AI / ML · LLM fine-tuning · Inference
AI Workstations in Mumbai
Workstations built for training, fine-tuning, and inference of language and diffusion models. PCIe lane budgeting, ECC tradeoffs, 1200-1500W 80+ Titanium PSUs, and VRAM tier matching for the workload. No RGB, all thermals.
PCIe lane budgeting, on paper first
Most workstation CPUs (Threadripper, Xeon W, EPYC) advertise 48-128 PCIe lanes, but the practical allocation against a multi-GPU + NVMe + 10GbE build is tighter than the headline number. We map lanes to devices on paper before we order parts, not after.
ECC tradeoffs, honestly
For training and fine-tuning of LLMs and large diffusion models, ECC does not give you a measurable accuracy win. What it does give you is reliability: bit-flips in a multi-day training run are usually fatal. We tell you honestly which tier your workload needs.
1200-1500W PSUs, sized for transients
Modern GPUs and CPUs pull 1.5-2x TDP for milliseconds during state transitions. We add sustained power draw, multiply by 1.4 for transients, and add 100W for the rest of the system. A typical 2x RTX 4090 build needs a 1200-1500W 80+ Titanium PSU. We do not under-size PSUs for AI builds.
Workload sizing
VRAM tier guide
A practical cheat sheet for matching VRAM to model size and use case. The honest answer is usually the second-cheapest card, not the most expensive.
8 GB
Inference of small models (≤3B parameters) and local IDE assistants.
Example: RTX 4060 Ti 8GB
12-16 GB
Fine-tuning small models (≤7B) and inference of mid-size models (7-13B).
Example: RTX 4070 Ti SUPER, RTX 4080 SUPER
24 GB
Fine-tuning 7-13B and inference of 30-70B models in 4-bit. The sweet spot for most AI builders.
Example: RTX 4090, RTX 5090
48 GB
Fine-tuning 30-70B and inference of 100B+ in 4-bit. For serious ML work.
Example: RTX 6000 Ada, A6000
What we will not build
Honest constraints
- We will not spec a single 16A Indian socket for a >1800W build. The math: a single 16A socket is fused at 16A × 230V = 3680W maximum, but sustained draw above ~1800W on a single socket is asking for a tripped breaker or a warm socket over time. Multi-GPU AI builds need a dedicated circuit.
- We will not under-size a PSU. If your build needs 1200W sustained, you get a 1500W PSU with 80+ Titanium rating. We lose the sale sometimes to builders who say "850W will be fine" — it is not fine, and we will not sign off on it.
- We will not skimp on cooling because the workload is "headless". A 24-hour training run is exactly the workload that cooks VRMs. We spec 360mm AIOs and positive-pressure cases as if the build were a gaming build.
- We will not promise NVLink on consumer cards. RTX 4090 / 5090 / 6000 Ada do not have NVLink. If you need NVLink (H100, A100), that is a different class of build with very different budgeting.
AI workstation questions
What is PCIe lane budgeting and why does it matter for AI workstations?
Do I need NVLink for AI workloads?
Should I get ECC memory for an AI workstation?
What is the right PSU size for an AI workstation?
How do I pick the right VRAM tier?
Contact & hours
We service all 35 areas across Mumbai, Monday to Saturday.