How to Spec a Custom GPU Server for AI/ML Work in India
Article summary
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If you build or train models in India, at some point you stop renting cloud GPUs and ask whether an in-house machine makes sense. It usually does once your utilisation is steady. Here's a practical way to spec a custom tower server for AI/ML work — no fluff. Start with the GPU The GPU decides what you can run, and VRAM is the ceiling: 24GB (RTX 4090) — comfortable for fine-tuning and inference on most 7B–13B models. 48GB (RTX 6000 Ada) — removes most memory limits; good for larger fine-tunes…
1Key Takeaways
- If you build or train models in India, at some point you stop renting cloud GPUs and ask whether an in-house machine makes sense.
- It usually does once your utilisation is steady.
- Here's a practical way to spec a custom tower server for AI/ML work — no fluff.
- Start with the GPU The GPU decides what you can run, and VRAM is the ceiling: 24GB (RTX 4090) — comfortable for fine-tuning and inference on most 7B–13B models.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that if you build or train models in India, at some point you stop renting cloud GPUs and ask whether an in-house machine makes sense.
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