What is Distributed Training?
Distributed Training parallelizes model training across multiple GPUs or machines to handle models and datasets too large for single devices. Modern LLM training requires distributed approaches using data, model, and pipeline parallelism.
This LLM training and alignment term is currently being developed. Detailed content covering technical concepts, implementation approaches, best practices, and practical considerations will be added soon. For immediate guidance on LLM training strategies, contact Pertama Partners for advisory services.
Distributed training transforms prohibitively long single-GPU training runs into manageable multi-day campaigns by parallelizing computation across dozens or hundreds of devices. Organizations mastering distributed training techniques access model capabilities that single-machine training cannot achieve within practical time and budget constraints. This competency separates teams capable of building competitive AI products from those limited to fine-tuning pre-existing models developed by larger organizations.
- Essential for training models beyond single GPU memory.
- Combines data parallelism, model parallelism, and pipeline parallelism.
- Communication bandwidth between devices can bottleneck training.
- Frameworks (DeepSpeed, Megatron) simplify implementation.
- Efficiency drops as parallelism increases (communication overhead).
- Cost optimization requires balancing parallelism strategies.
- Profile network bandwidth between training nodes before selecting parallelism strategies since communication overhead can consume 30-50% of wall-clock time on slow interconnects.
- Use gradient compression techniques like TopK sparsification or quantized AllReduce to reduce communication volume by 10-100x on bandwidth-constrained clusters.
- Implement fault-tolerant checkpointing that enables training resumption from the last saved state when individual nodes fail during multi-day training campaigns.
Common Questions
When should we fine-tune vs. use pretrained models?
Fine-tune when domain-specific performance is critical and you have quality training data. Use pretrained models with prompting for general tasks or when training data is limited. Consider parameter-efficient methods like LoRA for cost-effective fine-tuning.
What are the costs of training LLMs?
Training costs vary dramatically by model size, data volume, and compute infrastructure. Small models may cost thousands, while frontier models cost millions. Most organizations fine-tune rather than pretrain, reducing costs by 100-1000x.
More Questions
Implement RLHF or DPO alignment, extensive red-teaming, safety evaluations, and guardrails. Monitor for unintended behaviors in production. Safety is ongoing process, not one-time activity.
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
Flash Attention is an optimized attention algorithm that reduces memory usage and increases speed by recomputing attention on-the-fly rather than materializing full attention matrices. Flash Attention enables longer contexts and faster training for transformer models.
Ring Attention distributes attention computation across devices in a ring topology, enabling extremely long context windows by parallelizing sequence dimension. Ring Attention allows processing of contexts exceeding single-device memory.
Sparse Attention computes attention for only a subset of token pairs using predefined patterns, reducing computational complexity from quadratic to near-linear. Sparse attention enables longer context windows by limiting attention computation.
Sliding Window Attention restricts each token to attend only to nearby tokens within a fixed window, reducing complexity to linear while maintaining local context. Sliding window enables efficient processing of long sequences.
Grouped Query Attention (GQA) shares key-value pairs across groups of query heads, reducing memory and computation for multi-head attention while maintaining quality. GQA provides middle ground between multi-head and multi-query attention.
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