation does not establish that it automatically improves model accuracy. Nor does the number of GPUs alone predict speed: synchronization, collectives, activation transfers, memory constraints, batch and sequence lengths, and hardware topology all influence outcomes. Benchmark the candidate configuration on the workload and environment you intend to use.
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