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Related Questions
- What are the typical trade-off scenarios where model size and computational resources are often at odds in LLMs?
- How does the relationship between model size and computational resources impact the training time and inference speed of LLMs?
- What are the implications of increasing model size on the memory requirements and storage costs for LLMs?
- How do different optimization techniques, such as pruning and quantization, affect the trade-off between model size and computational resources?
- What are the key considerations when selecting between larger, more accurate models and smaller, faster models in LLMs?
- Can you explain how the choice of hardware architecture, such as GPU or TPUs, influences the trade-off between model size and computational resources in LLMs?
- What are the potential consequences of over- or under-estimating the computational resources required for a given model size in LLMs?
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