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LLM Training Infrastructure Strategy
A well-funded AI research lab is planning to train a new, exceptionally large language model that exceeds the memory and processing capacity of any single, commercially available computer. They are considering two potential infrastructure strategies:
Strategy 1: Commission a custom-built, monolithic supercomputer. This single machine would be engineered with unprecedented amounts of unified memory and processing cores to handle the entire training process internally. The project would be extremely expensive and have a multi-year development timeline before training could even begin.
Strategy 2: Lease a large cluster of hundreds of standard, high-performance servers and network them together. The training workload would be broken down and spread across these individual machines, which would work together on the problem simultaneously.
Based on the fundamental computational challenges of large-scale model development, which strategy is the more viable and commonly adopted approach in the industry? Justify your decision by evaluating the two strategies against the criteria of scalability, cost-effectiveness, and time-to-deployment.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Evaluation in Bloom's Taxonomy
Cognitive Psychology
Psychology
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Parallelism in Distributed LLM Training
LLM Training Infrastructure Strategy
A research team is developing a new language model with billions of parameters. They observe that their training process consistently fails on a single, top-of-the-line GPU, citing 'out-of-memory' errors. Which statement best analyzes the core computational bottleneck that requires the adoption of a distributed training strategy?
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