FSDP vs DeepSpeed ZeRO: Which Fits My Training Setup?
Start with FSDP when a PyTorch training stack needs distributed state sharding. Test DeepSpeed when you need its selectable ZeRO stages or CPU and NVMe offload features. Neither framework is a universal performance winner; compare the required configuration on your cluster.
Both can divide parameters, gradients, and optimizer state among GPUs. Their APIs, policies, and checkpoint handling differ.
Full sharding follows the same basic process
With full parameter sharding, each GPU stores a portion of the model. Before computing a layer group, the framework gathers the parameters that group needs. It then computes, releases gathered parameters according to its resharding policy, and combines and partitions gradients through reduce-scatter.
Grouping and prefetch decisions control temporary memory and communication overlap. Sharding every tiny operation separately can make communication dominate; gathering very large groups can create a temporary memory peak.
| Requirement | Candidate to test |
|---|---|
| PyTorch-native APIs and distributed tensor state | FSDP2 |
| Optimizer-state sharding while parameters stay replicated | DeepSpeed ZeRO-1 or ZeRO-2 |
| Full state sharding | FSDP or ZeRO-3 |
| NVMe state offload | DeepSpeed ZeRO-Infinity |
The FSDP2 tutorial uses fully_shard and DTensor parameters. It also exposes resharding policies: retaining parameters after forward trades memory for fewer later gathers. Construct the optimizer after applying the required sharding transformations.
Compare an entire training step
Keep the model, data, precision, sequence length, effective batch size, and GPU topology fixed. Measure peak allocated memory, step time, and communication. Verify a saved checkpoint can restore model state, optimizer state, and the intended training progress.
Hugging Face Accelerate can select FSDP or DeepSpeed through launch configurations for code prepared for those integrations. That does not imply any arbitrary training script can switch without adjustments, especially for checkpointing, offload, and custom optimizer handling.
The FSDP comparison in the LLM Engineering Guide connects these choices to training-state memory.