The NVIDIA RTX A6000 gives rendering, simulation and AI teams a 48GB professional GPU that handles scenes and models most cards can't load — without the capital cost of buying one. Deploy on-demand or as dedicated bare-metal, inside Indian data centres.
From early-stage AI startups to Fortune 500 R&D labs.
PLATFORM CHARACTERISTICS
NVIDIA RTX A6000 is a professional Ampere-generation GPU built around 48GB of ECC memory. That memory footprint is the reason teams pick it: full production scenes, large CAD assemblies and mid-size AI models fit on a single card, so nothing has to be split, simplified or batched down.
Professional Operations
Most teams hit the same wall. A single professional GPU workstation is a capital purchase that takes weeks to approve and months to arrive — and by the time it lands, the render deadline has moved or the model has grown. Meanwhile, hyperscaler GPU instances bill in dollars, add egress charges nobody forecast, and often place your data outside India.
*A GPU bought for a six-week project sits unused for the other forty-six. *Dollar-denominated GPU billing plus unpredictable data transfer fees make budgets impossible to hold. *Under the DPDP Act 2023, regulated teams in BFSI, healthcare and government need to answer where the data physically sits. "A region in Singapore" is not an answer.
KEY BENEFITS
Select an operational framework based on ownership preferences, management responsibilities, and business priorities.
| Feature | Independent Teams ₹160 / hr | Collaborative Operations ₹78,000 / month | Managed Delivery Custom — contact sales |
|---|---|---|---|
| Decision Authority |
Sovereign infrastructure, INR pricing, and engineers who have actually run these workloads.
Every RTX A6000 workload runs inside Indian data centres. Full data residency, no cross-border transfer.
INR pricing, no forex risk, no egress surprises vs. AWS, Azure and GCP.
On-demand instances for burst work, dedicated bare-metal for sustained pipelines — 1 to 8 GPUs, up to 128 cores and 2TB RAM, on 100 Gbps networking.
PyTorch, TensorFlow, CUDA, Jupyter and LLM frameworks ready on day one.
Rendering studios, engineering teams and AI groups running A6000 capacity in Indian data centres.
200+
Indian Customers
99.99%
Monthly uptime
24/7
Named SRE
Frequently asked
Learn about NVIDIA RTX A6000 infrastructure, deployment options, and enterprise GPU environments.
he NVIDIA RTX A6000 is a professional GPU built on the Ampere architecture with 48GB of GDDR6 ECC memory, 10,752 CUDA cores, 336 third-generation Tensor Cores and 84 second-generation RT Cores. It delivers 38.7 TFLOPS of FP32 performance and 768 GB/s of memory bandwidth, and is used for rendering, visualization, simulation, and AI inference and fine-tuning.
Each RTX A6000 provides 48GB of GDDR6 memory with ECC error correction. Two cards can be joined with an NVLink bridge for 96GB of combined memory and 112 GB/s of GPU-to-GPU bandwidth — useful for scenes or models that exceed a single card.
Yes, for inference and fine-tuning. Its 336 Tensor Cores and 48GB of memory handle production inference for diffusion, vision and NLP models, and support LoRA or QLoRA fine-tuning on mid-size open models. For large-scale pre-training, Host360 also offers H200, B200 and GB300-class Blackwell hardware
Yes. RTX A6000 supports advanced visualization, rendering, simulation, and AI-enabled professional environments.
Yes. RTX A6000 deployments commonly integrate with networking, storage, security, and management platforms.
Additional GPU resources can be added as project requirements and operational demands increase.
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Tell us what you're rendering, simulating or serving. A solutions architect will size the deployment, quote it in rupees, and tell you honestly if a different GPU is the better fit for what you're building.
Coordinate teams effectively.
Track operational activities.
INR-denominated pricing, no forex exposure, and no surprise egress line item at month end.
| Team Leads |
| Joint Teams |
| Service Managers |
| Management Effort | High Involvement | Moderate Involvement | Reduced Involvement |
| Support Structure | Internal Resources | Shared Resources | Managed Support |
| Operational Focus | Direct Execution | Coordination | Outcome Delivery |
| Planning Style | Independent Planning | Joint Planning | Service Planning |
| Resource Governance | Team Policies | Shared Policies | Managed Policies |
| Deployment Type | — | — | — |
On-demand cloud for bursts, dedicated bare-metal for sustained runs — same portal, same support.
Cloud engineers who understand AI workloads, backed by a 99.99% uptime SLA.
Measure performance.
Support scaling initiatives.
Head of Pipeline, post-production studio
“We keep a reserved baseline and burst on-demand when a delivery lands. Two cards NVLinked at 96GB handled a shot we'd otherwise have had to split across machines. Billing in rupees also ended a long-running argument with finance”
Dr. Meera Nambiar
Imaging Informatics Lead, hospital network
“Data residency was non-negotiable for us. Host360 gave us a written posture we could put in front of our compliance committee, not a marketing page. Reconstruction throughput improved as a side benefit.”
Sandeep Rao
“Large assembly simulation used to mean queueing for the one good workstation in the office. Now the whole design team has GPU access. ECC memory was the detail our CAE lead insisted on — accuracy in validation runs isn't negotiable.”
Farhan Qureshi
“48GB is the sweet spot for what we actually do — inference in production plus LoRA fine-tunes on mid-size models. We evaluated hyperscaler equivalents and the Indian latency plus INR pricing made the decision straightforward.”