Scale model training across multiple GPUs and nodes using data parallelism and model sharding strategies. Train billion-parameter models efficiently with DDP, FSDP, and DeepSpeed.
Foundation model training, large-scale experiments, architecture exploration
Custom LLM development, search ranking models, recommendation systems
Medical imaging models, genomics analysis, drug discovery simulations
Vision models, sensor fusion networks, simulation training
Time series models, risk analysis, fraud detection at scale
Choose DDP for models under 500M params, FSDP for 500M-10B, DeepSpeed ZeRO for 10B+ parameters
Configure multi-node GPU cluster with high-bandwidth interconnects (NVLink, InfiniBand)
Enable BF16/FP16 training with automatic loss scaling for 2x throughput improvement
Configure gradient accumulation, communication overlap, and memory optimization
Implement universal checkpointing for elastic scaling and fault recovery
Set up GPU utilization, memory, and throughput monitoring across the cluster
| Component | Function | Tools |
|---|---|---|
| Data Parallelism | Replicate model across GPUs, split data batches | PyTorch DDP, Horovod |
| Model Sharding | Distribute model parameters across GPUs | PyTorch FSDP, DeepSpeed ZeRO |
| Mixed Precision | FP16/BF16 training with loss scaling | torch.cuda.amp, DeepSpeed |
| Communication | Gradient synchronization, all-reduce operations | NCCL, Gloo, MPI |
| Checkpointing | Distributed state saving and loading | PyTorch DCP, DeepSpeed |
| Orchestration | Job scheduling, resource allocation | Kubernetes, Ray, SLURM |
Let us help you design a distributed training infrastructure that maximizes GPU efficiency.
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