GenAI

Why speed matters on the front-end

From the perspective of the user, speed is obviously an important factor. Most information is available on the internet within seconds, with most pages loading 5 seconds or less. Given that this is the time most people expect a response after requesting information, having very delayed responses will be a nuisance for users. Large language models (LLMs) and conversational AIs are often meant as an alternative to searching information up on google. If the time it takes to look something up on Google is significantly faster than the response time for your LLM, user satisfaction will decrease. GenAI is meant as a replacement for very arduous tasks. If these tasks end up being shorter time-wise than a response from your GenAI, it lacks this fundamental principle.

Why speed matters in the back-end

In the back-end, speed very often equates cost. Having to use hardware for very long times in order to respond to inference requests is costly. Modern hardware has optimizations intended to perform large-scale operations with speed. GPUs excel at parallelization tasks and matrix multiplication, which are heavily needed for GenAI workloads. Innovative software solutions have been developed, famously Nvidia with CUDA. CUDA is a software interface designed for parallelization tasks. Taking advantage of these innovations is more energy efficient than using older hardware.

A way to fix latency issues is by using High Performance Computing (HPC). HPC is a type of multi-core hardware infrastructure designed to process data and perform calculations at very high speeds using various optimization techniques such as parallel processing. It’s used extensively in various scientific fields such as climate monitoring, biomedical research and physics simulations.

HPC as a possible solution

Utilizing HPC for GenAI workloads seems like a match made in heaven. As detailed in this paper, “HPC is critical in mitigating latency for real-time LLM applications”. HPC can be useful to optimize the training process of LLMs as well as the inference time of the live model. However, challenges remain in the integration. Specifically in adapting LLMs for HPC, which may require extensive knowledge of both fields to do effectively. Bytesnet is especially relevant for this topic as it offers extensive HPC capabilities.

Small language models as a possible solution

Another option could be to use smaller models. It isn’t always necessary to use the latest and greatest LLM. While they may seem very appealing and flashy, it is often not the most cost-effective solution. LLMs are like sports cars, impressive and fun to use, SLMs are like a cheap family sedan. Both of these cars can be used for commutes just as both LLMs and SLMs can be used for effective GenAI usage.

Conclusion

To summarize, speed is fundamental to generating value with GenAI. On the front-end, the speed in which your GenAI responds to an inference is directly proportional to user satisfaction. On the back-end, utilizing modern hardware and software which come with optimization techniques will make the generating process much more efficient. We also discussed two possible solutions to make GenAI faster. The first is to utilize HPC, which seems like a match made in heaven for GenAI. The second is to use smaller models, SLMs can be as powerful as an LLM and cost far less computational power.