Advanced~3h
Speculative Decoding & Lookahead in AI Inference & ServingInterview Readiness Check
Draft-verify mechanism, acceptance rate math, draft model selection, self-speculative decoding (MEDUSA heads), speculative decoding for different GPU regimes, Lookahead decoding, and when speculation hurts..
speculative decodingdraft modelacceptance rateMEDUSAlookahead decodinglatency
Learning path
Try a sample question
In speculative decoding, a small 'draft' model generates K candidate tokens, and the large 'target' model verifies them in parallel. What is the key property that makes the target model's verification faster than generating K tokens sequentially?
A.The target model verifies all K tokens in one forward pass (similar to prefill), while sequential generation would require K separate forward passes
B.The draft model encodes all K candidate tokens into a single compressed embedding vector before transmitting to the target model, which then decodes the embedding rather than running a full attention forward pass
C.The target model maintains a precomputed probability lookup table indexed by token ID, allowing it to verify each draft token's likelihood with a single table lookup instead of running any attention or FFN computation
D.Verification intentionally skips all FFN (feed-forward network) layers and runs only the attention sub-layers, which reduces per-layer compute by approximately 4× since FFN accounts for roughly 2/3 of transformer FLOPs
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What this module tests
- Conceptual understanding of speculative decoding & lookahead fundamentals
- Trade-offs and design decisions in real system scenarios
- speculative decoding and draft model — applied knowledge
- Interview-style reasoning and explanation quality
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10 questions · Dimension scoring · AI coach feedback · Shareable result
Test your knowledge — free →No credit card · Takes 10–15 min
Frequently asked questions
- What does the Speculative Decoding & Lookahead check test?
- The check covers draft-verify mechanism, acceptance rate math, draft model selection, self-speculative decoding (medusa heads), speculative decoding for different gpu regimes, lookahead decoding, and when speculation hurts.. Questions span beginner to advanced difficulty and test both conceptual understanding and practical trade-off reasoning.
- What keywords should I know for Speculative Decoding & Lookahead?
- Key concepts include: speculative decoding, draft model, acceptance rate, MEDUSA, lookahead decoding, latency. These frequently appear in ai inference & serving interviews at product and infrastructure companies.
- How many questions are in the check?
- Each check has 10 questions — a mix of MCQ and short-answer. You get scored by dimension (e.g., scalability, consistency) so you know exactly where to focus.
- How long does it take to study Speculative Decoding & Lookahead?
- Most engineers spend around 3 hours on this module. The check itself takes about 10–15 minutes. You can retake it up to 3 times per day.
- What prerequisite knowledge do I need before Speculative Decoding & Lookahead?
- Speculative Decoding & Lookahead builds on Batching Strategies for LLMs. Complete those first for the best results.
- Is speculative decoding, draft model, acceptance rate covered?
- Yes. The Speculative Decoding & Lookahead module covers speculative decoding, draft model, acceptance rate among other key concepts. The check includes questions that reflect real ai inference & serving interview patterns.