How much does it cost to translate Reddit into all languages, and is it profitable?

This story began for me after X suddenly started translating everything into all languages. At some point, I caught myself thinking: how much does such a thing actually cost if you do it not with a “translate” button, but at the platform level?

Not individual posts, not user on click, but the entire stream. A post or comment appears - immediately run through the model, put translations into the database, index them, serve to users and search engines.

On paper, this looks very powerful. The same content instantly becomes global. Any thread can be read in your own language, search engines get pages for different countries, the product entry point becomes wider. But then a simple engineering question arises: how much does it actually cost?

First estimate: take H100 and a large model

First, I went the most obvious route. Take a powerful universal model like DeepSeek and put it on H100. Simply because this is the standard approach when you want “maximum quality”. Then I estimated the flow of posts on Reddit. About 10 million posts and comments appear per day. The average piece of text is short, about 75 tokens. That gives about 800 million input tokens per day. If translating into 10 languages, that turns into about 8 billion output tokens.

When you start applying this to the real performance of a large LLM, you get an unpleasant result. To handle such a flow, you need about 1100 H100-level GPUs. This is no longer a “server with GPU”, it’s a full-fledged data center.

In terms of money, just the GPUs cost about $40 million, and with servers, network, cooling, and everything else, it becomes even more. At this point, it becomes clear that I was going in the wrong direction in my thinking.

The conclusion is obvious - change the model or the GPUs.

If the task is only translation, why do we need a model that can reason, write texts, and simulate dialogue? I googled and found that there are specialized AI models for translation. For example, NLLB. It is trained specifically for translation, works faster and cheaper.

And here the economics change dramatically. Such a model runs not on H100, but on much more ordinary cards:

RTX 4090, L40S, A10, A100.

Optimal for production looks like L40S. It’s a normal server card, not space-priced, and gives good throughput. After recalculation, instead of a thousand H100s, we need a cluster of about 250 L40S. This is still a large scale. But it’s no longer an OpenAI-level story. It’s just expensive infrastructure that can be calculated and assembled.

How much does it cost?

Take the same 250 L40S. One card costs about $7,000. Just the GPUs give about $1.75 million in costs.

But in practice, a card does not exist by itself. You need servers, CPU, memory, disks, network, racks, and power. Usually that’s about the same on top, but a bit cheaper. In total, we get about $2.5 million for the entire cluster. This is a rough estimate, but the order is clear.

Now electricity. One L40S consumes up to 350 W. In total, the cluster including everything else comes to about 180 kW. Per month, that’s about 130,000 kWh. If we take the average data center price, it’s about 25,000 euros per month. And here’s an interesting point: electricity doesn’t seem to be the main problem. The main money goes into purchasing hardware.

Do we need 10 languages?

At this stage, it became obvious to me that the main cost driver is the number of languages. Each new language linearly increases the load. But traffic does not grow linearly. The first languages by number of speakers give a huge effect, then diminishing returns set in. Translating into, say, Finnish, where there are literally a few million speakers and English penetration is 97%, becomes absolutely unprofitable. So I decided not to take 10 languages, but to think about which ones actually give maximum coverage. The choice was based on the number of speakers, the top looks like this: English, Spanish, Portuguese, French, Russian, Hindi, and Indonesian. Also, German is everywhere, but I saw that coverage is not the largest (120 million), while English penetration in Germany is very high, so it can definitely not be a priority. What does this give in terms of coverage?

If we roughly count all speakers of these languages, including those who use them as a second language, we get a fairly impressive number. English about 1.4 billion, Hindi about 600 million (yes, not everyone in India speaks Hindi), Spanish about 560 million, French about 300 million, Russian about 250 million, Portuguese about 260 million, Indonesian about 200 million. You can’t just add these numbers because there are overlaps. But if we reduce it to unique coverage, we get about 3.5 billion people. That’s about 65% of the entire internet.

Recalculation for 7 languages

When the calculation moved from 10 languages to 7, the load dropped by about a third. Accordingly, the cluster also shrinks. Instead of 250 cards, we get about 180 L40S.

In terms of money:

GPU about $1.25 million
full cluster about $2 million

Electricity:

about 90,000 kWh per month
about 18,000 euros per month

So a small part of potential coverage is lost, but we save a lot on infrastructure.

Is it profitable for Reddit?

In the dry residue, the economics look quite simple. Launching such a system would cost about $2 million, plus about $0.5 million per year for support and electricity. For a company of Reddit's scale, this is not money that cannot be spent; the question is only about the return.

And the return here is primarily in traffic. Translation opens access to search in local languages and removes the entry barrier for a huge number of users. Even if this gives just a few percent growth, it already turns into tens of millions of additional views and, as a result, millions of dollars in advertising revenue per year. With proper implementation, this looks like an investment with a multiple return.

Therefore, I think we will see such a move from the company in the very near future.