We’ve been discussing the progress of AGI for quite a long time now. However we are almost forgetting the dark knight which actually makes a difference. In terms of our daily workflow, generating results and productivity, choosing the right LLM has become crucial right?
Think about it after the launch of ChatGPT users unequivocally accepted it to be the biggest player in the AI landscape. Why wouldn’t you? It took Open AI’s ChatGPT 5 days to reach 1 million users worldwide.
Recorded in Nov 2024 ChatGPT attracted approximately 464 million users each month. The large language model (LLMs) definitely has changed the way we imagined AI will disrupt our working approach.
However today there are multiple LLMs available for you to choose. In this guide we will learn more about different types of LLMs, choosing the right one for your workflow, and best practices. Read along!
Understanding Different Types of LLMs
For those of you not familiar with the term; LLM in AI means a type of artificial intelligence and it’s closely associated with Gen AI. The AI works on deep learning techniques, processing data, analyzing patterns, and providing results using high level computational power.
According to Hugging face LLM benchmark ranking there are more than 2500 LLMs in the market. The number keeps on updating too. Sure well known types of LLMs have already made their mark but you will be surprised to know that many people are unaware of ChatGPT alternatives.
The sudden burst of these large language models comes after countries directed their investment towards AI hardware production.
Now there might be new LLMs being introduced to the market. Countless businesses are adapting multi model LLM strategy to build an AI ready workforce. But let’s divide them into two groups. It will simplify your approach into choosing the right LLM for you. The two groups are Open-source vs. proprietary models.
Open-source vs. proprietary models
ELI5 Corner:-
- Imagine you’re cooking a meal. With a proprietary recipe (like KFC’s secret blend), you get a proven, ready-to-use formula but can’t modify or share it.
- In contrast, an open-source recipe (like your grandmother’s published cookbook) allows you to not only use it but also adapt it, improve it, and share your modifications with others.
- Similarly, in the AI world, this distinction between open-source and proprietary models shapes how we can use, modify, and build upon existing AI technologies.
Example:-
General-purpose LLMs vs. specialized models
The primary difference between these two types of LLM is easy to understand. One is a titan of the industry and the counterpart is a master craftsman. Choosing between the two will depend on various factors. Do remember general purpose LLMs stride confidently across vast domains, while specialized dance precisely within carefully trained boundaries.
Key Factors in LLM Selection
Before choosing the right LLM, considering key factors is important. If you are a sole user you can just shift to a different subscription tier or even to a different model. But on the other hand if you are running a business neglecting these factors troubles you later. New BCG research finds that 74% of companies struggle with reaping the benefits of their AI investments.
Performance Metrics
- Accuracy and reliability: Accuracy here refers to generating relevant responses. A higher accuracy means LLM is able to provide meaningful outputs. Reliability on the other hand refers to the consistency in mass producing results with source backed datasets.
- For example: During a marketing campaign you need factually accurate data. If the model lacks accuracy your generated campaign will not perform as you expected.
- Repercussions: Overlooking both accuracy and reliability leads to misleading information. So make sure you have been tracking the accuracy of your options while choosing the right LLM.
- Processing speed: The speed at which responses are generated from LLMs. Particularly important for real-time interactions.
- For example: A chatbot interacting with potential buyers on your ecommerce platform.
- Repercussions: Slow interactivity leads to frustration, lost sales opportunities, and poor user experience.
- Resource requirements: Revolves around the computational resources. Different models have varying demands. Assessing requirements is the right approach to carry out robust implementation of LLMs.
- For example: A law firm wants to introduce an enterprise grade local LLM-based AI solution for automating their documentation workflow.
- Repercussions: Without assessing proper requirements while choosing the right LLM, even simple tasks such as document analysis may consume excessive power. It might lead to frequent crashes and slow processing power.
- Cost considerations: It may contain ongoing licensing fees, third party fees, energy consumption, and expenses related to model scaling with the organization.
- For example: Many companies shifted to hybrid cloud architecture for AI advancement and accounting the cost thoroughly. It gave them the ability to run ahead with full speed without worrying about the high cost environment.
- Repercussions: Do remember while the model may offer superior performance, the high licensing fees, increased cloud computing costs, and expenses for necessary infrastructure upgrades quickly escalate.
Organizations are sure to accelerate AI adoption into their team, but without a clear picture. The question of what is LLM in Generative AI has not piqued their interest and they don’t understand the benefits of effective prompting strategies. You might be surprised that only 25% of companies are offering Gen AI training to their employees. Which later leads to futile efforts.
Best Practical Tips for Consideration
Every leader wants to implement an AI model ensuring efficiency and long-term sustainability. Hence these considerations play an important role while choosing the right LLM (Large Language Model) for your organization.
- Licensing and usage rights: learn about the licensing and usage rights. For enterprise grade implementation make sure LLM providers can legally use and distribute the AI model according to your needs.
- Privacy and security features: Ask for data privacy and protection regulations. Find out whether or not the LLM has a safeguarding feature against breaches and unauthorized access.
- Support and documentation: Inquire about the LLMs provides comprehensive support and documentation. Ignore these and you will face challenges in troubleshooting major errors.
- Community engagement: Active community engagement provides valuable insights about different types of LLM. Find a good community and be an active member. It will increase your innovation capabilities, and allow you to explore critical updates.
Most popular LLMs follow a structured approach to provide their user with above mentioned information. The approach came into light after a sudden surge of ethical misuse with AI. AI incidents have increased by over 30% from 2022, reaching 123 ethical violations in 2023, according to the AI Incident Database (AIID).
A Simple Selection Framework for Choosing the Right LLM
Before studying the framework let me help you understand how to effectively implement it. A framework is important in building an AI ready workforce.
- Start with the Assessment Phase; gathering relevant information about your daily workflow, requirements, challenges, and areas of improvements.
- Utilize a Decision Matrix to evaluate your specific requirements in relation to both general-purpose and specialized LLMs.
- Follow the Selection Guidelines for first phase decision making in choosing the right LLM.
- Employ the Implementation Roadmap to strategize your deployment.
- Monitor Results and Impacts using provide metrics.
To implement the framework correctly just pick a popular LLM model. If you are confused below we’ve provided information about what are the most popular LLM models.
Most Popular LLM Model
Remember choosing the right LLM does not mean filtering your choice based on popularity. However the list does provide you with a starting point to begin your search. Each has its own strengths and weaknesses so choose wisely.
| Model | Parameters | Pricing | Key Capabilities |
| GPT-4 | 1.76T (estimated) | Starting $0.03/1K tokens | Strongest general reasoning, coding, and creative tasks |
| Gemini Ultra | ~1.5T (estimated) | $0.01/1K tokens | Multimodal processing, strong coding, mathematical reasoning |
| Gemma | 7B – 8B | Free, open source | Good for deployment on consumer hardware, efficient inference |
| Llama 2 | 7B – 70B | Free, open source | Strong performance/size ratio, good for fine-tuning |
| Claude 3 | Not disclosed | $0.015/1K tokens (Sonnet) | Strong reasoning, analysis, and coding capabilities |
| Command | ~7B | Research only | Specialized in instruction following and coding |
| Falcon | 7B – 180B | Free, open source | Good multilingual support, efficient training |
| DBRX | ~7B | Research only | Optimized for dialogue and conversational tasks |
| Mixtral 8x7B | 47B effective | Free, open source | Strong performance across tasks, efficient MoE architecture |
| Phi-3 | ~3.8B | Free, open source | Compact but powerful, good for resource-constrained settings |
| Grok | ~314B (estimated) | Subscription based | Real-time data access, conversational abilities |
Table 0.1.0
As you can see, diverse types of LLMs have gained popularity since the introduction of Gen AI to the market. Carefully consider their price plans, features, ease of use, and additional relevant factors before making your choice.
If you want to discuss more about implementing LLM into your workspace, here is the r/subreddit to keep yourself updated
Find Your Preferred Gen AI Model in Weam AI
Facing difficulty in choosing the right LLM, use Weam AI, made for the ones who like the multimodal approach. It has multiple different types of LLMs, and exciting features waiting for you to test your Gen AI skills. The platform helps your team accelerate productivity, save cost on subscription based Gen AIs, & enable team collaboration.
Using the AIaaS product is super easy and it can effortlessly integrate with your team’s daily workflow. Need to adopt AI faster to be better? Learn all about Weam; and our aim in helping you adopt AI for scalable growth. Stay informed, make better decisions, and keep innovating.
Ready to Choose the Right LLM for You?
People always discuss how implementing AI into your workflow will help you increase productivity astonishingly. Your habit of saving time for performing a task will also change. The result will drastically improve and you will start focusing on progressive strategies and methodologies to implement.
However a lot of experts have pointed out users, organizations, and companies unaware of proper understanding of Gen AI platforms. Choosing the right LLM is not complex but when you feel overwhelmed by the mounting options, Weam AI can be your go to solution and you can Start for Free!
Remember making a decision towards better scaling and growth of your business needs thorough evaluation. Evaluation of parameters, capabilities of LLMs, and understanding your skills to be proficient with those Gen AI models.
FAQ
What factors should I consider when selecting an LLM for my application?
Think about your specific needs: task requirements, budget, and technical capabilities. Consider factors like accuracy needs, processing speed, and whether you need specialized features like code generation or multilingual support.
How can I determine the performance of different LLMs?
Test the models with your specific use cases. Compare accuracy, response time, and consistency. Create a small test set of typical tasks and evaluate how each model handles them.
Are open-source LLMs options available for LLMs?
Yes! Popular options include Llama 2, Mistral, and Falcon. They’re free to use but remember you’ll need to handle hosting and maintenance costs yourself.
What is the importance of the model’s knowledge cutoff?
It’s the last date of the model’s training data. Important for tasks requiring current information. Less critical for historical or fundamental topics.
How do I assess the cost-effectiveness of an LLM?
Calculate total costs including API fees, infrastructure, and maintenance. Compare against performance benefits. Consider your usage volume and specific requirements.
