Specifications Compared
| Spec | H100 | RTX-4000-ADA |
|---|---|---|
| TDP | 700W | 130W |
| VRAM | 80-94 GB | 20 GB |
| CUDA Cores | 16,896 | 6,144 |
| Memory Type | HBM3 | GDDR6 |
| Architecture | Hopper | Ada Lovelace |
| Form Factors | SXM5, PCIe, NVL | PCIe |
| Interconnect | NVLink, PCIe 5.0, InfiniBand | |
| Tensor Cores | 528 | 192 |
| FP8 Performance | 3,958 TFLOPS | |
| FP16 Performance | 1,979 TFLOPS | 26.7 TFLOPS |
| FP32 Performance | 67 TFLOPS | 26.7 TFLOPS |
| FP64 Performance | 34 TFLOPS | |
| INT8 Performance | 3,958 TOPS | 427 TOPS |
| Memory Bandwidth | 3,350 GB/s | 360 GB/s |
Performance Analysis
The H100's FP16 performance of 1979 TFLOPS vastly outpaces the RTX 4000 Ada's 26.7 TFLOPS, making it superior for training deep learning models where half-precision computations accelerate iterations. Its FP32 rate of 67 TFLOPS also exceeds the RTX 4000 Ada's 26.7 TFLOPS, benefiting single-precision tasks in scientific simulations. These metrics translate to faster convergence in large-scale neural network training on the H100.
Memory capacity and bandwidth define workload feasibility: the H100's 80 to 94 GB HBM3 at 3350 GB/s supports enormous batch sizes in model training and inference, reducing overhead from data swapping. The RTX 4000 Ada's 20 GB GDDR6 at 360 GB/s limits it to smaller batches, suitable only for modest models. This disparity affects inference latency, where H100's FP8 at 3958 TFLOPS enables high-throughput serving of large language models.
Power draw underscores deployment differences, as the H100's 700W TDP suits datacenter cooling, while the RTX 4000 Ada's 130W fits edge or desktop 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 4000 Ada Generation
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA RTX 4000 Ada Generation 20GB VRAM | 20GB | 8 vCPU 50GB RAM | 🌍global | $0.26/GPU/hr | |||
 Vast.ai | NVIDIA RTX 4000 Ada Generation 20GB VRAM | 20GB | 64 vCPU 42GB RAM 505GB Storage | Hungary | $0.40/GPU/hr | Available | ||
RunPod | NVIDIA RTX 4000 Ada Generation 20GB VRAM | 20GB | 8 vCPU 50GB RAM | 🌍global | $0.44/GPU/hr | |||
RunPod | NVIDIA RTX 4000 Ada Generation 20GB VRAM | 20GB | 0 vCPU 0GB RAM | 🌍global | $0.57/GPU/hr |
When to Choose the H100 NVL
The H100 excels in large-scale AI training and inference, where its 80 to 94 GB VRAM and 3350 GB/s bandwidth handle models exceeding 20 GB, such as billion-parameter LLMs. Datacenter users benefit from NVLink and InfiniBand interconnects for multi-GPU scaling, unavailable on the RTX 4000 Ada.
High-performance computing tasks demanding 1979 TFLOPS FP16 justify the H100 NVL's $1.40 to $2.89 per hour pricing, delivering returns through rapid experimentation cycles.
When to Choose the RTX 4000 Ada Generation
The RTX 4000 Ada suits cost-sensitive prototyping and visualization, with 20 GB VRAM and 26.7 TFLOPS FP16 at $0.09 to $0.27 per hour enabling quick iterations without datacenter overhead. Its 130W TDP and PCIe form factor simplify deployment in workstations or small clusters.
Smaller AI tasks like image generation or fine-tuning compact models leverage its balanced FP16 and FP32 performance efficiently.
Use Cases
LLM training requires massive VRAM and compute: H100's 80 to 94 GB HBM3 and 1979 TFLOPS FP16 handle billion-parameter models, unlike RTX 4000 Ada's 20 GB limit.
High-throughput inference benefits from H100's 3958 TFLOPS FP8 and 3350 GB/s bandwidth for large batches. RTX 4000 Ada suits only small models due to 20 GB VRAM.
Fine-tuning large models demands H100's 67 TFLOPS FP32 and high memory capacity. RTX 4000 Ada's constraints limit it to smaller datasets.
Stable Diffusion runs efficiently on RTX 4000 Ada's 26.7 TFLOPS FP16 and 20 GB VRAM at low $0.09 per hour cost. H100 overkill for single-image generation.
Scientific simulations leverage H100's 67 TFLOPS FP32 and NVLink scaling for complex datasets. RTX 4000 Ada's lower specs restrict large-scale computations.
Frequently Asked Questions
What is the VRAM difference between H100 and RTX 4000 Ada?▾
The H100 offers 80 to 94 GB HBM3 VRAM, compared to the RTX 4000 Ada's 20 GB GDDR6. This allows H100 to process much larger models without offloading. RTX 4000 Ada suffices for workloads under 20 GB.
How do cloud prices compare for H100 NVL and RTX 4000 Ada?▾
H100 NVL starts at $1.40 per hour, averaging $2.89 across nine offers. RTX 4000 Ada begins at $0.09 per hour, averaging $0.27 across ten offers. Price reflects H100's datacenter performance.
What are the FP16 performance specs?▾
H100 delivers 1979 TFLOPS FP16, dwarfing RTX 4000 Ada's 26.7 TFLOPS. This gap accelerates AI training on H100. Inference also favors H100 for high throughput.
Which has higher memory bandwidth?▾
H100 provides 3350 GB/s, versus RTX 4000 Ada's 360 GB/s. Higher bandwidth on H100 supports larger batch sizes in training. RTX 4000 Ada handles smaller data flows adequately.
What are the TDP ratings?▾
H100 has a 700W TDP for datacenter use, while RTX 4000 Ada uses 130W for workstations. Lower TDP makes RTX 4000 Ada easier to cool. H100 requires robust infrastructure.
Can RTX 4000 Ada replace H100 for AI training?▾
No, RTX 4000 Ada's 20 GB VRAM and 26.7 TFLOPS FP16 cannot match H100's scale for large models. Use RTX 4000 Ada for prototyping only. H100 is essential for production training.
Which is cheaper to rent, the H100 or the RTX 4000 Ada?▾
Cloud rental prices for both the H100 and RTX 4000 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 4000 Ada?▾
The H100 has 80 to 94 GB of HBM3 memory. The RTX 4000 Ada has 20 GB of GDDR6 memory.
Can I find H100 and RTX 4000 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 4000 Ada?▾
The H100 uses the Hopper architecture (2022) while the RTX 4000 Ada uses Ada Lovelace (2023). The H100 delivers 74.1x the FP16 throughput and 9.3x the memory bandwidth of the RTX 4000 Ada.