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Prompt

Haha. Let's get back to serious stuff. I'm actually very curious about the idea of building a local LLM. It's way out of my budget. But are there businesses who have built systems of this nature? The dimensions you provided aren't as big as I would have imagined

Output

Yes, there are businesses and organizations that have built systems to run local LLMs, though it's typically reserved for large enterprises, research institutions, or tech companies with the resources to manage such hardware and expertise. These systems are becoming more common, especially in industries where control over data privacy, performance, or customization is a priority. Let's dive into examples and insights into businesses or industries building these systems.

1. Tech Companies and Startups

Some companies, particularly in the tech and AI sectors, develop and deploy local LLM systems for specific purposes. These companies often choose to host models locally to maintain full control over data security and model customization, and to avoid reliance on third-party cloud providers like OpenAI or Google.

  • AI Startups: Certain startups focused on natural language processing (NLP) and AI services build custom LLM systems for their products. For example, companies specializing in AI-based customer service or advanced analytics may deploy local models to maintain high levels of performance and data control.
  • AI Infrastructure Providers: Companies like NVIDIA (with their DGX systems) are directly involved in selling hardware for such setups. They market their DGX Stations to companies and research institutions needing to run large AI models in-house.

2. Research Institutions and Universities

Many universities and research institutions develop local LLM systems for research purposes, often focusing on topics like AI ethics, model transparency, or domain-specific customization (e.g., medical or scientific fields). These organizations often have access to large-scale grant funding to cover hardware and operational costs.

  • Case Example: Meta's AI Research Division has released models like LLaMA to the research community, knowing that institutions have the means to run these locally. Universities often leverage GPU clusters for research and experimentation.

3. Enterprises in Regulated Industries

Organizations in highly regulated industries like finance, healthcare, and government often build local AI systems because they cannot afford to share sensitive data with third-party cloud providers.

  • Healthcare: Hospitals and medical research institutions might run local models to ensure patient data privacy is maintained while benefiting from the advancements of LLMs in medical diagnostics or natural language understanding.
  • Finance: Financial institutions may build and deploy in-house LLMs for use in areas like fraud detection, automated reporting, and market trend analysis, avoiding cloud-based services due to strict regulations on financial data security.

4. Private AI Research Labs

Some businesses exist solely to advance AI and machine learning research. For these companies, building their own local systems to run large models is necessary to maintain full control over their tools and to experiment freely with model architectures.

  • OpenAI (before becoming cloud-based) and DeepMind (owned by Google) originally built and maintained local systems for running advanced AI models.

Reasons for Building Local LLM Systems

Many businesses choose to build local LLM systems for reasons beyond financial capability:

  • Data Privacy: Businesses want to ensure that sensitive or proprietary data never leaves their systems. By running models locally, they maintain control over who accesses the data.
  • Customization: Local systems allow businesses to fine-tune models according to their specific needs, ensuring that the model is optimized for their domain or industry.
  • Latency: Running LLMs locally eliminates the need to communicate with external cloud services, reducing latency and providing faster response times, which can be critical for real-time applications.
  • Cost in the Long Term: While building a local system is expensive upfront, for businesses that have heavy, sustained AI workloads, running models in the cloud can become even more costly in the long term. Enterprises with constant AI processing needs often find local systems more cost-effective over time.

While local LLM setups are currently quite expensive and resource-intensive, the cost and size of such setups are decreasing as AI hardware evolves. With increased GPU performance and more efficient models being developed (e.g., LLaMA and Falcon models), it's becoming more feasible for mid-sized businesses to consider local AI infrastructure in the near future.

  • Custom LLMs for Local Communities: As the demand for localized, private AI solutions grows, smaller companies may start offering services that are domain-specific or region-specific, such as local language models for small markets or niche industries.
  • Edge AI: There’s also a growing movement toward edge AI, where smaller, optimized models are run on-premise but with lower hardware requirements than a full-scale LLM. These models can be tailored to specific business needs while keeping operational costs down.

In short, local LLM systems aren’t just for the big players, and as technology improves, they’re becoming more accessible. For now, businesses in regulated or data-sensitive industries often make the investment to gain the advantages of data privacy, customization, and long-term cost efficiency.

Let me know if you’d like to explore the cloud-based hybrid approach as a next step, or just keep diving into the hardware specs!

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.