Speculative decoding is a technique used to accelerate the inference speed of large language models like DeepSeek-R1 by leveraging a smaller draft model to generate candidate tokens in parallel. This approach can significantly reduce latency by allowing the model to produce multiple tokens simultaneously, which are then verified by the larger model to ensure accuracy. However, while speculative decoding generally improves throughput, it can introduce variability in latency due to the verification process.
How Speculative Decoding Works
1. Parallel Token Generation: A smaller draft model generates multiple candidate tokens in parallel. This is faster than sequential generation by the larger model because it utilizes GPU acceleration more efficiently[1][3].
2. Verification Process: The larger model verifies these candidate tokens. If they match the expected output, they are accepted; otherwise, only the incorrect tokens are recalculated[3][9].
Impact on Latency
- Reduced Average Latency: Speculative decoding can reduce the average latency by generating tokens more quickly. This is because the draft model requires fewer resources and can operate faster than the larger model[3][5].
- Variable Latency: While speculative decoding improves overall throughput, it can lead to inconsistent latency. When the draft model's predictions are incorrect, the larger model must recalculate, which can cause spikes in latency[3][9].
DeepSeek-R1 Specifics
DeepSeek-R1 incorporates enhancements like Multi-Token Prediction (MTP) and optimized speculative decoding, which further improve inference speed. MTP allows DeepSeek-R1 to predict multiple tokens in parallel, reducing decoding latency without compromising coherence[4]. The optimized speculative decoding in DeepSeek-R1 uses probabilistic agreement checking, accepting predictions based on confidence thresholds rather than exact matches, which reduces rejection rates and accelerates inference[4].
Overall, speculative decoding can significantly enhance the performance of DeepSeek-R1 by reducing average latency and improving throughput, but it may introduce variability in latency due to the verification process.
Citations:
[1] https://centml.ai/resources/2x-inference-speed-on-r1
[2] https://iaee.substack.com/p/deepseek-r1-intuitively-and-exhaustively
[3] https://www.theregister.com/2024/12/15/speculative_decoding/
[4] https://aman.ai/primers/ai/deepseek-R1/
[5] https://arxiv.org/html/2503.07807v1
[6] https://www.reddit.com/r/LocalLLaMA/comments/1i64ffn/draft_model_speculative_decoding_performance_with/
[7] https://arxiv.org/html/2502.02789
[8] https://www.linkedin.com/posts/lamersrick_i-worked-on-this-speculative-decode-version-activity-7293321395000819712-8yvC
[9] https://predibase.com/blog/predibase.com/blog/deepseek-r1-self-distillation-turbo-speculation
[10] https://aws.amazon.com/blogs/machine-learning/deploy-deepseek-r1-distilled-models-on-amazon-sagemaker-using-a-large-model-inference-container/