Large language models have been making waves in the field of natural language processing (NLP) with their impressive performance on various tasks. However, these models often come with high computational demands, making it challenging to deploy them in real-world applications. This is where the utilization of CPUs for accelerating the inference of large language models becomes crucial.
In their paper, the authors propose a parallelized approach to enhance the throughput of large language models. They achieve this by leveraging the parallel processing capabilities of modern CPU architectures and batching the inference requests. Through extensive evaluation, they demonstrate that their accelerated inference engine provides a substantial improvement in the number of tokens generated per second. Specifically, they report an impressive 18-22x improvement in throughput, which becomes even more significant for longer sequences and larger models.
One interesting finding discussed in the paper is the ability to run multiple workers in the same machine with NUMA (non-uniform memory access) node isolation. By doing so, the authors observe a 4x additional improvement in tokens per second, as reflected in Table 2. This scalability is essential for handling high-volume tasks efficiently and can greatly benefit Gen-AI based products and companies.
Moreover, the implications of using CPUs for inference go beyond just performance gains. The authors highlight the potential environmental benefits of reducing power consumption, estimating a remarkable 48.9% reduction in CPU usage for inference. This not only makes large language models more sustainable but also demonstrates the feasibility of achieving production-ready throughput and latency while maintaining an eco-friendly approach.
In conclusion, this paper presents a promising approach to address the computational demands of deploying large language models for real-world applications. By leveraging CPUs and implementing parallelization techniques, the authors achieve significant improvements in both throughput and environmental sustainability. These findings pave the way for more efficient and scalable deployment of large language models, opening up exciting possibilities for further advancements in NLP.