Specifications Compared
| Spec | H100 | RTX-6000-ADA |
|---|---|---|
| TDP | 700W | 300W |
| VRAM | 80-94 GB | 48 GB |
| CUDA Cores | 16,896 | 18,176 |
| Memory Type | HBM3 | GDDR6 |
| Architecture | Hopper | Ada Lovelace |
| Form Factors | SXM5, PCIe, NVL | PCIe |
| Interconnect | NVLink, PCIe 5.0, InfiniBand | NVLink |
| Tensor Cores | 528 | 568 |
| FP8 Performance | 3,958 TFLOPS | |
| FP16 Performance | 1,979 TFLOPS | 91.1 TFLOPS |
| FP32 Performance | 67 TFLOPS | 91.1 TFLOPS |
| FP64 Performance | 34 TFLOPS | 1.4 TFLOPS |
| INT8 Performance | 3,958 TOPS | 1,457 TOPS |
| Memory Bandwidth | 3,350 GB/s | 960 GB/s |
Performance Analysis
The H100 PCIe excels in mixed-precision AI workloads due to its 1979 TFLOPS FP16 performance, enabling faster neural network training than the RTX 6000 Ada's 91.1 TFLOPS FP16. Its FP32 throughput of 67 TFLOPS trails the RTX 6000 Ada's 91.1 TFLOPS, making the latter preferable for FP32-dominant tasks like traditional simulations. FP8 capability on H100 at 3958 TFLOPS optimizes large language model inference with quantized models.
Memory specifications create clear divides: H100's 80 to 94 GB HBM3 VRAM and 3350 GB/s bandwidth accommodate massive batch sizes and models exceeding 48 GB, minimizing data transfer bottlenecks in training loops. RTX 6000 Ada's 48 GB GDDR6 and 960 GB/s suffice for smaller datasets but limit scalability. Higher 700W TDP on H100 supports sustained peak loads, versus 300W on RTX 6000 Ada for efficient smaller jobs.
These metrics translate to real-world gains: H100 accelerates deep learning epochs by leveraging high tensor core throughput, while RTX 6000 Ada balances graphics and compute without extreme power demands.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
H100 PCIe
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Hyperstack | 4×NVIDIA H100 PCIe 80GB VRAM | 80GB | 124 vCPU 720GB RAM 3300GB Storage | Canada | $1.90/GPU/hr $7.60/hr total (4×) | Available | ||
Hyperstack | 2×NVIDIA H100 PCIe 80GB VRAM | 80GB | 60 vCPU 360GB RAM 1600GB Storage | Canada | $1.90/GPU/hr $3.80/hr total (2×) | Available | ||
Hyperstack | 8×NVIDIA H100 PCIe 80GB VRAM | 80GB | 252 vCPU 1440GB RAM 6600GB Storage | Canada | $1.90/GPU/hr $15.20/hr total (8×) | Available | ||
Hyperstack | NVIDIA H100 PCIe 80GB VRAM | 80GB | 28 vCPU 180GB RAM 850GB Storage | Canada | $1.90/GPU/hr | Available | ||
Hyperstack | 8×NVIDIA H100 PCIe 80GB VRAM | 80GB | 252 vCPU 1440GB RAM 6600GB Storage | Canada | $1.95/GPU/hr $15.60/hr total (8×) | Available |
RTX 6000 Ada Generation
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA RTX 6000 Ada Generation 48GB VRAM | 48GB | 16 vCPU 188GB RAM | 🌍global | $0.50/GPU/hr | |||
RunPod | NVIDIA RTX 6000 Ada Generation 48GB VRAM | 48GB | 10 vCPU 167GB RAM | 🌍global | $0.77/GPU/hr | |||
 Massed Compute | NVIDIA RTX 6000 Ada Generation 48GB VRAM | 48GB | 12 vCPU 72GB RAM 350GB Storage | Iowa | $0.79/GPU/hr | Available | ||
Massed Compute | 8×NVIDIA RTX 6000 Ada Generation 48GB VRAM | 48GB | 104 vCPU 640GB RAM 2800GB Storage | Iowa | $0.79/GPU/hr $6.32/hr total (8×) | Available | ||
Massed Compute | 4×NVIDIA RTX 6000 Ada Generation 48GB VRAM | 48GB | 52 vCPU 288GB RAM 1400GB Storage | Iowa | $0.79/GPU/hr $3.16/hr total (4×) | Available |
When to Choose the H100 PCIe
Select the H100 PCIe for large-scale AI training and inference where models demand over 48 GB VRAM, such as LLMs with billions of parameters. Its 1979 TFLOPS FP16 and 3350 GB/s bandwidth enable larger batches and faster convergence, ideal for datacenter clusters using NVLink or InfiniBand. Cloud users prioritizing throughput over cost benefit from this in production pipelines.
When to Choose the RTX 6000 Ada Generation
Opt for the RTX 6000 Ada Generation in cost-sensitive visualization, rendering, or mid-scale machine learning tasks fitting within 48 GB GDDR6. Its 91.1 TFLOPS FP32 matches FP16 for balanced compute, and 300W TDP reduces power costs versus H100's 700W. Abundant cloud availability at $0.20 per hour average suits prototyping or professional workflows.
Use Cases
H100's 1979 TFLOPS FP16 and 80-94 GB HBM3 VRAM handle massive datasets and models efficiently. Bandwidth at 3350 GB/s supports large batch sizes critical for training stability.
FP8 performance of 3958 TFLOPS on H100 accelerates quantized inference for huge models. High VRAM capacity exceeds RTX 6000 Ada's 48 GB limit.
H100's superior memory bandwidth of 3350 GB/s and 80-94 GB VRAM enable fine-tuning on large pre-trained models. It outperforms in mixed-precision tasks.
RTX 6000 Ada's 91.1 TFLOPS FP16 and 48 GB GDDR6 suffice for image generation pipelines. Lower $1.21 per hour average cost fits iterative creative workflows.
RTX 6000 Ada leads in FP32 at 91.1 TFLOPS for simulations, while H100's 67 TFLOPS FP32 pairs with higher VRAM for data-intensive codes. Choice depends on precision needs.
Frequently Asked Questions
Which GPU has more VRAM?▾
The H100 PCIe offers 80 to 94 GB HBM3 VRAM, surpassing the RTX 6000 Ada Generation's 48 GB GDDR6. This allows H100 to load larger AI models without swapping. Bandwidth follows suit at 3350 GB/s versus 960 GB/s.
What are the cloud rental prices?▾
H100 PCIe starts at $1.25 per hour, averaging $2.73 across 15 offers. RTX 6000 Ada begins at $0.20 per hour, averaging $1.21 across 48 offers. RTX provides more availability for budget users.
Which is better for AI training?▾
H100 PCIe dominates with 1979 TFLOPS FP16 and 80-94 GB VRAM for large-scale training. RTX 6000 Ada's 91.1 TFLOPS FP16 suits smaller jobs. Memory bandwidth of 3350 GB/s on H100 reduces bottlenecks.
How do power consumptions compare?▾
H100 PCIe has a 700W TDP for peak datacenter performance. RTX 6000 Ada uses 300W, enabling lower cooling and energy costs. This affects cloud pricing indirectly.
What architectures do they use?▾
H100 employs Hopper architecture optimized for AI tensor operations. RTX 6000 Ada uses Ada Lovelace for graphics and compute balance. Both launched in 2022 with NVLink support.
Is FP32 performance equal on RTX 6000 Ada?▾
RTX 6000 Ada delivers 91.1 TFLOPS FP32, matching its FP16. H100 trails at 67 TFLOPS FP32 but leads overall in AI precisions. Choose based on workload precision.
Which is cheaper to rent, the H100 or the RTX 6000 Ada?▾
Cloud rental prices for both the H100 and RTX 6000 Ada 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 H100 have compared to the RTX 6000 Ada?▾
The H100 has 80 to 94 GB of HBM3 memory. The RTX 6000 Ada has 48 GB of GDDR6 memory.
Can I find H100 and RTX 6000 Ada 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 H100 and the RTX 6000 Ada?▾
The H100 uses the Hopper architecture (2022) while the RTX 6000 Ada uses Ada Lovelace (2022). The H100 delivers 21.7x the FP16 throughput and 3.5x the memory bandwidth of the RTX 6000 Ada.