In the era of Generative AI (GenAI) and Large Language Models (LLMs), it is undeniable that their capabilities are continuously improving; from more accurate and plausible responses to higher quality and multi-modals outcomes just to name a few. However, LLMs still suffer from inherent issues that prevent them from being widely adopted in the enterprise. Some of these issues are still active areas of research.
LLMs may have a good “general” knowledge of the world and be capable of generating impressive and high-quality responses but they do not possess domain specific knowledge. They also tend to hallucinate in situations where they cannot generate an answer based on the trained data and they require a vast amount of training data, compute resources and highly skilled individuals to develop and deploy these models into production.
Recently, we have seen a new trend emerge- Small Language Model (SLMs). SLMs have shown a relatively good performance when compared to LLMs for specific use-cases.
Interestingly, SLMs like Llama3-8B and Mixtral 8x22B are showing promising results in certain areas, such as Question Answering, Sentiment Analysis and Reasoning. This suggests that other factors, besides the sheer size of the Language Model play important roles in its performance. Training data quality, model architecture, customization and fine-tuning techniques are all important factors determining the language model’s performance.
The key advantage of the SLMs is that they are purpose-built for specific use-case with a focused scope. As such, they require less data to train, less compute resources (GPUs) and, as a result, they are more efficient than their general-purpose LLMs counterparts.
Another advantage of SLMs is their potential for enhanced security. When coupled with advanced customization techniques, such as Retrieval Augmented Generation (RAG), where external knowledge is injected to a language model without it being trained, the result is a more factual, up-to-date responses and the ability to cite the resource or document it retrieved the response from. All within the security and the safety of the enterprise’s firewall.
In addition, the smaller compute footprints of SLMs make them more feasible to run locally on workstations or on-prem servers. It further increases flexibility, scalability, reduces the risk of data exposure during training, and shortens the development lifecycle.
Dell PrecisionTM Workstations offer a wide range of configurations and form factors to help jump start developing and deploying SLMs. Precision mobile, tower and rack workstations offer a wide selection of Intel® and AMD® processors, as well as NVIDIA® and AMD GPUs up to Quad NVIDIA RTX6000 Ada 48GB in the Precision 7960 tower workstation. Additionally, we offer a validated platform that runs state-of-the-art AI development platforms, such as Microsoft® Windows AI Studio, NVIDIA AI Workbench, HuggingFace® and others, to make it easy for the data scientist to start their AI journey.
As LLMs face growing challenges due to the need for extensive training data and substantial compute resources for fine-tuning, SLMs have emerged as a viable alternative, particularly when applied to domain-specific use cases. Enhanced security, shortened development cycles, flexibility, running locally, and lower latency are all added benefits of adopting SLMs. This has resulted in the rise of the SLM and generated a heightened interest in the field to evolve the AI ecosystem at an unprecedented pace.
