Unlocking Faster LLM Inference with TensorRT-LLM’s Speculative Decoding
Summary
TensorRT-LLM, an open-source library developed by NVIDIA, significantly boosts the inference throughput of large language models (LLMs) by leveraging speculative decoding. This technique uses two models sequentially: a smaller, faster draft model and a larger, slower target model. By speculatively generating tokens with the draft model and then verifying them with the target model, TensorRT-LLM achieves up to 3.6 times faster inference throughput. This article delves into the details of speculative decoding, its setup, and the performance improvements it offers.
Understanding Speculative Decoding
Speculative decoding, also known as speculative sampling, is a technique that pays a small additional computation cost to speculatively generate the next several tokens. These tokens are then verified by the target model to ensure the quality of output generation while giving a throughput boost. The key to this technique is using two models sequentially: a smaller, faster draft model and a larger, slower target model. The draft model speculates the future output tokens, while the target model determines how many of those tokens it should accept.
Setup and Performance
Setting up speculative decoding with TensorRT-LLM involves several steps. The draft model and the target model are run sequentially, with the draft model generating tokens that are then verified by the target model. The performance improvements are significant, with up to 3.6 times faster inference throughput compared to not using speculative decoding.
| Draft Model | Target Model | Tokens/Sec | Speedup |
|---|---|---|---|
| Llama 3.2 1B | Llama 3.1 405B | 111.34 | 3.33x |
| Llama 3.1 405B | Llama 3.1 405B | 120.75 | 3.61x |
| Llama 3.2 3 | Llama 3.1 405B | 101.86 | 3.04x |
| No Draft Model | Llama 3.1 405B | 33.46 | N/A |
How Speculative Decoding Works
The process of speculative decoding involves the following steps:
- Draft Model Generation: The draft model generates the next several tokens based on the input sequence.
- Target Model Verification: The target model verifies the quality of the tokens generated by the draft model.
- Token Acceptance: The target model determines how many of the tokens generated by the draft model it should accept.
Benefits of Speculative Decoding
The benefits of speculative decoding include:
- Faster Inference Throughput: Speculative decoding can achieve up to 3.6 times faster inference throughput compared to not using speculative decoding.
- Lower End-to-End Request Latency: By generating statistically more than one token per iteration, speculative decoding yields a lower end-to-end request latency.
Further Considerations
For those interested in exploring more about speculative decoding and its applications, it is recommended to delve into the detailed setup and performance metrics provided by NVIDIA’s technical blog. Additionally, understanding the trade-offs between throughput and time per output token, as well as the importance of batching in LLM inference, can provide a more comprehensive view of how to optimize LLM performance.
Conclusion
TensorRT-LLM’s speculative decoding is a powerful technique for boosting the inference throughput of large language models. By leveraging two models sequentially, a smaller, faster draft model and a larger, slower target model, TensorRT-LLM can achieve significant performance improvements. This technique is particularly useful for applications that require fast and efficient LLM inference. With its ability to generate tokens more efficiently and reduce end-to-end request latency, speculative decoding is a valuable tool for anyone working with LLMs.