8bit.tr Journal
Speculative Decoding and Fast Inference: Making LLMs Feel Instant
A technical guide to speculative decoding, draft models, and system tricks that cut latency without sacrificing quality.
Why Latency Dominates User Perception
Even small delays feel large in conversational interfaces. Users judge AI quality by responsiveness as much as by accuracy.
Speculative decoding targets this gap by generating candidate tokens faster and verifying them with a stronger model.
How Speculative Decoding Works
A smaller draft model proposes multiple tokens at once. The main model then validates or rejects them in batches.
When the draft is accurate, you get multiple tokens per verification step, reducing end-to-end latency.
Draft Model Selection
The draft model must be fast and reasonably aligned with the main model. If it is too weak, acceptance rates collapse.
Teams often fine-tune a smaller sibling model or distill from the main model to maximize compatibility.
System-Level Optimizations
Speculative decoding performs best with batching, caching, and kernel fusion.
It also benefits from early exit strategies: stop verification when confidence is high enough for the task.
When It Breaks Down
High-entropy tasks and creative writing reduce acceptance rates.
In these cases, fallback routing to standard decoding may be faster overall.
Acceptance Rate Tuning
Track acceptance rate as a first class metric. If the draft model is accepted less than half the time, you may lose the latency gains you expected. Improve it by distilling the draft model on real production traffic and aligning its token distribution with the main model.
Set guardrails for quality. If acceptance spikes on low quality outputs, add validation checks or lower the max draft length. The goal is predictable speed without sacrificing reliability.
Profile the decode path end to end. Kernel launch overhead, context length, and batching policy can erase gains even when acceptance is high.
Define a fallback threshold. When acceptance drops below a fixed level, switch to standard decoding to avoid unpredictable delays.
FAQ: Speculative Decoding
Does it change output quality? Typically no, because the main model still validates tokens.
Is it hard to implement? It requires model coordination and careful batching but is feasible for most teams.
What is the biggest win? Lower latency without needing a smaller main model.
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