GPU & AI SERVERS
GPU & AI Servers
Built in India. Proven in production — including our own data centre.
Most GPU servers you'll evaluate have never run a real workload before they reach you. Ours have. The same class of machine you're specifying is already carrying live training and inference load — in customer deployments, and in our own data centre, where we prove every new firmware and driver combination before it ever ships to you. This isn't a first-generation platform sold on a datasheet. It's a mature line, supported by the team that built it.
GPU and AI server sizing calculator — Work out the VRAM, GPU count, RAM and storage an AI workload needs, with the arithmetic shown.
Configurations in this range
- Altair 3G16SN — 3U GPU platform with sixteen hot-swap bays, built for accelerated work that has to keep its data close — inference, analytics and visual computing.
Frequently asked questions
Should I start with a tower or a rack GPU server?
Tower for proofs of concept, fine-tuning and continuous inference where a rack is not practical — it runs on office mains and stays quiet. Rack where density, airflow and serviceability matter, or where you will add nodes.
What should I prioritise for inference and RAG?
VRAM and NVMe latency. Start with a tower carrying 1–2 RTX GPUs of 24GB or more, and step up to a 2-GPU rack node for higher queries per second.
What does a fine-tuning node look like?
A balance of GPU count and CPU cores — typically a rack node with 2 to 4 GPUs, 128 to 256GB of RAM and three NVMe drives, keeping OS, scratch and dataset separate.
And full training?
More GPUs and faster fabric: a rack node with 4 to 6 GPUs, 100 or 200G networking, and an airflow-optimised chassis that holds boost clocks under sustained load.
Do I need NVLink?
Usually not. NVLink matters for large model parallelism and heavy multi-GPU training; most inference and many fine-tunes run excellently on PCIe Gen4 or Gen5 alone. We map your framework and batch size to the right interconnect.
How do you stop performance drifting after deployment?
Firmware, driver and container stacks are pinned to known-good versions, systems ship with golden images, and burn-in runs with the GPUs at peak draw so thermal and power behaviour is proven before delivery.