Lab Resources
Computational Resources
IIT Gandhinagar has a number of computational resources available for research and teaching purposes. In addition, the department also has a number of workstations available for students and faculty members.
The following is the list of computational resources in our lab apart from the institute and departmental resources.
Servers
Our servers are named after great mathematicians/scientists, plus Sustain after the lab’s focus on sustainability:
- Ramanujan (रामानुजन): Srinivasa Ramanujan made groundbreaking contributions to number theory.
- Bhaskar (भास्कर): Bhaskar (aka Bhaskar II) is known for his work Siddhānta Shiromani (सिद्धांत शिरोमणि) which includes advances in algebra, calculus, and astronomy.
- Sustain: named after the lab’s focus on sustainability and energy research.
| Ramanujan | Bhaskar | Sustain | |
|---|---|---|---|
| RAM | 512 GB (16 x 32GB) | 256 GB (4 x 64GB) | 192 GB (6 x 32GB) |
| CPU | AMD EPYC 7452 32-Core Processor @ 2.30 GHz | Intel Gold ICX 6326 @ 2.90 GHz | Intel(R) Xeon(R) Silver 4208 CPU @ 2.10GHz |
| Storage | 8 TB | 5 TB | 2 TB |
| Number of CPUs | 64 | 32 | 16 |
| GPU | 4 x NVIDIA A100-SXM4 (80GB) | 2 x NVIDIA RTX A5000 (24GB) | 2 x NVIDIA RTX A4000 (16GB) |
| Total VRAM | 320 GB | 48 GB | 32 GB |
Workstations
Our workstations are named after Indian scientists: Aryabhata (आर्यभट), well-known for the concept of zero (शून्य) and the place value system, and Vikram.
| Aryabhata | Vikram | |
|---|---|---|
| RAM | 32 GB | 64 GB |
| CPU | Intel Core i9-13900 | Intel Core i9-14700 |
| Storage | 2 TB | 4 TB |
| Number of CPUs | 24 | 32 |
| GPU | 1 x NVIDIA RTX Titan XP (12 GB) | 1 x NVIDIA RTX A2000 (12 GB) |
| Total VRAM | 12 GB | 12 GB |
Relative Performance
Measured with our open gpu-benchmark-suite — the same Docker-based workloads on every machine, grouped into raw compute, training, inference, and data-loading so the numbers are directly comparable.
| Benchmark | Ramanujan | Bhaskar | Sustain | Aryabhata | Vikram |
|---|---|---|---|---|---|
| GPU | A100-SXM4-80GB | RTX A5000 | RTX A4000 | TITAN Xp | RTX A2000 |
| Raw compute (TFLOPS, fp16) | 269.7 | 89.2 | 61.3 | 8.7 | 26.4 |
| Training — ResNet50 (img/s) | 1059.9 | 717.7 | 508.5 | 257.3 | 304.5 |
| Inference — ResNet50 (img/s) | 3139.4 | 1419.5 | 888.6 | 726.9 | 522.7 |
| Inference — detection (FPS) | 57.9 | 46.2 | 28.9 | 22.5 | 19.0 |
| Inference — LLM gen (tok/s) | 70.8 | 87.2 | 46.8 | — | 71.7 |
| Data loading (img/s) | 2483.8 | 2216.5 | 957.6 | 4805.0 | 4413.1 |
Raw compute (TFLOPS) and training throughput track the GPUs’ real power most reliably — the A100 leads everywhere it matters. Small-batch inference (LLM tokens/sec, detection FPS) is latency-bound, so it can look flat on large GPUs and varies with concurrent load. Numbers are point-in-time, single-GPU measurements; the TITAN Xp’s LLM generation is omitted as its older (Pascal) architecture is impractically slow at FP16 generation.
Server Policy
How to get access: Send an email to Dr. Supin Gopi, keeping Prof. Nipun Batra in cc. Mention the following details in your request: i) server name; ii) purpose for access.
Don’ts:
- Do not use the server for personal use or anything not related to the project. This includes any classwork or homework.
- Do not share your server credentials with anyone else.
Fair usage:
- Sometimes we have multiple projects running on the same server. Please be considerate of other projects and do not use up all the resources.
- Sometimes we may allocate specific cores or GPUs to specific projects. Please do not use cores or GPUs that are not allocated to your project.