The HARD Truth About Hosting Your Own LLMs

Summary

This video discusses the rising trend of running local large language models (LLMs) to gain flexibility, privacy, and cost efficiency in scaling AI applications. While hosting your own LLMs avoids paying per token, it requires powerful hardware and high upfront costs. The presenter introduces a hybrid strategy: start by using affordable pay-per-token services like Grok, then transition to hosting your own models when it becomes more cost-effective. The video outlines how to integrate Grok easily, highlights its speed and pricing, and provides a formula to determine the optimal time to switch to self-hosting.

Takeaways

• 💻 Running your own large language models (LLMs) locally is gaining popularity, offering advantages like no per-token costs and keeping your data private.
• 🔋 Running powerful LLMs locally, like Llama 3.1, requires extremely powerful and expensive hardware, often costing at least $2,000 for GPUs.
• ⚡ Local LLMs are efficient when scaling your business, but upfront costs and high electricity expenses make initial setup very expensive.
• 🚀 An alternative to running local LLMs is to pay per token using cloud services like Gro, which offer much cheaper and faster AI inference without hardware costs.
• 🛠️ Using Gro allows you to pay per token for LLMs like Llama 3.1 70B, and the platform is easy to integrate into existing systems with minimal code changes.
• 🌐 Gro is not fully private since the company hosts the model, so for highly sensitive data, users should consider moving to self-hosting as they scale.
• 💡 The strategy outlined suggests starting with Gro’s pay-per-token model, then transitioning to local hosting when scaling to save on long-term costs.
• 💰 Gro offers highly competitive pricing, charging around 59 cents per million tokens, making it affordable compared to other closed-source models.
• 📊 A cost-benefit analysis shows that once a business reaches a certain number of prompts per day (around 3,000), it becomes more cost-effective to self-host LLMs.
• 🌥️ Hosting your own LLM in the cloud using services like Runpod is recommended for flexibility, but comes at a recurring cost (about $280 per month for certain GPUs).

Q & A

What are the main advantages of running your own local large language models (LLMs)?

The main advantages include increased flexibility, better privacy, no need to pay per token, and the ability to scale without sending data to external companies. Local LLMs allow businesses to keep their data protected and potentially lower costs as they scale.

What are the challenges associated with running powerful LLMs locally?

Running powerful LLMs locally requires expensive hardware, such as GPUs that cost at least $2,000, and the electricity costs can be high when running them 24/7. Additionally, setting up and maintaining the models can be time-consuming and complex.

How does the cost of running local LLMs compare to cloud-hosted models?

Running LLMs locally requires a significant upfront investment in hardware and ongoing electricity costs. On the other hand, using cloud-hosted GPU machines can cost more than $1 per hour, which adds up quickly. However, local LLMs become more cost-effective once a business scales.

What is Grock, and why is it recommended in the script?

Grock is an AI service that allows users to pay per token for LLM inference at a very low cost, sometimes even free with their light usage tier. It offers speed and affordability, making it a great option for businesses before they scale to the point where hosting their own models is more cost-effective.

What is the suggested strategy for businesses wanting to use LLMs without a large upfront investment?

The suggested strategy is to start by paying per token with services like Grock, which is affordable and easy to integrate. Then, once the business scales to a point where paying per token becomes expensive, they can switch to hosting their own LLMs locally.

When does it make sense to switch from paying per token to hosting your own LLMs?

The decision to switch depends on the scale of the business and the number of LLM prompts per day. For example, if you reach a point where you're generating around 3,000 prompts per day, it becomes more cost-effective to host your LLM locally rather than paying per token with a service like Grock.

What are the hardware requirements for running Llama 3.1 70B locally?

To run Llama 3.1 70B locally, you need powerful hardware such as a GPU with at least 48GB of VRAM, like an A40 instance, which costs around 39 cents per hour in the cloud.

How easy is it to integrate Grock into existing AI workflows?

Integrating Grock into existing AI workflows is simple. Users only need to change the base URL in their OpenAI instance to Grock's API and add their API key. For LangChain users, it's even easier, with a pre-built package for Grock integration.

What are some of the concerns when using Grock for sensitive data?

While Grock offers better data privacy compared to proprietary models like GPT or Claude, it is still a hosted service. Therefore, it is recommended to use mock data when developing applications that handle highly sensitive information. Full data privacy is only guaranteed once the LLMs are hosted locally.

What are the potential long-term benefits of switching to local LLM hosting?

Once a business scales and begins generating a large number of prompts, hosting LLMs locally can save thousands of dollars compared to paying per token. Additionally, businesses gain full control over their data and can avoid reliance on external services.