A €100,000 bill is pushing this company toward local AI
A company spent €100,000 on Adobe credits in a single month and cancelled all its AI subscriptions to build its own GPU rig. Is it worth it?
English · Original discussion in Spanish · Published
A €100,000 bill is pushing this company toward local AI
One company has had enough: €100,000 in Adobe credits in a single month, and every AI subscription cancelled. The plan is to build a GPU rig and run the models locally. It sounds like a bold statement, but the technical fine print complicates the dream.
The cloud premium
The bill spirals because subscriptions charge around ten times the compute actually used, in credits. The alternative: buying several GPUs with 24-32 GB of VRAM and running models from 14B to 35B, enough for coding, RAG or document analysis. It's the same logic already used by companies running open source models in production and keeping paid ones just for experimentation.
The limits of a home rig
It's not all rosy math. RTX 5090 cards can't pool memory, so the practical ceiling is 32 GB per card, not enough for large models. Server GPUs that do share memory are a different league, and far more expensive. And obsolescence is a real risk: the sector moves so fast that a local setup can be outdated in barely over a year.
Fleeing the cloud may be the start of a bigger disappointment. As long as subscriptions keep that premium, some will prefer buying hardware over renting smoke. Until the next generation of models upends it all again.
One company has had enough: €100,000 in Adobe credits in a single month, and every AI subscription cancelled. The plan is to build a GPU rig and run the models locally. It sounds like a bold statement, but the technical fine print complicates the dream.
The cloud premium
The bill spirals because subscriptions charge around ten times the compute actually used, in credits. The alternative: buying several GPUs with 24-32 GB of VRAM and running models from 14B to 35B, enough for coding, RAG or document analysis. It's the same logic already used by companies running open source models in production and keeping paid ones just for experimentation.
The limits of a home rig
It's not all rosy math. RTX 5090 cards can't pool memory, so the practical ceiling is 32 GB per card, not enough for large models. Server GPUs that do share memory are a different league, and far more expensive. And obsolescence is a real risk: the sector moves so fast that a local setup can be outdated in barely over a year.
Fleeing the cloud may be the start of a bigger disappointment. As long as subscriptions keep that premium, some will prefer buying hardware over renting smoke. Until the next generation of models upends it all again.
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Summary of a discussion on Burbuja.info - Foro de economía, actualidad y política., translated from Spanish and reviewed before publication. Read the full discussion (18 replies).
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