NVIDIA GPU & HPC Solutions for AI, Simulation and Visualisation
From a single RTX PRO workstation to a multi-node GPU cluster, S A Vision sizes, builds and supports NVIDIA-based infrastructure for engineering, research and enterprise AI across India.
Which NVIDIA platform fits what you do?
Start from the work, not the part number. This guide shows where each NVIDIA platform usually fits; we validate it against your applications before you commit.
| Your workload | Typical NVIDIA platform | Typical users |
|---|---|---|
| CAD, 3D design, rendering, VR | NVIDIA RTX PRO 2000 to 5000 Blackwell workstation GPUs | Design and engineering teams |
| Heavy simulation, multi-GPU rendering, large local AI models at a desk | NVIDIA RTX PRO 6000 Blackwell, 96 GB (Workstation or Max-Q edition) | AI and simulation specialists |
| Personal AI development and prototyping | NVIDIA DGX Spark (GB10 Grace Blackwell, 128 GB unified memory) | Developers and researchers |
| Team-scale AI development with very large models | DGX Station class systems (GB300 Grace Blackwell Ultra) or multi-GPU RTX PRO workstations | AI teams and labs |
| Production inference, vision AI, VDI, render farms | RTX PRO 6000 Blackwell Server Edition in GPU servers | IT and operations teams |
| Large-scale training and HPC | Hopper and Blackwell data-centre GPU systems with high-speed fabric and parallel storage | Research labs, enterprise AI |
| Creative work, gaming, entry-level AI | GeForce RTX 50 series | Creators, students, studios |
Specifications are based on NVIDIA's public information and may change. See the product catalogue for the current line-up.
The NVIDIA line-up we configure
RTX PRO Blackwell
Professional GPUs for design, visualisation and AI: RTX PRO 2000 (16 GB), 4000 (24 GB), 4500 (32 GB), 5000 (48 GB) and 6000 (96 GB), all with ECC memory.
GeForce RTX 50 series
RTX 5090, 5080, 5070 Ti, 5070, 5060 Ti and 5060 for creators, studios and cost-sensitive AI development.
DGX Spark & DGX Station
Desk-side AI supercomputing: Spark for individuals, Station-class GB300 systems for teams working on very large models.
GPU servers
Rack servers with RTX PRO 6000 Blackwell Server Edition for inference, vision AI, virtual workstations and render farms.
Data-centre systems
Hopper and Blackwell GPU systems for training and large-scale HPC, with high-speed networking.
Storage & fabric
NAS and parallel file systems plus the network design that keeps GPUs busy instead of waiting for data.
HPC cluster design, build and support
A cluster is more than a stack of GPUs. We design compute, interconnect, storage and software as one system.
Requirements & benchmarking
Application profiling and proof-of-concept runs on your data to size nodes, memory and GPUs.
Architecture
Compute, network fabric and storage tiers balanced for your workload and budget.
Build & software stack
OS, GPU drivers, CUDA libraries, containers and job scheduling configured and validated.
Handover & support
Admin and user training, documentation, monitoring and annual maintenance options.
Workstations for graphics, visualisation and creative pipelines
Professional graphics and 3D visualization workstations for CAD and CAE, product design, VFX and animation, medical imaging and virtual production. We match GPU, CPU, memory and storage to your applications and certified-software requirements, and connect them to shared storage for team workflows.
For digital-twin and simulation-ready 3D workflows we also set up NVIDIA Omniverse based environments where they fit your pipeline.
Typical applications
- CAD, CAE and product visualisation
- VFX, animation and virtual production
- Medical imaging and scientific visualisation
- AI-assisted design and local model development
- Multi-user rendering and shared storage
Technology depth, cost discipline and real support
With 21+ years in high-end computing, S A Vision combines hands-on engineering expertise with relationships across NVIDIA's ecosystem and trusted brands such as PNY, Leadtek, HP, Rashi Peripherals, Allway HPC and Redington. We focus on maximising performance while maintaining cost efficiency, and our Pune-based engineers stay with you through deployment and support.
NVIDIA & HPC questions
What is the difference between RTX PRO and GeForce RTX for professional work?
RTX PRO GPUs are built for professional use: ECC memory, larger memory options, professional drivers with software-vendor certification and 24x7 reliability. GeForce RTX offers excellent value for creators, students and entry-level AI work. We recommend based on the applications you run and the cost of downtime.
How much GPU memory do I need for AI?
Model size, numeric precision and batch size decide memory needs, and techniques such as quantisation can fit larger models into less memory. We benchmark with your own models and data, then recommend a configuration so you do not overspend or under-size.
DGX Spark or DGX Station?
DGX Spark is a compact desktop AI system built on the GB10 Grace Blackwell Superchip with 128 GB of unified memory, suited to individual developers and prototyping. DGX Station is a much larger GB300 Grace Blackwell Ultra class system, delivered through NVIDIA's OEM partners, aimed at teams developing and running very large models locally.
Can you build an HPC cluster on a limited budget?
Yes. We right-size the first phase, choose GPU types for the real workload, reuse what you already own where sensible and design the network and storage so the cluster can grow in stages.
Do you provide installation and support across India?
Yes. Our Pune-based engineers handle design, installation, software stack setup and support for customers across India, with annual maintenance contracts available.
Planning an NVIDIA, HPC or AI safety deployment?
Tell us your workload and sites. Our engineers will recommend a right-sized platform and a clear next step.