vast.ai info
One of the biggest advantages of Vast.ai is that it gives you access to GPU resources that are simply not realistic for the average home PC.
### Some examples
* **RTX 3060 (12 GB VRAM)** – Training smaller models and LoRA fine-tuning.
* **RTX 4090 (24 GB VRAM)** – Fine-tuning 7B–14B models and serious AI development.
* **A100 (80 GB VRAM)** – Training and inference for large models.
* **H100 (80–94 GB VRAM)** – Currently one of the top-tier GPUs for AI training.
### What can you do with a home PC?
For example:
* **RTX 3060 or RTX 4060** → Run 7B–8B models and perform smaller fine-tuning tasks.
* **RTX 4090** → Excellent for advanced AI development, but still not enough to train a full Llama 70B model from scratch.
### What is Vast.ai good for?
With Vast.ai, you can rent configurations such as:
* 2× RTX 4090
* 4× RTX 4090
* 8× A100
* Even 8× H100
Buying hardware like this yourself would cost tens or even hundreds of thousands of dollars, making it impractical for most individual developers and small teams.
## An important distinction: Training vs. Fine-tuning
When people say they’re “training Llama,” they often mean one of two very different things:
### Fine-tuning
Fine-tuning means taking an existing model (for example, Llama 3.1 8B) and adapting it to your own data. This can often be done on a single RTX 4090 or on a rented GPU through Vast.ai.
### Pretraining (training from scratch)
Pretraining means building a completely new Llama-class model by training it on trillions of tokens. This requires an enormous amount of compute—typically hundreds or thousands of GPUs running continuously for weeks or months. That is far beyond what a single rented machine on Vast.ai is intended for and is usually done only by major AI labs and large technology companies.
### The practical approach
If your goal is to build your own AI assistant or a chatbot powered by your company’s knowledge base, then **fine-tuning** or **Retrieval-Augmented Generation (RAG)** is a much more practical and cost-effective approach. Vast.ai is an excellent platform for these workloads and costs only a fraction of what it would take to train a large language model from scratch.