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GPU Product

Rent NVIDIA RTX PRO 6000 GPUs

96 GB VRAM for Enterprise-Scale AI & ML Workloads

Technical Specifications

ArchitectureNVIDIA Blackwell
Memory Size96 GB GDDR7 ECC
Memory Bandwidth1 792 GB/s
Ray Tracing Cores188
Tensor Cores752
NVIDIA RTX PRO 6000
Rental Options

RTX PRO 6000 Rental Options

Rent RTX PRO 6000 GPUs on-demand with flexible pricing. Get 96 GB of VRAM for your largest AI workloads without upfront hardware costs.

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Comparison

RTX PRO 6000 vs RTX 5090

RTX PRO 6000RTX 5090% Diff
ArchitectureBlackwellBlackwellN/A
Process TechTSMC 4 nmTSMC 4 nmN/A
Transistors≈110 B92.2 B+19.3%
Compute Units (SMs)188170+10.6%
Shaders (CUDA)24 06421 760+10.6%
Tensor Cores752680+10.6%
RT Cores188170+10.6%
ROPs216192+12.5%
TMUs752680+10.6%
Boost Clock2 617 MHz2 407 MHz+4.35%
Memory TypeGDDR7 ECCGDDR7N/A
VRAM96 GB32 GB+200%
Bus Width512-bit512-bit0%
VRAM Speed28 Gbps28 Gbps0%
Bandwidth1 792 GB/s1 790 GB/s+0.1%
TDP600 W575 W+4.35%
PCIePCIe 5.0 ×16PCIe 5.0 ×16N/A

Datacenter Comparison

RTX PRO 6000 Blackwell vs NVIDIA H100 vs H200

Choosing between the RTX PRO 6000 Blackwell and NVIDIA's Hopper datacenter GPUs depends on your workload. In an RTX PRO 6000 vs H100 comparison, the Blackwell card pulls ahead on raw compute density and offers more VRAM (96 GB vs 80 GB) at a meaningfully lower hourly cost. In an RTX PRO 6000 vs H200 comparison, the H200 keeps the lead on memory capacity (141 GB) and bandwidth (4.8 TB/s) for the largest models, but the PRO 6000 is significantly cheaper to run and uses newer 5th-generation Blackwell tensor cores.

RTX PRO 6000H100 SXM5H200 SXM
ArchitectureBlackwellHopperHopper
Process TechTSMC 4 nmTSMC 4NTSMC 4N
Transistors≈110 B80 B80 B
Compute Units (SMs)188132132
Shaders (CUDA)24 06416 89616 896
Tensor Cores752 (5th gen)528 (4th gen)528 (4th gen)
Boost Clock2 617 MHz~1 980 MHz~1 980 MHz
Memory TypeGDDR7 ECCHBM3HBM3e
VRAM96 GB80 GB141 GB
Bandwidth1 792 GB/s3 350 GB/s4 800 GB/s
TDP600 W700 W700 W
PCIePCIe 5.0 ×16PCIe 5.0 ×16PCIe 5.0 ×16
Form FactorWorkstation/ServerSXM5SXM5

When the RTX PRO 6000 Wins

Multi-tenant inference, fine-tuning models up to ~100B parameters, generative AI rendering, and cost-sensitive enterprise deployments where Blackwell efficiency and 96 GB of VRAM matter more than peak HBM bandwidth.

When the H100 or H200 Wins

Frontier-scale training (200B+ parameters), workloads bottlenecked by memory bandwidth such as large MoE models or very long-context inference, and multi-node deployments that depend on NVLink fabric.

For full RTX PRO 6000 Blackwell benchmarks across LLM inference workloads, see our GPU benchmarks hub.

Performance

Performance Metrics

Massive GPU Memory

96 GB of GDDR7 lets you fit multi-billion-parameter LLMs and complex 3D scenes on a single card.

Blackwell AI Engines

Fifth-gen Tensor and fourth-gen RT cores deliver >125 TFLOPS FP32 and up to 4 000 AI TOPS.

PCIe 5.0 Throughput

Double host-to-GPU bandwidth vs PCIe 4.0, eliminating data-transfer bottlenecks in large-scale workloads.

Use Cases

Ideal Use Cases

Ultra-Large LLMs

Train or serve 70–175 B-parameter language models without multi-GPU partitioning.

Generative XR

Real-time ray tracing and neural render pipelines for AR/VR experiences.

AI-Driven Media

Video upscaling, diffusion, and texture synthesis at production scale.

HPC Simulation

Accelerate molecular dynamics, CFD, and large-graph analytics workloads.

RTX PRO 6000 FAQ

Common Questions About the RTX PRO 6000

NVIDIA Blackwell workstation GPU comes with 96 GB GDDR7 VRAM and very high memory bandwidth. Built for large AI models, complex scenes, and heavy simulation.
Yes. Next-gen RT and Tensor cores plus large VRAM handle high-poly scenes, 4K or 8K textures, NeRFs, and complex path-traced renders.
Yes. The 96 GB memory fits larger checkpoints and batches, which speeds up fine-tuning and high-throughput inference.
Yes. Multi-GPU nodes are available for data or model parallel training and large-scene rendering. Availability varies by location.
VRAM headroom is the big difference: 96 GB vs 32 GB. The 6000 Blackwell avoids out-of-memory issues on bigger models and data-heavy pipelines.
The RTX PRO 6000 Blackwell delivers more VRAM than the H100 (96 GB vs 80 GB) and uses newer 5th-generation Blackwell tensor cores, making it strong for inference and mid-scale training at a lower hourly cost. The H200 retains the lead in memory bandwidth (4.8 TB/s vs 1.79 TB/s) and capacity (141 GB), so it remains the better fit for the largest LLM training runs. For most enterprise inference and fine-tuning workloads, the RTX PRO 6000 is the more cost-effective choice.
Open the Console, create a new instance, choose VM deployment select RTX 6000 Pro, then launch. For long-term reservations, contact us.
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