Verified75% confidenceFactExact time
Speculative decoding generates candidate tokens through a lightweight draft model, with the main model verifying them in parallel in a single forward pass, accepting tokens that conform to the distribution and regenerating from the first rejected position.
3
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75%
Confidence
Long-term
Relevance
9/24/2026
First Seen
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Related Claims
VerifiedSpeculative Decoding uses a small, fast draft model to predict multiple tokens in advance, which are then verified in parallel all at once by the large model.75% similarUnverifiedSpeculative Decoding uses a smaller draft model to quickly predict subsequent tokens, which are then batch-verified by the main model, significantly improving overall throughput; MTP is one implementation variant of this, and models such as Qwen support it natively.74% similarUnverifiedIn a single one-step denoising step, the model directly reads out the probability distribution for each answer slot rather than sampling token by token.74% similarUnverified推测解码技术通过小模型预测草稿、大模型验证的方式加速生成，KV Cache共享机制降低重复请求的计算冗余72% similarUnverifiedDuring Transformer model inference, input tokens can be processed in parallel (prefill stage), while output tokens must be generated one by one autoregressively (decode stage), causing the unit price of output tokens to typically be higher than that of input tokens.71% similar
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