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Related Questions
- How does model parallelization affect the accuracy of large language models when distributing the computation across multiple machines?
- What is the optimal partitioning strategy for model parallelization in terms of achieving a balance between model size and computational requirements?
- Can you explain how distributed training affects the speedup of training large language models, and what factors influence the speedup?
- How does the choice of communication protocol impact the efficiency of distributed training for large language models?
- What are some common challenges in implementing model parallelization and distributed training for large language models, and how can they be addressed?
- How does the trade-off between model size and computational requirements change as the number of parallel devices increases in model parallelization?
- Can you discuss the implications of using model parallelization and distributed training on the deployment and inference of large language models in real-world applications?
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