Late April 2024, saw announcements of new Small Language Models (SLMs) from two major AI platform providers.
Microsoft announced the Phi-3 family of small language models, which includes the Phi-3-mini, the Phi-3-small, and the Phi-3-medium. A few days later H2O.ai released an upgraded foundational model, H2O-Dunabe2 and a model for chat-specific use cases, the H2O Danube2 Chat.
The buzz around SLMs is worth paying attention to. SLMs offer capabilities similar to large language models (LLMs) but require far less training data and processing power. Easier to adopt, less expensive to run, and with a smaller carbon footprint, these models hold the potential to further accelerate the already rapid pace of GenAI adoption.
Why are SLMs an attractive option?
As training techniques improve, smaller models with fewer parameters are becoming more and more accurate, increasing their appeal. SLMs can be more easily trained and fine-tuned, making them an attractive option for companies that want to customise a language model.
Additionally, since they utilise far less computing power than an LLM, they don’t require a massive investment in expensive infrastructure, and are therefore a much more feasible option for on-premises, at the edge, or on device deployments. They can summarize documents, surface key insights from text, and create sales or marketing content.
The smaller models make sense for simpler tasks, can work offline, and are a good alternative when organisations want to process information close to the source of collection, for example if they are building applications that require low latency or if they prefer to keep their data on-premises.
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By GlobalDataLLMs still have their place
In contrast, LLMs are ideal for applications that involve orchestration of multiple tasks or that need to excel at advanced reasoning and analysis. However, they necessitate a massive amount of infrastructure to host, and therefore generally require organisations to move their data to a third party that is running the model.
Even though most organisations are starting their GenAI journey with OpenAI (often via Azure) at present, many will likely begin to explore alternative models before long.
Some companies have noted that Azure costs are rising, which may prompt them to explore other options. Additionally, organisations have reported that the limit on the number of query requests that can be performed by OpenAI’s models in a given time period is holding them back from expanding deployments of GenAI.
For many organisations, the future will likely be a multi-model and hybrid-model environment. Some applications, possibly those that are customer facing, will require one or more LLMs hosted on the cloud, whereas other applications will perform well with SLMs that are locally hosted.
Diversification
Finally, companies would be wise to diversify and to not place all their eggs in one basket. The GenAI market is young and constantly has new entrants. As it matures, there will be inevitable product withdrawal, startup failures, and of course consolidation via merger and acquisition.
Diversification is a smart strategy at this point for ensuring long term availability and performance optimisation, plus it allows enterprises to exert pricing pressure via competition.
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