Large language models (LLMs) are pre-trained on vast datasets and utilize natural language processing (NLP) to perform various linguistic tasks such as text generation, code completion, paraphrasing, and more.
Capabilities of Large Language Models
- Text Generation: LLMs can produce coherent and contextually relevant text on a given topic, which is useful for applications ranging from article writing to chatbot interactions.
- Code Completion: They can assist in software development by automatically completing code or suggesting improvements, enhancing developer productivity.
- Paraphrasing: LLMs can rephrase text, summarize content, or provide explanations, making information more accessible.
Rapid Adoption of Generative AI
The first version of ChatGPT significantly contributed to the innovations in large language models and the rapid adoption of generative AI. These advancements have driven the overall growth of the industry. Generative AI has been swiftly integrated across various sectors, leading to more efficient business processes.
Use of Generative AI by Fortune 500 Companies
92% of Fortune 500 companies have started incorporating generative AI into their workflows. This adoption has led to increased operational efficiency, improved customer service, and accelerated innovation processes within these organizations.
Growth Projections for the Large Language Model Market
As the adoption of generative AI continues to rise, the LLM industry is expected to grow rapidly. The global market for large language models is projected to increase from $6.5 billion in 2024 to $140.8 billion by 2033. This growth reflects the expanding potential and application areas of these technologies.
With that, here is a list of the top 20 LLMs available in June 2024.
| LLM Name | Developer | Release Date | Access | Parameters |
| GPT-4o | OpenAI | May 13, 2024 | API | 1.76 trillion (George Hotz) - appr 280 billion active |
| Claude 3 | Anthropic | March 14, 2024 | API | over 137 billion |
| Grok-1 | xAI | November 4, 2023 | Open-Source | 314 billion |
| Mistral 7B | Mistral AI | September 27, 2023 | Open-Source | 7.3 billion |
| PaLM 2 | May 10, 2023 | Open-Source | 340 billion | |
| Falcon 180B | Technology Innovation Institute | September 6, 2023 | Open-Source | 180 billion |
| Stable LM 2 | Stability AI | January 19, 2024 | Open-Source | 1.6 billion, 12 billion |
| Gemini 1.5 | Google DeepMind | February 2nd, 2024 | API | Unknown |
| Llama 3 | Meta AI | April 18, 2024 | Open-Source | 8 billion, 70 billion |
| Mixtral 8x22B | Mistral AI | April 10, 2024 | Open-Source | 141 billion |
| Inflection-2.5 | Inflection AI | March 10, 2024 | Proprietary | Unknown |
| Jamba | AI21 Labs | March 29, 2024 | Open-Source | 52 billion |
| Command R | Cohere | March 11, 2024 | Both | 35 billion |
| Gemma | Google DeepMind | February 21, 2024 | Open-Source | 2 billion, 7 billion |
| Phi-3 | Microsoft | April 23, 2024 | Both | 3.8 billion |
| XGen-7B | Salesforce | July 3, 2023 | Open-Source | 7 billion |
| DBRX | Databricks’ Mosaic ML | March 27, 2024 | Open-Source | 132 billion |
| Pythia | EleutherAI | February 13, 2023 | Open-Source | 70 million to 12 billion |
| Sora | OpenAI | February 15, 2024 (announced) | API | Unknown |
| Alpaca 7B | Stanford CRFM | March 13, 2023 | Open-Source | 7 billion |
See working, with sources.
Source: Dr Alan D. Thomson
Requirements for Running Advanced Language Models
To run advanced language models, you’ll need adequate disk space to save them and sufficient RAM to load them. Currently, the memory and disk requirements are the same. Here are the requirements for various models:
| Model | Original Size |
Quantized Size (4-bit) |
|---|---|---|
| 7B | 13 GB | 3.9 GB |
| 13B | 24 GB | 7.8 GB |
| 30B | 60 GB | 19.5 GB |
| 65B | 120 GB | 38.5 GB |
Running Models on CPU and GPU
- LLaMA-65B: This model can run on a CPU with 128GB of RAM. However, it may not be efficient compared to using data center GPUs.
- GPT-4: This model is significantly larger than LLaMA-65B and supports a 32K context window. Running GPT-4 inference requires a substantial amount of fast GPU memory and is not feasible on consumer hardware. The inference cost also increases quadratically with the input sequence length, making it more demanding.
Conclusion
Large language models and generative AI continue to revolutionize various sectors with their natural language processing capabilities. Innovations like ChatGPT have facilitated the rapid adoption of these technologies, contributing to the industry’s overall growth. The widespread use of these technologies by a significant portion of Fortune 500 companies highlights their importance and impact in the business world. The market for large language models is expected to grow significantly in the coming years, underscoring the vast potential of this technology.
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