multi-gpu training strategies

**Multi-GPU training strategies** is the **parallelization approaches for distributing model computation and data across multiple accelerators** - strategy choice determines memory footprint, communication cost, and scaling behavior for a given model and cluster. **What Is Multi-GPU training strategies?** - **Definition**: Framework of data parallel, tensor parallel, pipeline parallel, and hybrid combinations. - **Decision Inputs**: Model size, sequence length, network topology, memory per GPU, and target throughput. - **Tradeoff Axis**: Different strategies shift bottlenecks among compute, memory, and communication domains. - **Operational Outcome**: Correct strategy can reduce time-to-train by large factors on fixed hardware. **Why Multi-GPU training strategies Matters** - **Scalability**: Single strategy rarely fits all model sizes and hardware configurations. - **Memory Fit**: Hybrid partitioning allows models to train beyond single-device memory limits. - **Throughput Optimization**: Balanced strategy minimizes idle time and communication tax. - **Cost Control**: Efficient parallelism improves utilization and lowers run cost. - **Roadmap Flexibility**: Strategy modularity supports growth from small clusters to large fleets. **How It Is Used in Practice** - **Baseline Selection**: Start with data parallel for fit models, then add tensor or pipeline when memory limits are hit. - **Topology-Aware Placement**: Map parallel groups to physical links that minimize high-latency cross-node traffic. - **Iterative Validation**: Benchmark strategy variants against tokens-per-second and convergence quality metrics. Multi-GPU training strategies are **the architecture choices that determine distributed learning efficiency** - selecting the right parallel mix is essential for scalable, cost-effective model development.