Quicker Large Language Models with anticipatory interpretation and AWS Inferentia2 | AWS Machine Learning Blog

Larger Language Models in Natural Language Processing

In recent years, there has been a significant increase in the size of large language models (LLMs) utilized in natural language processing (NLP) tasks like question answering and text summarization. These larger models, with hundreds of billions of parameters, have been shown to produce better results compared to smaller versions.

Performance and Cost Considerations

While larger models tend to offer improved performance, they also come with increased computational demands and higher deployment costs. For example, the median per-token latency on AWS Trainium for Llama-3-70B is 21.4 ms, significantly higher than the 4.7 ms latency for Llama-3-8B. Customers need to carefully consider performance requirements to meet user needs effectively.

Speculative Sampling Technique for Efficient Inference

Speculative sampling is a method that enhances the computational efficiency of large language model inference while maintaining accuracy. By using a smaller, faster draft model to generate multiple tokens, which are then verified by a larger, slower target model, this technique streamlines the inference process. The speculative sampling loop involves an adjustable window size k, allowing for faster processing when speculated tokens are accepted.

Implementing Speculative Sampling on AWS Neuron

To implement speculative sampling on AWS Neuron-powered instances like Inferentia and Trainium, developers can harness the benefits of both larger and smaller models for optimal performance and cost-effectiveness. By adjusting parameters like token acceptor and k value, developers can fine-tune the inference process to balance quality and efficiency.

Conclusion

With the introduction of speculative sampling and advancements in AI chip technology on AWS, developers now have the flexibility to leverage the strengths of both large and small language models. By experimenting with speculative sampling and customizing parameters, developers can achieve a balance between inference speed, cost, and model quality, enhancing the overall user experience.


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