Distributed Scaling

Distributed training scaling uses DDP and FSDP wrapping, gradient accumulation, and multi-node launch tooling to optimize execution across multiple GPUs and nodes. Presets for 1B/3B/7B models simplify configuration.

DDP and FSDP wrapping strategies manage memory differently during distributed training. FSDP shards model parameters across GPUs to maximize throughput on constrained hardware, while DDP replicates them for simpler multi-GPU execution.

Multi-node launch tooling for mixed-precision training initializes the distributed environment via the DistributedLauncher. You select a preset like for_1b or for_7b and run your training script to apply mixed-precision policies.

Distributed training works with 7B parameter models on constrained hardware by applying FSDP wrapping, mixed-precision policies, gradient accumulation, and memory estimation to maximize throughput and manage memory efficiently.

Gradient accumulation and phase-based checkpointing handle distributed training by saving model states at specific phases. This enables reproducible scaling and allows recovery without restarting the multi-node training workflow.

You need torch to implement learning-rate scaling and memory estimation for distributed training. The workflow applies these torch-based optimizations alongside DDP/FSDP wrapping to maximize multi-GPU training throughput.