Run Llama 3.1 8B
Ready in about 90 seconds. No install, no setup. From $0.45 per hour.
What it does
Llama 3.1 8B is small enough to train on a single card and capable enough to be useful afterwards. That combination is why most fine-tuning starts here.
Fine-tuning means showing the model a few hundred to a few thousand examples of the work you want, so it learns your tone, your format, and your vocabulary. It does not teach the model new facts reliably. It teaches it a job.
Typical results: a support reply writer that sounds like your team, a summarizer that produces your report format, a classifier that uses your internal category names.
What you can ask it
800 past support replies, before and after your editing.
You get backA model that drafts replies in your house style, ready to review.
500 examples of your weekly report format.
You get backA model that turns raw numbers into your report without a prompt template.
2,000 tickets tagged with your internal categories.
You get backA classifier that uses your names, not generic ones.
Your setup
We run this on an RTX 4090. A LoRA fine-tune on a thousand examples takes about 40 minutes, which is roughly $0.30. $0.45 per hour, billed by the minute.
Why this card?
Training needs far more memory than running, because the card has to hold the model, a copy of the gradients, and the optimizer state at the same time. Full fine-tuning of an 8 billion parameter model needs around 80GB. We use LoRA instead, which trains a small adapter rather than the whole model and brings the requirement down to about 16GB. That fits an RTX 4090 with room to spare, and for the kind of style-and-format learning most people want, LoRA gets you the same result.
- Memory
- 24GB
- Speed
- about 2 seconds per example
- Rate
- $0.45/hr
What will it cost you?
Billed by the minute on a RTX 4090 at $0.45 per hour. A batch job shuts the GPU off when the last example is done, so this is the whole cost.
Two ways to run it
Click and use it
JupyterLab with Axolotl installed and a working config already filled in. For people who want to see the training curve.
Get early access Best for exploring.Hand us the whole pile
Upload a spreadsheet of examples, set the strength, and collect a finished model. This is the path most people should take.
Join the list Best for volume. The GPU shuts off automatically when it is done.Model facts
| Parameters | 8B |
|---|---|
| License | Llama 3.1 Community License |
| Memory required | 16GB minimum for LoRA, 80GB for full fine-tuning |
| Base model | Trained from scratch by Meta |
| Publisher | Meta |
| Recommended hardware | RTX 4090, 24GB |
| Hugging Face | meta-llama/Llama-3.1-8B-Instruct |
Want Llama 3.1 8B the day it opens?
We are testing the rental flow with a small group first. Leave an email and we will tell you when Llama 3.1 8B is ready to run. One message, no newsletter.
Llama 3.1 8B questions
How many examples do I need to fine-tune a model?
Between 200 and 1,000 for most tasks. Below 200 the model tends to memorize rather than learn a pattern. Above about 2,000 the returns flatten out for style-and-format work. Quality matters far more than quantity: 300 carefully checked examples beat 3,000 messy ones every time.
Who owns the model I train?
You do. The adapter you produce is yours, it downloads to your machine, and we do not use it or your training data for anything. The base model keeps its original license, so a model fine-tuned from Llama 3.1 is still subject to the Llama Community License when you deploy it.
Do I need to install anything to use Llama 3.1 8B?
No. We start a machine with Llama 3.1 8B already loaded and hand you a link. Everything runs in your browser, there is nothing to download, and nothing is left on your computer afterwards. It works the same on a Mac, a Windows laptop, or a Chromebook. A workspace is usually ready in about 90 seconds.
How much does it cost to run 1,000 training examples?
About $0.30. Llama 3.1 8B takes about 2 seconds per example on the RTX 4090 we recommend, so 1,000 training examples is roughly 40 minutes of GPU time at $0.45 per hour. Billing is by the minute, and a batch job shuts the GPU off the moment the last item finishes. The calculator above works this out for your own numbers.
Can I run Llama 3.1 8B on a cheaper card?
Sometimes, and the calculator will not always make it look worth it. Training needs far more memory than running, because the card has to hold the model, a copy of the gradients, and the optimizer state at the same time. Full fine-tuning of an 8 billion parameter model needs around 80GB. We use LoRA instead, which trains a small adapter rather than the whole model and brings the requirement down to about 16GB. That fits an RTX 4090 with room to spare, and for the kind of style-and-format learning most people want, LoRA gets you the same result. If you want to try a different card anyway, the advanced catalog lets you pick one and shows the estimated time before you commit.
What are the license restrictions on Llama 3.1 8B?
Llama 3.1 8B is released under the Llama 3.1 Community License. That is not a permissive license, so read it before you use the output in paid work. We show the license on every model page precisely because this catches people out, and there is usually a permissively licensed alternative in the same category.
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