What is Speculative Decoding?
Speculative Decoding
An inference optimization that uses a smaller "draft" model to generate candidate tokens, which a larger model then verifies in parallel. Speculative decoding can speed up LLM inference by 2-3x on many workloads.
How Speculative Decoding Works
A small draft model generates several token candidates quickly. The larger target model evaluates these candidates in a single forward pass, accepting any that match its distribution and rejecting the rest. The accept-reject step is mathematically structured to preserve the target model's output distribution exactly. Effective speculative decoding requires the draft model to be a good approximation of the target. Common pairings include using a smaller version of the same model family (Llama-7B drafting for Llama-70B) or distilled drafts.
Why Speculative Decoding Matters
Speculative decoding is the most effective inference speedup that does not change model quality. The core LLM serving frameworks (vLLM, TensorRT-LLM, SGLang) all support it. For high-throughput LLM applications, speculative decoding reduces serving costs by 30-50% with no quality loss. Engineers working on LLM serving should understand the technique.
Practical Example
A high-volume customer service AI deployment serves 50M tokens per day. Adding speculative decoding with a 1B draft model for an 8B target model reduced inference cost by 38% and lowered p95 latency by 41%. The draft model added only 200MB of GPU memory overhead.
Use Cases
- LLM serving
- Cost reduction
- Latency reduction
- High-throughput inference
Salary Impact
LLM inference optimization expertise commands $300K and up at AI infrastructure-focused companies.
Where this skill pays off
This skill shows up most in software engineering roles. See live data on the AI premium, the tools, and what hiring managers screen for.
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Related Terms
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Frequently Asked Questions
What does Speculative Decoding stand for?
Speculative Decoding stands for Speculative Decoding. An inference optimization that uses a smaller "draft" model to generate candidate tokens, which a larger model then verifies in parallel. Speculative decoding can speed up LLM inference by 2-3x on many workloads.
What skills do I need to work with Speculative Decoding?
Key skills for Speculative Decoding include: Inference Optimization, PyTorch, CUDA, LLM Serving. Most roles also expect Python proficiency and experience with production systems.
How does Speculative Decoding affect salary?
LLM inference optimization expertise commands $300K and up at AI infrastructure-focused companies.
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