How to choose the right LLM for your business
By 10Pearls editorial team
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Podcast: Navigating the LLM Landscape and Choosing the Right Model
This podcast explores critical business considerations when opting for an open-source or proprietary large language model (LLM). Some key technical factors include performance speed and accuracy, as well as model capabilities, like size and context length.
Additional factors such as usage rights, subscription costs, tokens, maintenance, and data privacy are also important. This discussion provides a detailed cost-benefit analysis of both open-source and proprietary LLMs. The choice ultimately hinges on weighing a number of factors, such as a business’ unique requirements, risk aversion, budget, and other available resources.
Choosing the Right LLM for Your Business
This podcast has been AI-generated based on content from our blog. While we strive for accuracy, the information presented is intended for informational purposes only and may not fully capture the nuances of the original blog post. Please refer to the written content for the most accurate and comprehensive details.
Key considerations for LLM selection
In this podcast, 10Pearls has highlighted the following key factors to consider when choosing an LLM:
Performance
Performance can be gauged broadly in two categories: speed and precision.
- Inference speed
Speed determines how quickly an LLM processes information and provides an output. This is especially important for chatbots, where immediate turnaround is expected. - Precision
While speed is one of the most important aspects of an LLM, precision is equally important. There’s no use in getting quick responses if they’re factually incorrect.
Model dimensions
Model dimension can be explained as a model’s brain power and memory, which can be gauged by a model’s size and context length. speed and precision.
- Context length
Context length can be defined as a model’s short-term memory. It defines how much a model can retain and reference information. Larger context length requires a model to “remember” and have contextually relevant conversations. - Model size
This can be measured in billions of parameters and is a generic indicator of a model’s complexity and capability. Larger models tend to be more sophisticated, with the ability to process billions of data points within training a model.
Usage rights
These are the licensing terms and restrictions for using and modifying a model. Proprietary LLMs provide robust infrastructure and restrictive terms of service, with less flexibility for customization. In contrast, open-source models offer greater flexibility and added maintenance and security responsibilities.
Cost
Cost encompasses both the upfront and ongoing expenses associated with an LLM. Businesses should evaluate these costs in the context of their budget and long-term scalability requirements.
- Direct costs
Subscription fees or usage-based pricing for paid models are direct costs. While the cost per token starts at an affordable rate, it can add up quickly with higher volumes of tokens used. - Indirect costs
While an open-source model does not have a subscription fee, other indirect costs, such as infrastructure, customization, and maintenance expenses, must be considered.
Scalability
This comes down to an LLM’s ability to handle an increasing user load and demand. While open-source models can be customized to offer tailored solutions for your business, paid models often offer better scalability due to cloud infrastructure.
Data privacy
While open-source models are bound by legalities related to data privacy, they will have access to your data. While developing a personalized open-source model allows hosting it on private servers for greater data control, it requires a robust infrastructure and could be costly.
Key takeaways:
- Performance: Balance speed (fast outputs) and precision (accuracy).
- Model dimensions: Larger models handle more data, context length enables better memory.
- Usage rights: Proprietary models offer reliability, open-source allow customization but need maintenance.
- Costs: Proprietary models have subscription fees, open-source requires infrastructure investment.
- Scalability: Proprietary models scale easily, open-source needs customization for growth.
- Data privacy: Open-source enables private hosting, proprietary models manage data differently.
- Business fit: Choose based on needs, budget, and risk tolerance.
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