Specifications Compared
| Spec | GH200 | QUADRO-RTX-8000 |
|---|---|---|
| TDP | 900W | 260W |
| VRAM | 96 GB | 48 GB |
| CUDA Cores | 16,896 | 4,608 |
| Memory Type | HBM3 | GDDR6 |
| Architecture | Hopper | Turing |
| Form Factors | SXM | PCIe |
| Interconnect | NVLink-C2C, PCIe 5.0 | NVLink |
| Tensor Cores | 528 | 576 |
| FP8 Performance | 3,958 TFLOPS | |
| FP16 Performance | 1,979 TFLOPS | 16.3 TFLOPS |
| FP32 Performance | 67 TFLOPS | 16.3 TFLOPS |
| FP64 Performance | 34 TFLOPS | |
| INT8 Performance | 3,958 TOPS | |
| Memory Bandwidth | 4,000 GB/s | 672 GB/s |
Performance Analysis
The GH200's FP16 performance of 1979 TFLOPS vastly outpaces the Quadro RTX 8000's 16.3 TFLOPS, enabling over 120 times faster half-precision computations critical for deep learning training and inference. This FP16 advantage supports training larger models with more parameters, while the GH200's FP32 at 67 TFLOPS offers about four times the single-precision throughput of the Quadro RTX 8000's 16.3 TFLOPS, benefiting scientific simulations and rendering. The GH200's FP8 capability at 3958 TFLOPS further accelerates inference tasks in modern LLMs. Memory bandwidth defines another chasm: 4000 GB/s on the GH200 permits batch sizes up to six times larger than the 672 GB/s on the Quadro RTX 8000, reducing data loading bottlenecks in memory-bound workloads like transformer training. Higher TDP of 900 W on the GH200 versus 260 W reflects its datacenter orientation, demanding robust cooling, whereas the Quadro RTX 8000 fits power-constrained workstations.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
GH200
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vultr | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 72 vCPU 480GB RAM 960GB Storage | Atlanta | $1.99/GPU/hr | Available | ||
 Lambda Labs | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 64 vCPU 432GB RAM 4096GB Storage | Virginia | $2.29/GPU/hr | Available | ||
 Denvr | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 72 vCPU 480GB RAM 7600GB Storage | Virginia | $3.87/GPU/hr | |||
 CoreWeave | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 72 vCPU 480GB RAM 7680GB Storage | United States | $6.50/GPU/hr |
When to Choose the GH200
Select the GH200 for large-scale AI training and inference where 96 GB HBM3 VRAM and 4000 GB/s bandwidth handle massive datasets and models. Its 1979 TFLOPS FP16 and 3958 TFLOPS FP8 excel in LLM development and HPC simulations requiring NVLink-C2C interconnects in SXM form factor. Cloud availability at $1.99 per hour makes it ideal for scalable, on-demand compute.
When to Choose the Quadro RTX 8000
Choose the Quadro RTX 8000 for legacy workstation tasks like CAD, 3D modeling, and professional visualization where PCIe form factor and 260 W TDP integrate easily into desktops. Its 48 GB GDDR6 suffices for graphics workloads not demanding AI-scale compute, and NVLink supports multi-GPU setups in compatible systems. Absence of cloud offers suggests on-premise availability for cost-sensitive, non-AI professional use.
Use Cases
GH200's 1979 TFLOPS FP16 and 96 GB HBM3 enable training of massive LLMs with large batch sizes. Quadro RTX 8000's 16.3 TFLOPS FP16 cannot compete.
GH200's 3958 TFLOPS FP8 and 4000 GB/s bandwidth accelerate high-throughput inference. Quadro RTX 8000 lacks FP8 support and sufficient bandwidth.
GH200's 67 TFLOPS FP32 and high memory capacity handle fine-tuning of large models efficiently. Quadro RTX 8000's lower specs limit scale.
GH200's superior FP16 performance and VRAM support faster image generation at higher resolutions. Quadro RTX 8000 suits only smaller-scale tasks.
GH200's 67 TFLOPS FP32 and NVLink-C2C excel in parallel simulations. Quadro RTX 8000's 16.3 TFLOPS FP32 fits basic compute only.
Frequently Asked Questions
What is the VRAM difference between GH200 and Quadro RTX 8000?▾
The GH200 offers 96 GB HBM3 VRAM, doubling the Quadro RTX 8000's 48 GB GDDR6. This allows GH200 to manage larger models and datasets. Bandwidth also differs: 4000 GB/s versus 672 GB/s.
How do FP16 performances compare?▾
GH200 delivers 1979 TFLOPS FP16, over 120 times the Quadro RTX 8000's 16.3 TFLOPS. This gap accelerates AI training significantly. FP32 shows GH200 at 67 TFLOPS versus 16.3 TFLOPS.
What are the power requirements?▾
GH200 has a 900 W TDP suited for datacenters, while Quadro RTX 8000 uses 260 W for workstations. Higher TDP correlates with GH200's compute density. Form factors are SXM versus PCIe.
Is GH200 available in the cloud?▾
GH200 pricing starts at $1.99 per hour, averaging $3.59 per hour across four offers. Quadro RTX 8000 has no live cloud offers. This makes GH200 preferable for on-demand use.
What architectures do they use?▾
GH200 employs 2023 Hopper architecture with FP8 support at 3958 TFLOPS. Quadro RTX 8000 uses 2018 Turing without FP8. Interconnects include NVLink-C2C for GH200 and NVLink for Quadro RTX 8000.
Which has higher memory bandwidth?▾
GH200 provides 4000 GB/s, nearly six times the Quadro RTX 8000's 672 GB/s. This impacts batch sizes in training. HBM3 versus GDDR6 contributes to the difference.
Which is cheaper to rent, the GH200 or the Quadro RTX 8000?▾
Cloud rental prices for both the GH200 and Quadro RTX 8000 vary by provider, configuration, and availability. This page shows live pricing from 25+ providers updated every 60 seconds. Scroll to the Live Cloud Pricing section to compare current rates.
How much VRAM does the GH200 have compared to the Quadro RTX 8000?▾
The GH200 has 96 GB of HBM3 memory. The Quadro RTX 8000 has 48 GB of GDDR6 memory.
Can I find GH200 and Quadro RTX 8000 GPUs available to rent right now?▾
Yes. This page shows real-time availability across 25+ cloud GPU providers. The Live Cloud Pricing section displays only in-stock offers with current pricing.
What is the main difference between the GH200 and the Quadro RTX 8000?▾
The GH200 uses the Hopper architecture (2023) while the Quadro RTX 8000 uses Turing (2018). The GH200 delivers 121.4x the FP16 throughput and 6.0x the memory bandwidth of the Quadro RTX 8000.