
Advanced Strategies for GenAI Infrastructure in Production
Deep Technical Insights into Distributed Training and Inference Foundation Models
AllReduce
, AllGather
). NCCL’s standard implementation can occupy up to 24 streaming multiprocessors (SMs) on high-end GPUs such as NVIDIA A100 GPUs. AWS’s SMDDP layer — integrated into SageMaker’s Deep Learning Containers — reconfigures these collective operations. By reducing SM usage (to fewer than 9 SMs per operation) and overlapping AllReduce
and AllGather
collectives with backward passes via async transfers and low-latency GPU memory copy via GPUDirect RDMA, SMDPP liberates additional GPU cycles for core computation. In one benchmark, training 100B-parameter GPT-NeoX model on 32 p4d nodes yielded a throughput improvement of ~30.58% compared to using NCCL alone. NCCL_SOCKET_IFNAME
and NCCL_IB_HCA
) — to minimize latency across nodes. offload_activations
and activation_loading_horizon
) to meet stringent performance goals.sharded_data_parallel_degree
to 128 to balance memory requirements and communication load — ensuring that the data parallel degree is optimally set (e.g., 256 total GPUs yielding a replication factor of 2). AllReduce
, AllGather
) that underpin most distributed training setups. It is highly optimized for standard GPU clusters but typically utilizes up to 24 SMs on high-end GPUs such as the A100, which can sometimes limit available compute resources for model operations.Choose SageMaker (Hyperpod) with SMDDP for most large-scale training tasks. Leverage its ability to configure both the data parallel and model parallel strategies with parameters such as
gradient_accumulation_steps
, offload_activations
, and delayed_parameter_initialization
. Use HyperPod when training models that require persistent, self-healing clusters and where advanced orchestration (via Slurm or EKS) is necessary. Consider EKS/ECS or ParallelCluster for environments requiring granular tuning of low-level communication parameters.Any opinions in this post are those of the individual author and may not reflect the opinions of AWS.