To scale a model, first identify what is running out: compute throughput, per-GPU memory, or the ability to serve more requests. In PyTorch, use DistributedDataParallel (DDP) to train replicated models across GPUs when the model fits on each one; consider Fully Sharded Data Parallel (FSDP2) when model state does not fit on one GPU. Tensor and pipeline parallelism partition model computation or layers further. For inference, replicate a model to handle separate batches, or split a single model across GPUs when it needs to span devices.
What does distributed machine learning divide?
A distributed job runs work across multiple processes, devices, or machines. The important distinction is what gets divided:
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- Data parallelism: workers process different portions of the data while running copies of the model. This can increase training throughput or let inference workers handle separate batches.
- Model parallelism: the model itself is partitioned across devices. Tensor parallelism divides model computation; pipeline parallelism assigns different model layers to different devices.
Adding devices does not by itself guarantee faster training or inference. The choice changes memory use, communication, and coordination, and the result depends on the workload and hardware.
Which parallelism approach should you start with?
| Situation | Starting point | Main tradeoff to assess |
|---|---|---|
| The model fits on one GPU, and you want more training throughput | DDP | Each worker keeps a model replica, so weigh additional data throughput against duplicated model state and gradient communication. PyTorch Distributed Overview |
| Model state does not fit on one GPU | FSDP2 | Sharding reduces the model state held by each worker, while adding communication and configuration considerations. PyTorch Distributed Overview; FSDP documentation |
| FSDP2 has reached a scaling limit, or the model needs finer partitioning | Tensor and/or pipeline parallelism | Choose how to divide computation or layers, and account for communication topology and operational complexity. PyTorch Distributed Overview |
| Inference requests or batches can be handled independently | Data-parallel inference | Each process runs a model copy, so compare replicated model memory with the benefit of serving separate batch shards. Torch-TensorRT distributed inference |
| A single inference model needs to span GPUs | Tensor-parallel inference | Model shards must coordinate and exchange data across GPUs. Torch-TensorRT distributed inference |
How does DDP train across GPUs?
DDP starts from replicated models: each rank, or distributed worker, has a copy of the model and processes its portion of the data. During training, workers synchronize gradients with an all-reduce operation so their model updates stay aligned. This makes DDP a straightforward starting point when the model fits on each GPU and the goal is to process more training data in parallel.
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The memory cost is central to the choice: every worker holds model state rather than dividing it among workers. DDP is therefore not the answer to a model that cannot fit on one GPU. PyTorch’s comparison of DDP and FSDP explains this replicated-versus-sharded distinction in its FSDP2 getting-started tutorial.
When is FSDP2 a better fit?
FSDP shards model state across workers. In the full-shard pattern, parameters are gathered for forward and backward computation, while gradients and optimizer state are sharded; gradients are reduce-scattered and optimizer updates operate on local shards. This lowers the model-state requirement per device compared with keeping a complete copy on every worker.
The tradeoff is coordination and communication: parameters must be gathered when needed, and the configuration and limitations depend on the workload. FSDP2 is a memory strategy, not a universal drop-in speedup. Check the relevant options and constraints in the PyTorch FSDP documentation and FSDP2 tutorial.
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When should you consider tensor or pipeline parallelism?
Consider tensor parallelism (TP) or pipeline parallelism (PP) when sharding alone is not a sufficient scaling approach or the model needs to be partitioned more finely. TP divides model computation across devices; PP divides model layers among devices. These approaches change where model work happens, rather than simply assigning separate data batches to complete model replicas.
The partition has to suit the model and the connections between devices. PyTorch’s distributed overview presents TP and PP as approaches to consider when FSDP2 reaches scaling limits; it does not establish a universally best partition or performance gain for every setup. See the PyTorch Distributed Overview for the framework-level guidance.
How is distributed inference different from distributed training?
For inference, the key question is whether separate requests can be handled by separate model copies, or whether one model must be divided across GPUs. Data-parallel inference runs replicated models on separate batch shards. Tensor-parallel inference splits a model across GPUs, with processes coordinating the partitioned computation.
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Compilation and distribution are separate responsibilities. Torch-TensorRT’s distributed inference material describes compiling models for these patterns, but leaves process coordination and data movement to the distributed framework. Its distributed inference examples include multi-GPU and two-node cases; the deployment guide explains the distinction between data and tensor parallelism.
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Which communication backend fits the hardware?
Backend choice depends on the devices. PyTorch’s practical rule of thumb is NCCL for CUDA GPU distributed training and Gloo for CPU distributed training. Hardware and network setup—including GPU hosts connected through InfiniBand or Ethernet—affect how communication behaves, so treat the rule as a starting point rather than an absolute ranking for every cluster.
Consult the PyTorch distributed communication documentation for backend guidance and hardware- and network-related details relevant to the job.
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What changes when the job moves to a cluster?
A cluster adds machines and a network to the devices and processes already involved in a distributed job. The parallelism pattern describes how computation is divided; orchestration manages jobs and resources. Kubernetes is one orchestration route, not a requirement for all distributed training.
PyTorch’s article on Kubeflow Trainer and PyTorch on Kubernetes describes support for DDP, FSDP/FSDP2, and tensor parallelism. That is an implementation option for teams using Kubernetes, not evidence that every team needs Kubernetes or that it is the best choice for every workload.
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Start with the problem you need to solve, then compare configurations on your own workload and hardware. The cited PyTorch material describes architecture and framework guidance; it does not provide a matched benchmark across DDP, FSDP2, tensor parallelism, and pipeline parallelism that establishes a general performance winner.
Quick Recap
- If the model fits on each GPU, assess DDP against the cost of replicated state and gradient synchronization.
- If model state is the constraint, assess FSDP2’s per-device memory reduction against communication and configuration demands.
- If further partitioning is needed, evaluate TP or PP with attention to model structure and device communication topology.
- For inference, decide whether independent request batches or a single model split across devices is the actual requirement.
- For CPU-only distributed work, begin with Gloo; for CUDA GPU work, begin with NCCL, then account for the host and network configuration.
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