Bringing Generative AI Within Reach: How xMAD.ai Lowers Barriers to LLM Innovation

Large Language Models: Vast Potential with Lingering Problems

Large Language Models have become an integral tool for our daily lives. From drafting emails to summarizing documents, how individuals utilize ChatGPT, Gemini, and other LLMs may vary, but its usefulness and versatility is undisputed.

Now as open source models have reached an inflection point in quality, we are at a new stage of innovation and widespread deployment. With models such as Llama 3.2 achieving the sophistication of closed source premium models such as GPTo1, customizing and running LLMs locally no longer requires a sacrifice in LLM quality.

All of these developments are incredibly exciting, but a few obstacles for LLM deployment remain and have only been magnified as the utility and potential of LLMs continues to grow.

The Ever Increasing Hardware Requirements for LLMs

The first problem is clear: LLMs are massive. The increased sophistication of LLMs corresponds with ever increasing hardware requirements, meaning:

• Most individuals and even many companies lack the prerequisite hardware to use the latest LLMs
• The deck is stacked against independent developers and small labs

Hardware requirements are now reaching unprecedented levels – even the megacorporations like Amazon, Google, Microsoft, and Meta have reported unprecedented levels of capital expenditures on Nvidia chips and other AI hardware. [1] [2]

The Technological Barrier to Entry for LLM Deployment

For small businesses and curious developers there is perhaps a larger problem: LLMs are complex. It is difficult to:

• Understand how to operate LLMs properly without hours of practice and a dedicated guide
• Use the myriad of tools integrated into platforms such as HuggingFace, which is out of reach for everyone except for the most enthusiastic developers

The Need for Simple Local LLMs

For developers and enterprises wishing to utilize LLMs for more than simple daily tasks, the next step is to begin deploying LLMs on their local hardware.

Cloud services were great for testing the capabilities of LLMs for the first time and for individual tasks such as drafting emails, but obvious problems emerge when attempting to run larger operations:

• Companies and developers are unable to use their existing hardware and are instead forced to pay for a separate cloud service based on their LLM usage
• Cloud services often restrict the customization options for LLMs
• Industries which require guaranteed privacy for their data, such as medical, legal, and government, are entirely unable to use cloud services for LLM tasks

xMAD.ai’s Solution: Local LLMs created in 3 clicks

To solve these persistent problems, xMAD.ai has developed a tool to deploy your first LLM locally in just 3 clicks.

• Create your own custom model and deploy without writing a line of code
• Simple, intuitive interface
• No need for extensive technical and coding experience
• Requires only minimal hardware

Below is a comparison of hardware requirements of leading baseline models before and after xMAD.ai.

The models in green are open sourced and accessible on Huggingface. They can be deployed into existing workflows with one line of code – we can’t wait to see what you build!

Additionally, if you would like access to more advanced models or self-serve fine-tuning, join our beta at this link.

xMAD.ai Team

Citations

[1] Gallagher, D. (2024, Aug 2). Big Tech’s AI Race Has One Main Winner: Nvidia. The Wall Street Journal. https://www.wsj.com/business/telecom/amazon-apple-earnings-63314b6c?mod=article_inline

[2] Gallagher, D. (2024, Oct 6). Microsoft’s AI Story Is Getting Complicated. The Wall Street Journal. https://www.wsj.com/tech/ai/microsofts-ai-story-is-getting-complicated-ebe63ac9?mod=Searchresults_pos5&page=1