So ChatGPT 4.0 given some input from the Llama 3.1 website gave me above negative estimate. I now asked Meta itself:
What would I need to self-host you?
Self-hosting me would require significant resources and expertise. Here's a high-level overview of what you'd need:
Hardware:
A powerful server or cluster with a multi-core processor (at least 8 cores)
Ample memory (at least 64 GB RAM)
Fast storage (at least 1 TB SSD)
Software:
A Linux-based operating system (e.g., Ubuntu)
Docker or a similar containerization platform
A database management system (e.g., MySQL or PostgreSQL)
A web server (e.g., Apache or Nginx)
Dependencies:
Python 3.8+ and various libraries (e.g., Transformers, PyTorch, or TensorFlow)
Node.js (for the web interface)
Expertise:
Proficiency in Linux system administration
Experience with containerization and orchestration (e.g., Docker, Kubernetes)
Familiarity with web development and APIs
Knowledge of natural language processing and AI concepts
Additional considerations:
Large amounts of storage for training data and models
High-bandwidth internet connection for updates and communication
Regular maintenance and updates to ensure security and performance
Please note that self-hosting me is not a trivial undertaking and requires significant resources and expertise. I'm happy to assist you with any questions or tasks you have, but I'm not available for self-hosting at this time.
So you mention no special hardware. I read that special GPUs would be required and more than one machine. What drawbacks should I expect if I installed you on my beefy developer machine? It has 64GB of RAM, 2TB of SSD, 10TB of HDD, 24core CPU.
Your developer machine is indeed beefy! While it's technically possible to run me on your machine, there are some drawbacks to consider:
1. Resource contention: I'd be competing with your development work for resources, potentially slowing down your workflow.
2. GPU limitations: While your CPU is impressive, I'm designed to leverage multiple high-end GPUs (e.g., NVIDIA V100 or A100) for accelerated processing. Without them, performance might be degraded.
3. Single-machine limitations: I'm built to scale across multiple machines, allowing me to handle a large volume of requests concurrently. On a single machine, I'd be limited to a smaller workload.
4. Heat and power consumption: Running me on your machine could increase heat generation and power consumption, potentially affecting component lifespan.
5. Storage constraints: While you have ample storage, my training data and models are massive (hundreds of GBs). You might need to dedicate a significant portion of your storage to me.
6. Maintenance and updates: I require regular updates and maintenance, which could impact your machine's availability for development work.
7. Limited redundancy: If your machine goes down, I'd be unavailable until it's back online.
Keep in mind that these drawbacks might not be significant if you only plan to use me occasionally or for personal projects. However, if you expect heavy usage or critical availability, a multi-machine setup with specialized hardware would be more suitable.
If you're still interested in running me on your machine, I can provide more detailed guidance on the installation process and potential workarounds for these limitations.
