AI Tools: vLLM Prefix Caching for Throughput

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-tools-ai-vllm-prefix-caching-r10a4-creators

1. What does vLLM's automatic prefix caching reuse across requests that share identical beginning text?

   1. GPU compute cycles during inference
   2. PCIe transfer bandwidth allocations
   3. CPU thread scheduling priorities
   4. KV (key-value) blocks from earlier computations

2. Which configuration parameter must be set to enable automatic prefix caching in vLLM?

   1. cache_strategy equal to 'prefix'
   2. prefix_mode configuration set to automatic
   3. enable_prefix_caching set to true
   4. reuse_kv_blocks enabled by default

3. A developer notices their prefix cache hit rate has dropped significantly below their established baseline. What should they do?

   1. Ignore it since prefix caching is optional
   2. Investigate why the hit rate dropped and alert on this metric
   3. Disable prefix caching entirely since it's clearly failing
   4. Restart the vLLM service to reset the cache

4. Why does prefix caching provide minimal benefit when every incoming prompt is completely unique?

   1. The KV blocks are stored in CPU memory which is too slow
   2. The GPU memory becomes too small to hold any cache
   3. Unique prompts require different model weights
   4. There are no common prefixes to cache across requests

5. What limitation remains even when prefix caching is fully enabled and functioning optimally?

   1. Cold-start latency cannot be eliminated
   2. All inference requests become instant
   3. Throughput is multiplied indefinitely
   4. GPU memory requirements drop to zero

6. When a language model generates a very long output, what happens to previously cached prefixes?

   1. Long outputs improve cache efficiency by default
   2. Long generations may evict cached prefixes from memory
   3. Long generations automatically enable larger cache sizes
   4. Cached prefixes are preserved indefinitely regardless of length

7. What is the relationship between prefix caching and request batching in vLLM?

   1. They conflict and should never be used together
   2. Request batching makes prefix caching unnecessary
   3. Prefix caching replaces the need for any batching
   4. They are complementary techniques that can both improve throughput

8. What should be considered when sizing GPU memory for the prefix cache?

   1. Disable cache memory allocation to prioritize batch size
   2. Allocate enough memory to hold expected prefix blocks without constant eviction
   3. Use the smallest possible cache to maximize available memory for inference
   4. Set cache size equal to total GPU memory for maximum hits

9. Which Prometheus metric is most important to monitor for understanding prefix caching effectiveness?

   1. Prefix cache hit rate
   2. Model loading time
   3. Request queue length only
   4. GPU temperature during inference

10. A team runs their vLLM deployment with prefix caching enabled but sees no throughput improvement. The workload consists of thousands of unique user queries with no shared system prompt. What explains this?

    1. Unique queries actually benefit more than repeated ones
    2. Prefix caching only works with batched requests, not individual ones
    3. Without shared prefixes, there are no KV blocks to reuse across requests
    4. The enable_prefix_caching flag requires additional dependencies

11. Why do system prompts particularly benefit from prefix caching?

    1. System prompts are processed faster by the model architecture
    2. They appear at the beginning of every request and remain identical across users
    3. They require less GPU memory to process
    4. System prompts are stored in CPU cache by default

12. When throughput doubles after enabling prefix caching, what typically caused this improvement?

    1. Request batching was automatically enabled
    2. The GPU clock speed automatically increased
    3. The same KV blocks were computed once but reused for multiple requests
    4. The model was switched to a more efficient architecture

13. Can prefix caching alone replace the need for request batching?

    1. Yes—batching becomes unnecessary with good cache performance
    2. Only for synchronous request patterns
    3. No—batching and caching are complementary optimizations
    4. Only if batch sizes are less than 8 requests

14. What happens to the effective throughput when prefix cache hit rate is high?

    1. Effective throughput multiplies because redundant computation is avoided
    2. Effective throughput only improves for the first request
    3. Effective throughput becomes unlimited
    4. Effective throughput stays the same but latency decreases

15. A team wants to optimize a chat application where each user message is completely different. What should they expect from enabling prefix caching?

    1. Reduced GPU memory usage
    2. Faster cold-start times for new conversations
    3. Minimal throughput improvement due to lack of shared prefixes
    4. A doubling of throughput since all chats use similar templates