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 demonstrates overwhelming compute superiority in AI-relevant precisions: FP16 at 1979 TFLOPS vastly outpaces the RTX 6000 Ada's 91.1 TFLOPS, enabling faster model training on massive datasets. Its FP8 capability reaches 3958 TFLOPS, ideal for inference on quantized large language models, while FP32 sits at 67 TFLOPS versus the RTX 6000 Ada's balanced 91.1 TFLOPS. This FP16 to FP32 delta means the H100 prioritizes tensor core acceleration for deep learning training, whereas the RTX 6000 Ada maintains parity in single-precision for graphics and simulations. Memory differences prove critical: 3350 GB/s bandwidth on HBM3 versus 960 GB/s on GDDR6 allows the H100 to process larger batch sizes without bottlenecks, supporting models like 70B-parameter LLMs that overwhelm the RTX 6000 Ada's 48 GB limit. In real-world terms, training throughput on H100 can exceed RTX 6000 Ada by over 20 times in FP16-heavy workflows. Power draw underscores scalability: H100's 700W TDP suits dense clusters, while 300W enables efficient single-node use.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
H100 NVL
| 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 NVL
Choose the H100 for large-scale LLM training or inference where VRAM exceeds 48 GB and FP16 compute surpasses 1979 TFLOPS proves essential. Its 3350 GB/s bandwidth handles enormous batch sizes in distributed setups via NVLink and PCIe 5.0. Datacenter tasks like scientific simulations on Hopper architecture benefit from 80 to 94 GB HBM3, unavailable on workstation GPUs.
When to Choose the RTX 6000 Ada Generation
Opt for the RTX 6000 Ada Generation in budget-constrained environments needing 48 GB GDDR6 for fine-tuning or Stable Diffusion at $0.20 per hour starting price. Its 300W TDP fits edge deployments or visualization workstations, with balanced 91.1 TFLOPS FP32 and FP16 suiting rendering and smaller inference. PCIe form factor simplifies integration without SXM5 requirements.
Use Cases
H100's 1979 TFLOPS FP16 and 80 to 94 GB HBM3 VRAM support massive models and batch sizes. RTX 6000 Ada's 48 GB limit and 91.1 TFLOPS fall short for large-scale training.
FP8 at 3958 TFLOPS on H100 accelerates quantized inference for billion-parameter models. Superior 3350 GB/s bandwidth handles high concurrency unlike RTX 6000 Ada's 960 GB/s.
H100's extensive VRAM and Hopper tensor cores enable efficient fine-tuning of models over 48 GB. RTX 6000 Ada suffices only for smaller datasets.
RTX 6000 Ada's 91.1 TFLOPS FP32 and lower $1.20 per hour average cost fit image generation workflows. 48 GB GDDR6 handles typical diffusion model sizes adequately.
H100's 3350 GB/s bandwidth and 80 to 94 GB VRAM accelerate simulations with large datasets. Its interconnects like NVLink outperform RTX 6000 Ada's PCIe-only setup.
Frequently Asked Questions
Which GPU has more VRAM: H100 or RTX 6000 Ada?▾
The H100 offers 80 to 94 GB HBM3 VRAM, exceeding the RTX 6000 Ada Generation's 48 GB GDDR6. This enables H100 to load larger models without swapping. RTX 6000 Ada suits workloads under 48 GB.
How do cloud prices compare for H100 NVL and RTX 6000 Ada?▾
H100 NVL starts at $1.40 per hour with an average of $2.89 across nine offers. RTX 6000 Ada Generation begins at $0.20 per hour averaging $1.20 over 48 offers. Price reflects H100's datacenter capabilities.
What is the FP16 performance difference?▾
H100 delivers 1979 TFLOPS FP16, over 21 times the RTX 6000 Ada's 91.1 TFLOPS. This gap accelerates AI training significantly on H100. RTX 6000 Ada performs adequately for lighter tensor tasks.
Which has higher memory bandwidth?▾
H100 provides 3350 GB/s with HBM3, more than three times the RTX 6000 Ada's 960 GB/s GDDR6. Higher bandwidth on H100 supports larger batches in ML. RTX 6000 Ada bandwidth fits professional viz.
Is H100 or RTX 6000 Ada better for power efficiency?▾
RTX 6000 Ada Generation uses 300W TDP versus H100's 700W, making it more efficient for single-node use. H100's power suits clustered high-throughput AI. Efficiency depends on workload scale.
Can RTX 6000 Ada handle LLM inference like H100?▾
RTX 6000 Ada's 91.1 TFLOPS FP16 limits it to smaller models under 48 GB, unlike H100's 3958 TFLOPS FP8 and 80 to 94 GB VRAM for large-scale inference. H100 excels in production serving.
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.