Running an open model on your own graphics card looks free once it is bought. Here is how to price the card, the hidden costs and the same traffic paid per message in dinars.
Your card was refused abroad, so running a model at home looks like the way out. An open model such as Llama costs nothing to download. It needs no foreign account and no subscription.
The catch is the machine. This article puts a dinar figure on each side, so you can decide with numbers rather than with hope.
A model lives in the memory of the graphics card while it answers. If it does not fit, it slows to a crawl or does not start.
VRAM is the memory of the graphics card. Its job is to hold the model while it works.
Count two bytes per parameter at full precision. An 8-billion-parameter model therefore needs about 16 GB for its weights alone, before the conversation itself takes its share.
Compressed versions exist. They shrink the model to a third of its size, and they also lose some quality. A 70-billion-parameter model, even compressed, still asks for two 24 GB cards working together.
The card is only the first line of the bill. It needs a power supply strong enough, cooling, and a machine around it.
What if I buy a second-hand card on Ouedkniss?
The price drops, and the warranty disappears with it. A card that ran day and night for someone else may fail within months, and nobody refunds you.
Summer is the other cost. A card under load heats the room, and in July a room with no air conditioning makes it slow itself down to survive.
Budget for an uninterruptible power supply. A power cut in the middle of a long job can corrupt your files and damage the card.
Now the other side. With an API, you pay for each message and nothing before the first one.
Let's see right away what that costs.
Your shop's assistant answers 50,000 questions a month. Llama 3.1 8B Instruct costs about 0.028 DA a message on its model page, read on 5 October 2026. That makes about 1,400 DA a month, with no machine to buy.
A message, on the model page, means 1,000 tokens sent and 500 returned. A longer conversation costs more, and the page shows you the rate for each side.
The larger Llama 3.3 70B Instruct, the one that needs two cards at home, costs about 0.15 DA a message. The same 50,000 messages come to about 7,500 DA a month.
The comparison comes down to one division. Take the price of the card and its machine, then divide it by what the API would cost you each month.
For every 100,000 DA of hardware, the API pays about 71 months of the traffic above on Llama 3.1 8B. That is almost six years, before electricity and before the first repair.
Buy a card first and look for the traffic later.
Run a month on the API, read your real volume on your usage page, then decide whether a machine would ever pay for itself.
When you are ready to call a model from your code, the five-minute alternative to buying a GPU shows you the two lines to change.
Context: you found a graphics card you like on a listing, and your app sends about 20,000 messages a month.
Task: in a spreadsheet, work out how many months of API use on Llama 3.1 8B the card's price would pay for.
20,000 messages at about 0.028 DA make about 560 DA a month. Divide the card's price by 560. A card listed at 100,000 DA equals about 179 months of API use, nearly fifteen years.
Yes, a compressed 7B or 8B model fits on an 8 GB card. It answers slowly and loses quality, which makes it a good learning tool and a weak base for a product.
OpenDunes is hosted in Algeria, and its API can mask personal data before a request leaves for the model. Check what your own data requires under Law 18-07 before you send it anywhere.
Yes. The API speaks the OpenAI format, so the official OpenAI libraries and the frameworks built on them work after you change the base URL and the key.
You now know what a machine really costs against a price per message. In the next article, you plug your code into the API in five minutes. See you there!