Speculative Decoding Quiz

Answers and explanations appear as you take the quiz.

1. How does speculative decoding speed up generation from a large model?
   • A small draft model guesses several tokens that the large model verifies in parallel
   • Compressing the model to half its size permanently
   • Removing the attention mechanism
   • Training the model on more data
2. For a large model at low batch size, why can one-token autoregressive decoding be slow?
   • Each token requires internet access
   • Repeated reads of large model weights can make decoding memory-bandwidth bound
   • The model permanently stores a full copy of every possible output
   • The tokenizer performs the model’s matrix multiplications
3. In greedy speculative decoding, what happens at the first token where draft and target predictions disagree?
   • The whole conversation is thrown away and restarted from scratch
   • The draft model is retrained immediately
   • The matching prefix is kept, the target token is used at the mismatch, and later draft tokens are discarded
   • The output quality permanently degrades
4. Under exact speculative sampling, how does the output distribution compare with sampling from the target model alone?
   • It is always lower quality but faster
   • It is identical only when the draft model is larger
   • It becomes deterministic regardless of target sampling
   • It is identical in distribution
5. What most directly determines the speedup from speculative decoding?
   • The acceptance rate of the draft model's guesses
   • The color of the GPU
   • The length of the model's name
   • The time of day
6. What do Medusa and EAGLE have in common as approaches?
   • They require quantum hardware
   • They eliminate the need for a separate standalone draft model
   • They delete the target model
   • They only work on images
7. What should a serving team measure before claiming speculative decoding will speed up its workload?
   • Only the target model’s parameter count
   • Only whether the draft produces fluent text
   • Draft cost, target verification cost, accepted tokens and end-to-end latency under representative traffic
   • The number of tokenizer files
8. In exact speculative sampling, what happens when a proposed token is rejected?
   • The whole conversation is discarded
   • The target model is permanently changed
   • The rejected token is kept without verification
   • A correction step samples from the distribution needed to preserve the target model’s output distribution