Specifications Compared
| Spec | H100 | RTX-4070 |
|---|---|---|
| TDP | 700W | 200W |
| VRAM | 80-94 GB | 12 GB |
| CUDA Cores | 16,896 | 5,888 |
| Memory Type | HBM3 | GDDR6X |
| Architecture | Hopper | Ada Lovelace |
| Form Factors | SXM5, PCIe, NVL | PCIe |
| Interconnect | NVLink, PCIe 5.0, InfiniBand | |
| Tensor Cores | 528 | 184 |
| FP8 Performance | 3,958 TFLOPS | |
| FP16 Performance | 1,979 TFLOPS | 29.1 TFLOPS |
| FP32 Performance | 67 TFLOPS | 29.1 TFLOPS |
| FP64 Performance | 34 TFLOPS | |
| INT8 Performance | 3,958 TOPS | 466 TOPS |
| Memory Bandwidth | 3,350 GB/s | 504 GB/s |
Performance Analysis
The H100's FP16 performance of 1979 TFLOPS vastly outpaces the RTX 4070's 29.1 TFLOPS, enabling faster neural network training where half-precision computations dominate. This delta translates to training large models in hours rather than days: for instance, the H100 handles massive datasets without precision loss in mixed workflows. The FP32 rating of 67 TFLOPS on H100 versus 29.1 TFLOPS on RTX 4070 benefits simulation-heavy tasks requiring single-precision accuracy.
Memory specifications dictate real-world scalability. With 80 to 94 GB HBM3 and 3350 GB/s bandwidth, the H100 supports enormous batch sizes in training, reducing iterations and wall-clock time. The RTX 4070's 12 GB GDDR6X at 504 GB/s limits it to smaller batches, risking out-of-memory errors for models exceeding 10 billion parameters. Inference benefits similarly: H100's FP8 at 3958 TFLOPS yields high throughput for serving, while RTX 4070 suits low-latency single queries.
Power draw underscores efficiency contexts. The H100's 700W TDP demands robust cooling and infrastructure, yet delivers superior flops per watt in datacenter settings. The RTX 4070's 200W suits edge deployments with minimal overhead.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
H100
| 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 4070
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA GeForce RTX 4070 Ti 12GB VRAM | 12GB | 6 vCPU 30GB RAM | 🌍global | $0.50/GPU/hr |
When to Choose the H100
The H100 excels in enterprise AI training and large-scale inference where VRAM exceeds 50 GB is essential. Its 80 to 94 GB HBM3 capacity and 3350 GB/s bandwidth enable processing of models like 175 billion parameter LLMs without sharding, across NVLink or InfiniBand interconnects. Cloud users facing deadlines prioritize its 1979 TFLOPS FP16 for rapid iterations.
High-performance computing clusters favor the H100's SXM5 or NVL form factors and 67 TFLOPS FP32 for scientific simulations requiring distributed scaling.
When to Choose the RTX 4070
The RTX 4070 fits budget-conscious prototyping and small-scale inference with its 12 GB GDDR6X at $0.07 per hour starting price. It handles fine-tuning of models under 7 billion parameters efficiently via 29.1 TFLOPS FP16, ideal for individual developers or SMBs. Gaming-integrated workflows leverage its PCIe form factor seamlessly.
Cost-sensitive generative tasks like lightweight Stable Diffusion benefit from 504 GB/s bandwidth without overprovisioning power at 200W TDP.
Use Cases
LLM training demands over 50 GB VRAM for large models: H100 provides 80 to 94 GB HBM3 versus RTX 4070's 12 GB. Its 1979 TFLOPS FP16 accelerates convergence significantly.
High-throughput inference requires FP8 performance of 3958 TFLOPS on H100 for serving billions of tokens. RTX 4070's 29.1 TFLOPS limits it to small-scale deployments.
Fine-tuning mid-sized models benefits from H100's 3350 GB/s bandwidth for large batches. The 12 GB on RTX 4070 restricts dataset sizes and efficiency.
Stable Diffusion runs effectively on 12 GB GDDR6X with 29.1 TFLOPS FP16 at $0.07 per hour. H100's 700W TDP overkill for consumer image generation.
Scientific tasks leverage H100's 67 TFLOPS FP32 and NVLink interconnect for distributed simulations. RTX 4070's 29.1 TFLOPS falls short in precision-heavy workloads.
Frequently Asked Questions
What is the VRAM difference between H100 and RTX 4070?▾
The H100 offers 80 to 94 GB HBM3 VRAM, enabling large model handling. The RTX 4070 provides 12 GB GDDR6X, suitable for smaller datasets. This gap affects batch sizes in training.
How do cloud prices compare for H100 vs RTX 4070?▾
H100 rentals start at $0.80 per hour, averaging $3.14 across 57 offers. RTX 4070 begins at $0.07 per hour, averaging $0.19 over 9 offers. Pricing scales with performance tiers.
What are the FP16 performance specs?▾
H100 delivers 1979 TFLOPS in FP16 for rapid AI computations. RTX 4070 achieves 29.1 TFLOPS, adequate for consumer tasks. The difference impacts training speed by orders of magnitude.
Which has higher memory bandwidth?▾
H100 provides 3350 GB/s with HBM3, supporting massive data flows. RTX 4070 offers 504 GB/s via GDDR6X for moderate workloads. Bandwidth dictates inference throughput.
What is the power consumption of each GPU?▾
H100 requires 700W TDP, suited for datacenter cooling. RTX 4070 uses 200W, ideal for desktop or edge use. Efficiency varies by workload scale.
Can RTX 4070 handle LLM fine-tuning?▾
RTX 4070 manages fine-tuning for models under 7B parameters with 12 GB VRAM. Larger tasks exceed its capacity, favoring H100's 80 to 94 GB.
Which is cheaper to rent, the H100 or the RTX 4070?▾
Cloud rental prices for both the H100 and RTX 4070 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 4070?▾
The H100 has 80 to 94 GB of HBM3 memory. The RTX 4070 has 12 GB of GDDR6X memory.
Can I find H100 and RTX 4070 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 4070?▾
The H100 uses the Hopper architecture (2022) while the RTX 4070 uses Ada Lovelace (2023). The H100 delivers 68.0x the FP16 throughput and 6.6x the memory bandwidth of the RTX 4070.