Explain Transformers and MoE in LLMs

Q: Explain Transformers and MoE in LLMs

This is a Machine Learning interview question from Amazon for Machine Learning Engineer roles. View the full question and solution on PracHub.

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Question

You are interviewing for a role working with large language models (LLMs).

Explain the following concepts and how they relate to building and scaling LLMs:

Transformer architecture
- What are the key components (e.g., self-attention, multi-head attention, positional encodings, feed-forward networks)?
- How does the self-attention mechanism work at a high level?
- Why are Transformers well-suited for language modeling compared to RNNs/LSTMs?
Mixture-of-Experts (MoE) architecture
- What problem does MoE try to solve in the context of LLMs?
- How does expert routing work conceptually (e.g., gating networks, top-k experts)?
- What are the main trade-offs of MoE (compute efficiency vs. model complexity, training stability, load balancing)?
Collective communication and parallelism for LLMs
- Briefly describe common forms of parallelism used to train and serve large models: data parallelism, tensor/model parallelism, and pipeline parallelism.
- What is collective communication (e.g., all-reduce, all-gather, broadcast) and why is it critical for large-scale distributed training?
- Give a simple example of where an all-reduce operation is used when training a Transformer model.

Focus on clear explanations that would help a strong software engineer understand how large language models are structured and scaled.

Explain Transformers and MoE in LLMs

Solution

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