One card that trains, serves and renders. 96 GB of GDDR7 memory and fifth-generation Tensor Cores let you run mid-size LLMs, generative video and heavy 3D scenes on the same instance — billed in rupees, invoiced with GST. Free setup and migration make the move simple, and hourly billing lets you prove a workload before you commit to a term.
Quick answer · Updated September 2026
What is the RTX PRO 6000 Blackwell cloud GPU?
The NVIDIA RTX PRO 6000 Blackwell is a 96 GB GDDR7 professional GPU that handles both AI and graphics. On XcellHost it is rented by the hour or month from Indian data centres, starting at ₹1,15,000 a month (excl. GST), for LLM fine-tuning and inference, generative video, digital twins and heavy 3D rendering.
Key takeaways
Twice the memory of any 48 GB card — fits 70B-class models quantised on one GPU
FP4 Tensor Cores cut the cost per token for inference
Split into up to four MIG slices for teams and notebooks
Hosted in India with INR billing and GST invoice
Best for:LLM inferenceFine-tuningGenerative videoDigital twins
Rent RTX PRO 6000 Cloud GPUs
Use 96 GB of professional GPU memory for generative AI, simulation, rendering and digital twins.
Start with ₹20,000 free credits
Get RTX PRO 6000 Cloud GPUs access in minutes
Share your details and we’ll send the access steps by email.
We value your privacy and never share your information with third parties. See our Privacy Policy.
Flexible plans for every workload
RTX PRO 6000 Cloud GPU Plans
Select your billing period and choose the GPU configuration that fits your team. Prices start at ₹1,15,000/month.
For single users & PoC · 1 GPU
1×
RTX PRO 6000GPU Cloud Lite
Server Type
Dedicated GPU VM
GPU
1 × 96 GB
Virtual CPU
16 vCPU
Memory
128 GB
NVMe Storage
1000 GB
Network Port
1 Gbps
Dedicated IP
1
Operating System
Ubuntu 22.04 / Win Server 2025
Drivers
CUDA pre-installed
Protection
Virtual Firewall + Anti-DDoS
Endpoint Security
Acronis Cyber Protect Cloud
Support
24×7
₹1,15,000+GST · Monthly billing≈ ₹197/hr on demand
For single users & PoC · 1 GPU
1×
RTX PRO 6000GPU Cloud Standard
Server Type
Dedicated GPU VM
GPU
1 × 96 GB
Virtual CPU
24 vCPU
Memory
192 GB
NVMe Storage
1000 GB
Network Port
1 Gbps
Dedicated IP
1
Operating System
Ubuntu 22.04 / Win Server 2025
Drivers
CUDA pre-installed
Protection
Virtual Firewall + Anti-DDoS
Endpoint Security
Acronis Cyber Protect Cloud
Support
24×7
₹1,36,000+GST · Monthly billing≈ ₹233/hr on demand
For single users & PoC · 1 GPU
1×
RTX PRO 6000GPU Cloud Pro
Server Type
Dedicated GPU VM
GPU
1 × 96 GB
Virtual CPU
32 vCPU
Memory
256 GB
NVMe Storage
2000 GB
Network Port
1 Gbps
Dedicated IP
1
Operating System
Ubuntu 22.04 / Win Server 2025
Drivers
CUDA pre-installed
Protection
Virtual Firewall + Anti-DDoS
Endpoint Security
Acronis Cyber Protect Cloud
Support
24×7
₹1,57,000+GST · Monthly billing≈ ₹269/hr on demand
For teams & multi-GPU · 2 GPUs
2×
RTX PRO 6000GPU Cloud Duo
Server Type
Dedicated GPU VM
GPU
2 × 96 GB
Virtual CPU
48 vCPU
Memory
384 GB
NVMe Storage
2000 GB
Network Port
1 Gbps
Dedicated IP
1
Operating System
Ubuntu 22.04 / Win Server 2025
Drivers
CUDA pre-installed
Protection
Virtual Firewall + Anti-DDoS
Endpoint Security
Acronis Cyber Protect Cloud
Support
24×7
₹2,72,000+GST · Monthly billing≈ ₹466/hr on demand
For teams & multi-GPU · 2 GPUs
2×
RTX PRO 6000GPU Cloud Duo Max
Server Type
Dedicated GPU VM
GPU
2 × 96 GB
Virtual CPU
64 vCPU
Memory
512 GB
NVMe Storage
4000 GB
Network Port
1 Gbps
Dedicated IP
1
Operating System
Ubuntu 22.04 / Win Server 2025
Drivers
CUDA pre-installed
Protection
Virtual Firewall + Anti-DDoS
Endpoint Security
Acronis Cyber Protect Cloud
Support
24×7
₹3,14,000+GST · Monthly billing≈ ₹538/hr on demand
For training & render farms · 4 GPUs
4×
RTX PRO 6000GPU Cloud Quad
Server Type
Dedicated GPU VM
GPU
4 × 96 GB
Virtual CPU
96 vCPU
Memory
768 GB
NVMe Storage
4000 GB
Network Port
1 Gbps
Dedicated IP
1
Operating System
Ubuntu 22.04 / Win Server 2025
Drivers
CUDA pre-installed
Protection
Virtual Firewall + Anti-DDoS
Endpoint Security
Acronis Cyber Protect Cloud
Support
24×7
₹5,45,000+GST · Monthly billing≈ ₹933/hr on demand
For training & render farms · 4 GPUs
4×
RTX PRO 6000GPU Cloud Quad Max
Server Type
Dedicated GPU VM
GPU
4 × 96 GB
Virtual CPU
128 vCPU
Memory
1024 GB
NVMe Storage
8000 GB
Network Port
1 Gbps
Dedicated IP
1
Operating System
Ubuntu 22.04 / Win Server 2025
Drivers
CUDA pre-installed
Protection
Virtual Firewall + Anti-DDoS
Endpoint Security
Acronis Cyber Protect Cloud
Support
24×7
₹6,28,000+GST · Monthly billing≈ ₹1,075/hr on demand
Indicative pricing in INR, exclusive of 18% GST. Plan codes: XG.PRO6K.1-16, XG.PRO6K.1-24, XG.PRO6K.1-32, XG.PRO6K.2-48, XG.PRO6K.2-64, XG.PRO6K.4-96, XG.PRO6K.4-128.
Hourly or monthly? Work it out.
Tell us how much you'll actually use the GPU and we'll show the cheaper way to pay.
Pay-as-you-go₹34,672
Monthly plan₹1,15,000
1-year plan (per month)₹1,03,500
Go hourly — you'd save ₹80,328 a month versus the monthly plan.
Indicative, excl. GST.
Key features of RTX PRO 6000 on XcellHost
AI and graphics on one cardFine-tune a 30B-parameter model in the morning and render a path-traced scene in the afternoon — no second fleet to buy or manage.
FP4 precision for cheaper inferenceBlackwell's FP4 Tensor Cores roughly double tokens-per-rupee on quantised models compared with FP8 on the previous generation.
Slice it with MIGSplit one GPU into up to four isolated instances with dedicated memory — ideal for dev teams, notebooks and small inference endpoints.
Confidential by designSecure boot, hardware root of trust and confidential computing keep model weights and customer data sealed — useful for DPDP Act obligations.
India-hosted, low latencyServe users across India from our Mumbai-region data centres without overseas round trips.
Live GPU monitoringTrack utilisation, memory and temperature from the portal and get alerts before jobs slow down.
Hourly or term billingRun a proof of concept by the hour, then save up to 10% on a 1-year term.
Experts on callOur engineers help tune vLLM, TensorRT and render settings for the best throughput.
Why choose XcellHost for your RTX PRO 6000 cloud GPU
Hosted in India
GPUs run in Indian data centres, so latency stays low and your data stays in the country for DPDP Act comfort.
INR billing with GST invoice
Pay in rupees and claim input tax credit — no forex swings or USD card charges.
99.95% uptime SLA
Enterprise power, cooling and network with a written uptime commitment.
24×7 human GPU experts
Real engineers on phone, WhatsApp and ticket — in English and Hindi.
Free setup & migration
We install drivers, CUDA and frameworks, and move your data and images for you.
Scale 1 → 4 GPUs
Start with one GPU for a proof of concept and add more as projects grow.
ISO 27001 security
Certified operations, virtual firewall, anti-DDoS and Acronis endpoint protection.
No surprise bills
Flat plans include storage, a public IP and bandwidth — no hidden egress charges.
Outcomes you can plan around
2×
Bigger models per GPU
96 GB versus 48 GB means fewer shards and simpler serving for 30B–70B models.
+66%
More memory bandwidth
1,597 GB/s against 960 GB/s on RTX 6000 Ada speeds up memory-bound inference.
4
Isolated GPU slices
MIG turns one card into four secure instances for dev, test and small endpoints.
₹0
Hardware capex
Rent instead of buying ₹8–10 lakh workstations; stop paying when the project ends.
Ratios are based on NVIDIA's published specifications. Actual results depend on your model, framework and batch size.
What are the NVIDIA RTX PRO 6000 Blackwell specifications?
1
96 GB ECC memory
96 GB GDDR7 with error correction holds large models, long context windows and huge scenes on one card.
2
24,064 CUDA · 752 Tensor cores
Fifth-generation Tensor Cores add FP4 support — up to 4 PFLOPS of AI compute for quantised inference.
3
Passive heatsink
Server edition with no fan — cooled by the chassis airflow, built for 24×7 duty in our Indian data centres.
4
PCIe Gen 5 x16
Double the host bandwidth of Gen 4 for faster data loading and multi-GPU scaling inside a node.
Architecture
NVIDIA Blackwell
GPU memory
96 GB GDDR7 with ECC
Memory bandwidth
1,597 GB/s
CUDA cores
24,064
Tensor cores
752 (5th gen)
RT cores
188 (4th gen)
FP32
120 TFLOPS
FP8 Tensor
2 PFLOPS*
FP4 Tensor
4 PFLOPS*
Multi-Instance GPU
Up to 4 isolated slices
Interconnect
PCIe Gen 5 x16
Form factor
Dual-slot, passive (server edition)
*With sparsity. Figures per NVIDIA's published server-edition specifications.
Performance
How RTX PRO 6000 stacks up against 48 GB cards — published specifications, side by side.
RTX PRO 6000
96
L40S
48 +100%
RTX 6000 Ada
48 +100%
RTX A6000
48 +100%
GPU Workstation On-Premises VS RTX PRO 6000 On Cloud
Buying your own workstation
XcellHost GPU Cloud
₹8–10 lakh+ upfront hardware capex
Monthly OPEX from ₹1,15,000 — no capex
Hardware ages out in about three years
Move to newer GPUs whenever you're ready
Power, cooling and UPS are your problem
Tier-IV class data-centre power and cooling
Usable only at the office desk
Access from office, home or site, on any device
Manual backups, if any
Snapshot and Acronis backup options
Needs in-house IT to maintain
Managed 24×7 by certified experts
One faulty part stops all work
Hardware replacement backed by an SLA
Fixed capacity, idle most nights
Scale up for deadlines, down when done
Launch RTX PRO 6000 In The Cloud 4 Easy Steps
01
Share your workload
Tell us the model, scene or software you'll run and your hours of use. We reply with a sizing note and a GST-ready quote.
02
Pick a plan and term
Choose hourly for a proof of concept, or a monthly to 1-year RTX PRO 6000 plan. KYC and a PO are all we need.
03
We deliver your GPU server
We provision the RTX PRO 6000 with your OS, drivers, CUDA and frameworks, then share SSH or remote-desktop access.
04
Run, monitor and scale
Track GPU use from the portal, call 24×7 support any time, and add GPUs as projects grow.
LLM fine-tuning & inferenceLoRA/QLoRA on 7B–70B models; high-throughput serving with vLLM or TensorRT-LLM.
Generative video & imagesText-to-video, diffusion and upscaling pipelines that need memory headroom.
Heavy 3D & digital twinsOmniverse, Unreal and USD scenes too large for 48 GB cards.
Rendering & VFXPath-traced frames for studios in Mumbai, Hyderabad and Pune without a render farm.
Simulation & scienceCFD, molecular dynamics and geospatial workloads with FP32 muscle.
Analytics on GPURAPIDS, cuDF and vector search over large in-memory datasets.
Shared team workstationsMIG slices or vWS desktops for design and data-science teams.
Platforms & workloads covered
Windows Server
Ubuntu Linux
Docker
Kubernetes
Jupyter
Any browser (RDP/DCV)
Case studies — what teams achieve
LF
Moving a document-AI model from US GPUs to India cut latency and settled data-residency questions
Lending fintech, 120 staff · Fintech · BengaluruIllustrative case study
Challenge
The team served a 34B-parameter document-understanding model on US-hosted GPUs. Round-trip latency from India was high, egress bills were unpredictable, and the compliance team wanted borrower data kept in India under the DPDP Act.
What we set up
Two XG.PRO6K.1-32 instances in our Mumbai zone running vLLM with FP8 weights, a private network to their app servers, and MIG slices for staging. We migrated weights and set up Grafana GPU dashboards.
Configuration: 2 × XG.PRO6K.1-32 on a 12-month term
62%lower p95 inference latency
38%lower monthly GPU bill in INR
100%borrower data kept in India
AP
Generative video previz for ad agencies without building a render farm
Ad-film post studio · Media · MumbaiIllustrative case study
Challenge
Agencies wanted AI-generated animatics within a day. The studio's 24 GB workstations ran out of memory on text-to-video models, so jobs failed or had to be cut into short clips.
What we set up
A 4-GPU XG.PRO6K.4-96 node with ComfyUI and a shared NVMe project volume. Artists queue jobs from their desks; we burst to hourly billing during campaign weeks.
Configuration: 1 × XG.PRO6K.4-96, hourly during peaks
3×more concepts delivered per brief
18 hturnaround, down from 3 days
0out-of-memory failures after migration
AM
A plant digital twin that engineers and the AI team share on one GPU pool
Auto-components maker · Manufacturing · PuneIllustrative case study
Challenge
Engineering needed a real-time Omniverse twin of a new line, while the data team trained vision models for defect detection. Buying separate workstations and a training server would take a full quarter's budget.
What we set up
Two XG.PRO6K.2-64 nodes: MIG slices for vision training during the day, full GPUs for the twin during design reviews. A site-to-site VPN connects the plant network.
Configuration: 2 × XG.PRO6K.2-64 on a 6-month term
Every RTX Pro 6000 deployment ships with the same baseline, documented and auditable. Access is the control that matters most in a hosted environment, so it is treated as the primary boundary rather than an afterthought.
NVIDIA
