CIO OPINION
Role of open source
In the realm of generative AI , open-source models are increasingly playing a crucial role in democratising access to AI . Open-source models have the potential to further digital advancement and lower the barrier for experimentation with Generative AI . In addition to making technology more accessible , open-source models foster a sense of collaboration . Nevertheless , as with all things , open-source models have their own advantages and drawbacks .
IT leaders seeking better visibility , control and customisation of generative AI deployments must consider the pros and cons of open-source models outlined below .
Customisability rapidly evolving , so open source can provide users with more flexibility to swap models or model providers , with fewer exit barriers .
Longer time to value
The investments in data engineering , tooling integration and infrastructure to train and run these models can be high , particularly for larger models . This represents a significant fixed cost and longer time to value when compared with proprietary alternatives .
Complex model
Open-source models can be customised to meet the needs of organisations as developers have access to the model parameters and source code . This helps enterprises to have a better control over costs , output , and alignment with their use cases .
By owning open-source-model-based products , enterprises can continuously evolve them to meet internal and customer demands . It also makes their applications harder for competition to imitate .
Control over privacy
A key reason for enterprise interest in open-source models is that enterprises can potentially run generative AI models across the environment of their choice , on-premises , cloud or edge , which gives them significant control over data and security risks , for example , no need to send any data to the cloud if it is hosted on-premises .
Community improvement
Using open-source models , enterprises can tap into the power of development communities , which seek to constantly refine these models . Of course , this is dependent on the vibrancy of the community .
Transparency
Open-source models can be more thoroughly inspected and analysed , which could not only boost confidence in their adoption , but also enable enterprises to meet future regulatory requirements more effectively .
Vendor lock-in
The adoption of open-source models can reduce the strength of vendor lock-in . The landscape of generative AI models is
It will be harder to upgrade and manage the life cycle of open-source models when they come out , particularly if significant customisation was built on top of a given version . Additionally , maintaining consistent performance and quality standards across various model releases can also be challenging .
Varied licensing
There are a variety of licensing models within open source today . This can impose restrictions for the consumer and will require rigorous review from legal teams before adoption . For example , not all opensource models are certified for commercial use .
Skills
It takes dedicated personnel with deep expertise in fine-tuning and model operations to customise and host these models . For most companies , this is a tall order .
Gap
There is currently a gap in terms of accuracy between proprietary LLMs and open-source LLMs models , as measured by different benchmarks . This gap might narrow over time , as new models emerge .
Open-source generative AI models , such as Bloom , Llama 2 , and Starcoder have their unique strengths and weaknesses . IT leaders should consider the pros and cons and perform an objective analysis of open-source models on a case-by- case basis . Ultimately , before implementation of these models in production scenarios , a meaningful , unbiased analysis of total cost of ownership , TCO and risk assessments should be conducted . p
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