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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallUse data parallelism when a full training replica fits on each GPU; use model parallelism when parts of the model itself must be spread across devices. If replicated training state—not the model’s individual layer computation—is what exceeds memory, sharded data parallelism such as PyTorch FSDP may be the better next step. These approaches can also be combined.
What data parallelism and model parallelism divide
Both distribute neural-network training across devices, but they partition different things. Data parallelism divides examples in a batch among workers. Model parallelism divides the model’s computation or depth among devices.
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Data parallelism: different examples, replicated model
In standard replicated data parallelism, each GPU holds a full copy of the model and processes a different portion of the batch. Each worker computes gradients from its local examples, then synchronizes gradients or updates so the replicas stay consistent. PyTorch’s DistributedDataParallel (DDP) is a synchronous implementation; its documentation describes one process per GPU patterns and recommends NCCL for GPU communication.
This setup is a straightforward baseline when the model and its training state fit on every GPU and the goal is to process more data in parallel. Its trade-off is that each GPU stores a replica, while gradient synchronization adds communication.
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Model parallelism: parts of a model across devices
Model parallelism divides work associated with a single model. Two common forms split the model in different ways:
- Tensor parallelism (TP) splits individual layers or their tensor operations across devices. Devices must communicate as those layer computations proceed.
- Pipeline parallelism (PP) assigns different portions of model depth to sequential stages on different devices. Activations pass between stages; how effectively the GPUs stay busy depends on pipeline scheduling and workload shape.
These methods can help when a layer or model computation cannot fit or run effectively on one device. They also introduce within-model communication and partitioning complexity, so they are not automatically faster than replicated data parallelism.
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Where FSDP fits
Fully Sharded Data Parallel (FSDP) is still data parallel training, not tensor parallelism. Rather than keep parameters, gradients, and optimizer state replicated on every data-parallel worker, FSDP shards model state across workers and gathers what is needed for computation. This can reduce per-GPU memory pressure while retaining the data-parallel approach.
PyTorch’s current stable FSDP documentation describes the wrapper and its options. The project’s FSDP announcement, published March 14, 2022 and updated November 15, 2024, explains the aim of sharding parameters, gradients, and optimizer states and notes optional CPU offload. Check the documentation for the PyTorch release you have installed before adopting API names or configuration details.
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Compare the practical trade-offs
| Approach | What it partitions | Typical reason to use it | Main trade-off |
|---|---|---|---|
| Replicated data parallelism (DDP) | Input batch across workers; each keeps a full model replica | Increase data throughput when the full training replica fits on each GPU | Full-replica memory use and gradient-synchronization cost |
| Sharded data parallelism (FSDP) | Input work across data-parallel workers and model state across those workers | Reduce replicated parameter, gradient, and optimizer-state memory | Collectives and state gathering add communication; behavior depends on configuration |
| Tensor parallelism | Operations within individual layers | Spread a layer or its computation across devices | Layer-level communication and model partitioning requirements |
| Pipeline parallelism | Model depth across stages | Partition a deep model into sequential stages | Activation transfers and the need to keep pipeline stages usefully occupied |
There is no universal point at which one strategy becomes faster or cheaper. Memory use, batch size, model and layer shape, sequence length, GPU architecture, interconnect, and communication overhead all affect the result. PyTorch and NVIDIA documentation explain mechanisms and guidance, not a hardware-independent speed or cost crossover.
Choose a strategy in this order
- Check whether a full training replica fits. Include parameters, gradients, optimizer state, and the activations needed for the intended batch and sequence length. If it fits and you want more data throughput, start with DDP.
- If replicated state is the memory bottleneck, try sharding. FSDP or another sharded data-parallel method can reduce per-GPU state by distributing it across workers. This addresses replicated-state memory; it does not split an individual layer’s mathematical operation in the way TP does.
- If a layer itself needs multiple devices, consider TP. Evaluate the layer dimensions and communication over the actual GPU interconnect; tensor partitioning adds communication during the computation.
- If model depth is the useful partition, consider PP. Divide layers into stages and evaluate activation transfers and pipeline utilization for the workload.
- Add other dimensions only when the workload calls for them. NVIDIA’s Megatron Core guide discusses context parallelism for long sequences and expert parallelism for mixture-of-experts models, alongside data, tensor, and pipeline parallelism. These are framework-oriented recommendations, not universal performance guarantees.
- Benchmark the complete configuration. Measure memory and throughput with the intended model, sequence length, batch, GPU count, software versions, and interconnect. Do not assume that adding GPUs produces linear speedup.
Combining parallelism dimensions
Data, tensor, pipeline, context, and expert parallelism are composable rather than mutually exclusive. NVIDIA’s Megatron Core Parallelism Strategies Guide recommends beginning with data parallelism and adding dimensions as model size, depth, sequence length, or model type requires them. In the guide’s illustrative LLaMA-3 70B configuration, 64 GPUs are arranged as TP=4, PP=4, CP=2, and DP=2; the product of those dimensions is 64. This is a configuration example from NVIDIA’s guide, not a requirement for every 70B run or a promise of particular performance.
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Framework and hardware qualifications
Implementation details vary by framework and release. For PyTorch GPU DDP, the current stable documentation identifies NCCL as the recommended high-performance backend; see the torch.distributed communication documentation for communication APIs and backends. Confirm current FSDP API behavior in the documentation matching your installed PyTorch version.
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For PyTorch’s tutorial entry point, see Distributed training tutorials.
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