Competitive Differentiation in the LLM: A Deep Dive
Learn how foundation models can stand out in the crowded LLM landscape. Explore key strategies for competitive differentiation and achieve market dominance.
Compute Performance, Safety and Alignment, Accuracy and Retrieval Augmented Generation are Three Emerging Differentiation Vectors
Machine Learning foundation models are a new category that has largely been undifferentiated, with major providers competing on similar types of customer benefits. Over the past year, development focus has centered on model attributes such as context length, rate of hallucinations, and the size of the training data.
Simultaneously, both mature and newly established AI companies have been developing their own foundation models and associated development platforms, making them available to third parties to power their AI applications. As the segment grows in size and maturity, different types of benefits are emerging as competitive differentiation for the increasing variety of licensable, commercially available foundation models.
Before we look at the market, here are the main product characteristics that can be used as vectors for competitive differentiation. They include benefits related to:
The types of customer benefits listed above are present across the segment in various combinations. In the past few months, foundation model providers have started to develop competitive differentiation across three vectors: compute performance, safety and alignment, and RAG. Here is what the categories look like for LLMs:
Compute Performance - Mistral, Nvidia
Launched in September 2023, Mistral’s open-source 7B model has been developed to be “compute efficient, helpful, and trustworthy” and claims to outperform larger models such as LLaMA 2 13B and LLaMA 2 34B. Measured on benchmarks for commonsense and STEM reasoning, the 7B parameter model holds its own and pushes performance barriers for open-source, smaller LLMs. With support for English and code and an 8k token context length, you can?download?Mistral 7B on the developer’s website. You can also access and deploy it through?Amazon Bedrock, Microsoft’s?Azure AI Studio, Google’s?Vertex AI, and?Hugging Face.
Based in Paris, France, Mistral’s mission is to provide “Frontier AI in your hands” and its roadmap includes more models with larger sizes, better reasoning, and multiple language support.
Launched in November 2023, Nvidia AI’s family of LLMs called?Nemotron-3 8B?has also been developed with compute performance improvements in mind. Built to integrate seamlessly with the NVIDIA TensorRT-LLM open-source library and the NeMo deployment framework, the models are designed to enable cutting-edge accuracy, low latency, and high throughput. Available through the Azure AI Studio and Hugging Face, the?Nemotron 3 catalog?includes a base model, three versions for chatbots, and one version for Q&A applications.
Nvidia will likely continue to develop its foundation models and development frameworks to achieve superior training and inference performance, especially when paired with its GPU infrastructure and hardware acceleration frameworks.
Safety & Alignment - Inflection, Anthropic
Last week, Inflection?announced?that they have completed the training of version two of their foundation model. With improvements to its factual knowledge and reasoning capabilities, Inflection 2 will power the PI assistant and will be available for third-party applications through the?Conversational API, once it becomes generally available. With a mission to provide supportive and empathetic personal intelligence for everyone, Inflection is investing heavily in AI safety and alignment and has been developing extensive policies and principles to safeguard the impact of the technology on human beings.
Also last week, Anthropic?launched?the Claude 2.1 API through the developer console and deployed it as the engine behind the free and paid versions of the Claude chatbot. With a large context window of 200k tokens, a 2x reduction in the hallucination rate, and new features such as system prompts and tools (in beta), the new version is part of Anthropic’s mission to provide “AI research and products that put safety at the frontier.” You can access the Claude API through Anthropic’s developer console and through Amazon Bedrock.
Accuracy & Retrieval Augmented Generation - Cohere, AI21Studio, Amazon Titan Embedding
Cohere?Coral?is a customizable knowledge assistant built on top of the company’s Command foundation model for the creation of RAG applications. Able to connect with a company’s data sources, Coral is optimized for document Q&A prompts that generate responses verifiable with citations, to mitigate hallucinations. Alongside Coral, Cohere provides?Embed, a text representation language model that generates embeddings and can be deployed alongside Command to improve the performance of RAG applications.
Last week, Cohere?published?an LLM university course on how to build a RAG-Powered Chatbot. You can access the foundation models on the company’s website and through Amazon Bedrock, with Azure AI Studio support?coming soon.
AI21Studio is offering a?Contextual Answers API, which accesses a “powerful question answering engine [...] with AI-generated answers that are 100% based on your company’s proprietary data.” The tool, which runs on top of the company’s?Jurassic-2foundation models, is designed to generate grounded, truthful, and correct answers. You can find AI21Studio’s products on their website and on the Google Cloud Marketplace, Amazon Bedrock, and?Dataiku.
Another model that can be used to improve accuracy and build RAG applications is Amazon Titan Embeddings, which is available through Amazon Bedrock.
Besides the categories and models mentioned above, there is the General Purpose segment, made up of products such as the GPT family from OpenAI, the Llama family from Meta,?PaLM from Google, and?Luminous?from Aleph Alpha.
General Purpose Segment - OpenAI, Meta, Google, Aleph Alpha
The General Purpose segment includes widely recognized models like OpenAI's GPT family, Meta's LLaMA family, Google's PaLM, and Aleph Alpha's Luminous. These models are designed to be versatile and robust, catering to a broad range of applications across various industries.
OpenAI's GPT Family
OpenAI's GPT family, particularly GPT-4, is renowned for its broad applicability and advanced capabilities in natural language understanding and generation. GPT models are integrated into numerous applications, from chatbots to creative content generation, and are accessible via the OpenAI API and platforms like Microsoft Azure.
Meta's LLaMA Family
Meta's LLaMA models are another major player in the general-purpose AI model space. These models are optimized for scalability and performance across diverse tasks, providing strong competition in the open-source AI community. LLaMA models are available through Meta's research platforms and other AI development environments.
Google's PaLM
Google's PaLM (Pathways Language Model) is part of their broader AI ecosystem, designed for high-performance language understanding and generation tasks. PaLM integrates seamlessly with Google's cloud services, offering extensive capabilities for developers looking to build advanced AI applications.
Aleph Alpha's Luminous
Aleph Alpha's Luminous models stand out with their focus on multilingual capabilities and deep contextual understanding. These models are designed to deliver high accuracy and reliability across various languages and domains, making them a strong choice for global AI applications.
Conclusion
The landscape of foundation models in the LLM space is evolving rapidly, with providers differentiating their offerings based on compute performance, safety and alignment, and accuracy with retrieval-augmented generation. As the market grows, we can expect to see further innovations and specializations, providing users with a diverse array of powerful AI tools to meet their specific needs.
Have you seen other foundation models that differentiate in interesting ways? If so, please share your insights and experiences with us! https://go.tenten.co/aisub