Before the specs — here's what you need to know
Dedicated, not shared
Every instance is a full, dedicated GPU. No multi-tenant throttling, no noisy neighbours eating your throughput.
The price shown is the price billed
No egress charges, no surprise line items on the invoice.
Support that answers
Under 2 hours average resolution, 24/7, from 120+ engineers who work on infrastructure.
Data stays in India
Deployed from India. Built to meet DPDP, RBI, and SEBI data localisation requirements from day one.
No sales call required
Sign up, deploy, and start running workloads today. Talk to sales only if you want to, not because the platform makes you.
See what RTX Pro 6000 actually costs
Configure your usage below and see the exact number — hourly, monthly, or annual.
Mumbai Tier-4 Datacenter · Monthly & yearly commitments available · Custom configurations on request
Why teams choose this card
Built for AI and visual work
5th-gen Tensor Cores for inference, 4th-gen RT Cores for rendering. One card, two disciplines.
The newest architecture NVIDIA ships
Blackwell, with native FP4 precision support most previous-generation cards don't have.
On demand, not on order
No procurement cycle, no hardware to buy or wait on. Spin up an instance and start, scale down when the job's done.
What Blackwell actually changes
NVIDIA's own published figures from the RTX Pro 6000 Blackwell Server Edition launch, measured against the previous-generation L40S.
Up to 5x higher LLM inference throughput for agentic AI applications
Up to 3.3x faster text-to-video generation
Nearly 7x faster genomics sequencing
Nearly 2x faster recommender system inference
Over 2x faster rendering
5th-gen Tensor Cores: up to 3x the previous generation
4th-gen RT Cores: up to 2x the previous generation's ray-triangle intersection rate, with RTX Mega Geometry enabling up to 100x more ray-traced triangles
Source: NVIDIA, "Where AI and Graphics Converge" (RTX Pro 6000 Blackwell Server Edition launch, GTC 2025).
Built for these workloads
AI inference & LLM serving
Serve models up to 70B parameters in FP8 on a single GPU. No multi-GPU orchestration for most production inference jobs.
Fine-tuning & model training
Fine-tune models up to 30–40B parameters at full precision on one card, or larger with LoRA/QLoRA.
3D rendering & visualisation
Real-time ray tracing for architecture, automotive design, and VFX pipelines.
Digital twin & simulation
NVIDIA Omniverse-ready for manufacturing, robotics, and industrial digital twin work.
Scientific computing & HPC
FP32-heavy workloads including molecular dynamics and physics-based simulation.
What models actually fit in 96GB
| Model size | Precision | Approx. VRAM needed | Fits on one RTX Pro 6000? |
|---|---|---|---|
| 7B | FP16 | ~14 GB | Yes, comfortably |
| 13B | FP16 | ~26 GB | Yes |
| 30–40B | FP16 | ~60–80 GB | Yes |
| 70B | FP8 / INT8 | ~70 GB | Yes |
| 70B | FP16 (full precision) | ~140 GB | No — needs H200 or a multi-GPU setup |
| Mixtral-class MoE (141B total params) | Quantized | ~71 GB | Yes, quantized |
If you're moving off an older card
| Spec | RTX 6000 Ada | A100 80GB | H100 80GB | RTX Pro 6000 (Blackwell) |
|---|---|---|---|---|
| Architecture | Ada Lovelace | Ampere | Hopper | Blackwell |
| Memory | 48GB GDDR6 | 80GB HBM2e | 80GB HBM3 | 96GB GDDR7 |
| Memory bandwidth | 960 GB/s | ~1.9–2.0 TB/s | 3.35 TB/s | 1.6 TB/s |
| Native FP4 support | No | No | No | Yes |
| NVLink | No | Yes | Yes | No |
vs RTX 6000 Ada
A straightforward generational leap — double the memory, close to double the bandwidth, and a full architecture generation ahead, including native FP4 precision Ada doesn't support.
vs A100
More memory (96GB vs 80GB) and a newer architecture, though A100's HBM still edges out RTX Pro 6000 on raw bandwidth. The trade favours RTX Pro 6000 for single-GPU inference workloads that need the extra headroom over raw throughput.
vs H100
H100 still wins on raw bandwidth and on any workload that scales across GPUs over NVLink. RTX Pro 6000 wins on single-GPU inference economics — independent benchmarks show lower cost per token for models that fit on one card, since there's no interconnect cost to pay for.
Which CloudPe GPU fits your workload
Answer up to 3 quick questions. If RTX Pro 6000 isn't the right card, we'll tell you which one is — no wrong doors.
What's your primary workload?
Frequently Asked Questions
It's built on server-grade silicon with ECC memory and drivers certified for professional and AI workloads. It isn't designed or optimised for gaming.
They're not really the same category. The 5090 is a consumer card without ECC memory or MIG support, capped at 32GB. RTX Pro 6000 is built for production inference and rendering with 96GB of ECC memory. If gaming is the goal, the 5090 is the right card — it's just not this one.
Yes, both. It's best suited for inference and fine-tuning at up to 30–40B parameters at full precision, or up to 70B parameters quantized.
Usage-based, shown in full in the calculator above. No egress fees, no hidden line items.
Yes, deployed from CloudPe's Mumbai data centre.
Yes. Multi-GPU configurations are available. RTX Pro 6000 doesn't support NVLink, so GPUs communicate over PCIe rather than a dedicated interconnect — fine for most parallel inference jobs, a real constraint for large distributed training.
RTX Pro 6000 has more memory (96GB vs 80GB on both) and a newer architecture with native FP4 support neither A100 nor H100 has. H100 still has more raw memory bandwidth (3.35 TB/s vs 1.6 TB/s) and NVLink for multi-GPU scaling, which RTX Pro 6000 lacks. For single-GPU inference, RTX Pro 6000 is typically the more cost-efficient choice.
Up to 70B parameters quantized (FP8/INT8), or 30–40B at full precision, on a single card.