AI Foundations: Grouped-Query Attention Tradeoffs

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-foundations-ai-grouped-query-attention-tradeoffs-r10a4-creators

1. When migrating from multi-head attention (MHA) to grouped-query attention (GQA), what typically happens to model quality?

   1. Quality stays exactly the same on every task
   2. Most tasks experience negligible quality loss, but some tasks may regress
   3. Quality improves across all tasks due to reduced overfitting
   4. Quality always degrades significantly and requires complete retraining

2. What is the primary memory-saving mechanism in grouped-query attention?

   1. GQA reduces the precision of cached values
   2. GQA reduces the number of attention heads that need to be cached
   3. GQA eliminates the need for attention computations entirely
   4. GQA compresses the model weights themselves

3. According to the tradeoff between cache size and quality, if you want the smallest possible KV cache, which configuration would you choose?

   1. GQA with 2 query groups
   2. MHA with all heads having separate K and V
   3. Multi-query attention (MQA) with 1 group
   4. GQA with 8 query groups

4. When deploying GQA in a production system, why does the lesson recommend testing multiple group counts (1, 2, 4, 8) on your own evaluation suite?

   1. To determine which group count matches the original training configuration
   2. To compare inference speed across different hardware platforms
   3. To find the configuration with the best quality-to-memory tradeoff for your specific use case
   4. To find the configuration that produces the smallest model file

5. A developer notices their long-context retrieval accuracy drops after switching from MHA to GQA. Which statement best explains this?

   1. The model was not retrained after the architecture change
   2. The evaluation suite is using incorrect attention masks
   3. GQA is known to regress first on long-context retrieval tasks
   4. The KV cache was not properly initialized during the migration

6. Can grouped-query attention completely eliminate the need for KV cache during inference?

   1. No, GQA still requires KV cache but uses significantly less memory than MHA
   2. Yes, GQA eliminates KV cache entirely by computing attention on-the-fly
   3. Yes, but only for short sequences under 512 tokens
   4. No, KV cache is completely unnecessary for any transformer architecture

7. What is required when migrating a model from MHA architecture to GQA?

   1. The model can be converted without any retraining
   2. Continued pretraining or fine-tuning is necessary to maintain quality
   3. Only the attention implementation code needs to change
   4. A complete model rebuild from scratch is required

8. A student claims that switching to GQA will always improve inference speed. Why might this be incorrect?

   1. Inference speed depends on hardware and batching, not attention mechanism
   2. GQA reduces memory but speed depends on whether the workload is memory-bound or compute-bound
   3. GQA has no effect on inference speed whatsoever
   4. GQA actually always decreases inference speed due to complexity

9. If your application requires matching multi-head attention quality exactly on all tasks, what should you consider about GQA?

   1. GQA only differs from MHA in speed, not quality
   2. GQA cannot guarantee matching MHA quality on every task
   3. GQA outperforms MHA on all tasks by design
   4. GQA will always match MHA quality if properly tuned

10. What is the relationship between the number of query groups and the size of the KV cache in GQA?

    1. They are unrelated
    2. They are inversely proportional
    3. They are directly proportional
    4. They follow a logarithmic relationship

11. Which of the following is NOT a capability of grouped-query attention?

    1. Trading off cache memory for model quality
    2. Completely eliminating the need for KV cache storage
    3. Sharing K and V matrices across query groups
    4. Reducing memory requirements compared to full multi-head attention

12. When planning continued pretraining from MHA to GQA, what should you estimate first?

    1. The inference batch size you plan to use
    2. The exact number of tokens needed for convergence
    3. The number of attention heads in the original model
    4. The memory savings you expect from the configuration change

13. Why might a developer choose GQA with 4 groups instead of 2 groups?

    1. To achieve maximum possible memory savings
    2. To match MQA performance exactly
    3. To improve model quality at the cost of some additional memory
    4. To eliminate the need for any retraining

14. What specific type of task should you test explicitly when validating a GQA model?

    1. Short conversational responses
    2. Long-context retrieval tasks
    3. Image classification tasks
    4. Code generation tasks

15. A 14-year-old learning about AI reads that GQA 'halves cache memory with negligible quality loss.' They want to use GQA for their project. What's the most important next step?

    1. Test different group counts on their specific evaluation data before committing
    2. Deploy immediately since the lesson says quality loss is negligible
    3. Use the default group count without any testing
    4. Replace all attention mechanisms in their model with GQA