Comparing LLMs models for GenAI development

As you look to develop and deploy a GenAI application, you’ll need to choose a large language model (LLM) to use as your base model. Your choice of LLM will significantly impact several aspects of your GenAI application. Understanding the effects and impact of your choice is important, as it will help you and your team make informed decisions that line up with your organization’s goals and resources. This can help ensure that your investment in AI technology delivers your desired outcomes. 

Key considerations for comparing LLMs 

To choose the right LLM for your GenAI application, it’s important for you to understand various technical and practical aspects.  

Model size and parameters 

The size of a model is typically described by the number of parameters it has. Parameters are the components adjusted during training to learn from data. Some models have hundreds of thousands of parameters. Others have millions or billions. GPT-4 is estimated to have upwards of 1.76 trillion parameters.  

Larger models with more parameters generally provide better performance but require more computational resources. If you see model names with a number attached, such as Gemma 2B or Mistral 7B, then this likely refers to the number of parameters for that model (2B = 2 billion, and 7B = 7 billion). 

Training dataset 

Every LLM is built with comprehensive training data, and the quality and diversity of that training data significantly impact the model’s ability to generalize. Generalization refers to the model's ability to effectively apply what it has learned from the training data to new, unseen data. This means the model can understand and respond appropriately to a wide range of inputs—even those it has never encountered before.  

The diversity of a training dataset also affects the LLM. This diversity includes different topics, writing styles, languages, and contexts. For example, diverse data better equips a model to understand both technical jargon and casual conversation. This makes the model more versatile, performing well across various applications, tasks, and situations. 

When an LLM is trained, it processes training data up until a certain point in time, known as the cutoff date. Subsequently, a model's knowledge is limited to information available up to that cutoff date. Data that is up-to-date allows the model to generate responses that are more relevant and accurate, reflecting the latest information and trends. 

As you consider various LLMs, pay attention to the training cutoff date. The recency of training data impacts your ability to maintain model performance, especially if your application will deal with rapidly changing fields or topics. Many LLMs offer regular updates, with updates to the training data, to help them stay relevant and continue to provide high-quality outputs. 

Model performance 

Your application’s efficiency and speed will depend greatly on the performance and capabilities of the LLM you choose.  

Model performance encompasses effectiveness, accuracy, and efficiency. Numerous benchmarking tests have been developed to help LLM developers and consumers make standardized comparisons of how models perform in certain areas or tasks. Some examples of well-known benchmarks include: 

• Measuring Massive Multitask Language Understanding (MMLU): 16,000 multiple-choice questions covering 57 academic subjects, including medicine, mathematics, philosophy, and law.
• HumanEval: A test with 164 human-written programming problems to evaluate LLMs trained on programming code.
• MATH: A test with 12,500 competition-level mathematics problems. 

When an LLM is released, it is often accompanied by a technical datasheet which shows its performance on various benchmarks. By examining the results from these benchmarks, you can see how models stand up against one another regarding various capabilities. 

Some benchmark tests evaluate speed and efficiency, not just accuracy. Faster models can handle more queries in less time, which is essential for real-time applications. Efficient models make better use of available hardware, reducing operational costs and increasing throughput. 

High-performance models do have their downsides. They require more computational resources to operate, and this can impact your application’s response time and overall throughput. Therefore, your choice of model will require a thoughtful balance between desired performance and available resources.  

Ease of fine-tuning 

If you have a very specific business use case, it may not be enough to adopt a general-purpose LLM and use it as is. You may need to fine-tune your LLM, further adapting and customizing it to your unique needs. If customizability is a high value for you, then choosing an LLM that supports extensive customization will help you ensure that your GenAI application meets your unique needs. 

Fine-tuning allows you to adapt a pre-trained model to specific tasks, improving its performance in your particular context. The availability of pre-trained versions, along with tooling and documentation, facilitates the fine-tuning process so that you can tailor the AI to your particular requirements. Customizations may include:  

• Adjusting the model to better understand industry-specific terminology
• Improving accuracy for specific types of queries
• Enhancing its ability to generate relevant content 

Models with a strong community ecosystem can provide valuable resources and assistance, making it easier for you as you implement customizations effectively. 

Scalability 

Scalability is a key consideration when selecting an LLM, particularly if your product roadmap anticipates significant growth in user demand over time. Some LLMs can more easily be expanded to support larger datasets, more users, or additional features. Picking a model that can scale efficiently can help future-proof your application and maintain performance and reliability. 

How well a model will scale depends on factors such as its architecture, hardware requirements, and support for distributed training and inference. For example, models with efficient architectures and lower memory usage are generally easier to scale across multiple servers or cloud environments. Models that support distributed training can be more effectively scaled to handle large-scale data processing and real-time applications. 

Security, privacy, and ethical considerations 

In GenAI application development, data privacy and protection are an all-important concern. This is especially the case if your application will process sensitive or personal data. Models that more easily support compliance with relevant regulations will simplify your ability to protect your data and maintain user trust. 

Different models have varying levels of support for security features and compliance requirements. For example, some models may include built-in mechanisms for data encryption and secure data handling, while others might require additional layers of security to be implemented. 

Separately, it’s important to become familiar with the training data and methods for the LLMs you are considering. Maintaining the integrity of your AI application also includes addressing potential biases in the model and ensuring ethical use and deployment. Evaluating these aspects helps maintain the integrity and trustworthiness of the AI system. 

Top open source LLMs 

Open source LLMs are developed with publicly available data and research, and anyone is allowed to access, modify, and build upon them. This openness fosters innovation and collaboration within the AI developer community. 

Proprietary models, on the other hand, are developed by private companies and often come with usage restrictions and licensing fees. Customization options may also be limited. Although proprietary models may offer cutting-edge performance and specialized features, open source models typically provide greater flexibility, transparency, and cost-effectiveness. These are what make open source LLMs an attractive option for many organizations. 

Gemma 

Gemma is a family of open models that comes out of Google’s extensive research in AI and natural language processing. It was released in February 2024, and it comes in two sizes: Gemma 2B and Gemma 7B. Alongside the models, Google has released a set of tools to help developers with innovation, collaboration, and responsible use. 

Key features from Gemma include: 

• Responsible Generative AI Toolkit: Provides guidance and tools for creating safer AI applications.
• Multi-framework support: Includes toolchains for inference and supervised fine-tuning using frameworks such as JAX, PyTorch, and TensorFlow.
• Ease of use: Ready-to-use Colab and Kaggle notebooks and integration with tools such as Hugging Face and NVIDIA NeMo.
• Performance optimization: Optimized for use on different AI hardware platforms, including NVIDIA GPUs and Google Cloud TPUs.
• Commercial use: Permits responsible commercial usage and distribution for all organizations, regardless of size. 

Mistral AI 

Mistral AI models are developed by an independent team focused on delivering high-performing models generalized for diverse applications. The main model, Mistral 7B, was released in September 2023. Mistral AI’s models emphasize performance optimization, providing a balance between speed and accuracy. 

Key features from Mistral 7B include: 

• Model size: 7.3 billion parameters, making it a powerful and efficient model.
• 32k context window: Supports extensive context for improved understanding and generation.
• Code and language proficiency: Strong performance in coding tasks and proficiency in English tasks.
• Code proficiency: Strong performance in code-related tasks.
• Fine-tuning capability: Easy to fine-tune on various tasks, including chat.
• Open license: Released under Apache 2.0 license for unrestricted use. 

Llama 3 

Llama 3, developed by Meta and released in April 2024, provides enhanced performance and scalability. It is available in multiple sizes (8B and 70B), supporting a wide range of applications. Llama 3 excels at handling complex tasks like translation and dialog generation, making it a versatile tool for various AI applications. 

Key features from Llama 3 include: 

• Multiple sizes: Available in 8B and 70B parameter versions.
• Improved performance: Significant enhancements over Llama 2, with better reasoning and instruction-following.
• 8K context length: Handles longer sequences more efficiently.
• Advanced architecture: Uses grouped query attention (GQA) for faster inference and improved efficiency.
• Instruction fine-tuning: Enhanced capabilities in chat and reasoning tasks.
• Accompanied by responsible AI tools: Includes Llama Guard 2, Code Shield, and CyberSec Eval 2 for safe and responsible AI deployment.
• Community license: Available under an open-source license that allows usage, adaptation, and building upon the model. Large-scale usage (greater than 700 million monthly active users) requires additional licensing. 

Additional open models to consider 

While Gemma, Mistral 7B, and Llama 3 are prominent open models, there are other strong but lesser-known models that also offer significant capabilities:  

• GPT-NeoX-20B: Developed by EleutherAI, designed for high performance in few-shot reasoning tasks and trained on a 825GiB diverse dataset known as The Pile.
• BLOOM: A 176 billion parameter multilingual language model developed by BigScience, capable of generating text in 46 natural languages and 13 programming languages.
• OpenLLaMA: A model built by OpenLM Research, utilizing efficient attention mechanisms and stable embeddings, in various sizes (3B, 7B, 13B). 

Practical considerations for implementation 

When planning your GenAI application implementation, consider the following practical considerations to ensure a smooth and successful deployment. 

Resource availability 

Evaluate the hardware and infrastructure requirements of your underlying model. LLMs—especially those with large parameter counts—can be resource-intensive. Does your organization have the necessary computational resources? These include: 

• Powerful GPUs or TPUs
• Sufficient memory
• Storage capabilities 

Assessing these needs up front will help avoid performance bottlenecks and ensure efficient operation. 

Integration with existing systems 

When aiming for a seamless deployment, consider a model’s compatibility and ease of integration with your existing systems. Evaluate how well it fits with your current workflows and infrastructure, software platforms, and data pipelines. LLMs often come with APIs, software libraries, and other integration tools—how do these align with your current setup? A smooth integration will minimize disruptions, allowing your organization to leverage the GenAI model more effectively within your established processes. 

Assessing the integration capabilities of different models is crucial for ensuring they fit well within your existing ecosystem. Robust APIs and integration tools can significantly reduce the effort, time, and financial cost needed for deployment. 

Cost and ROI 

The more powerful models come with higher operational costs, as they require more computational resources. Some might even need specialized hardware. There’s no point in trying to build an application on bleeding-edge LLM tech if your application isn’t financially viable.  

A model’s scaling and customizability also have implications on cost. Some models may offer better economies of scale, reducing the cost per unit of performance as deployment size increases. Evaluate the cost structure of different models to make an informed decision that aligns with your budget constraints while maximizing the ROI of your GenAI application. 

Supporting your chosen LLM may potentially require additional expenses such as hardware procurement, training, maintenance, and licensing. Consider these expenses and weigh them against your application’s expected return on investment. 

Identify the benefits that an application built on your chosen model will bring, such as: 

• Improved efficiency
• Enhanced capabilities
• Potential revenue growth 

At the same time, calculate the total cost of ownership for this implementation. Understanding the financial impact will help justify the investment and guide budget allocation decisions.

Choosing the right LLM  

The choice of LLM for your GenAI application is a big one. Your project’s implementation and success will depend heavily on this decision. By understanding the key considerations that go into that decision, you are well-equipped to make an informed choice. Ultimately, you want to choose the LLM that best aligns with your organization’s needs and capabilities. 

A thoughtful and thorough evaluation of these factors will help you choose the best LLM for your GenAI journey.

Ready to take a technical deep dive? Learn the benefits of combining OpenWebUI, a feature-rich, open source LLM and GraphRAG, a hybrid AI advancement of retrieval-augmented generation (RAG).