Specifications Compared
| Spec | A100 | RTX-4070 |
|---|---|---|
| TDP | 400W | 200W |
| VRAM | 40-80 GB | 12 GB |
| CUDA Cores | 6,912 | 5,888 |
| Memory Type | HBM2e | GDDR6X |
| Architecture | Ampere | Ada Lovelace |
| Form Factors | SXM4, PCIe | PCIe |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | |
| Tensor Cores | 432 | 184 |
| FP16 Performance | 312 TFLOPS | 29.1 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 29.1 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | |
| INT8 Performance | 624 TOPS | 466 TOPS |
| Memory Bandwidth | 2,039 GB/s | 504 GB/s |
Performance Analysis
The A100 SXM4 80GB dominates in FP16 performance at 312 TFLOPS compared to 29.1 TFLOPS on the RTX 4070 Ti SUPER: this gap accelerates mixed-precision training for large neural networks, reducing time for forward and backward passes. In FP32, the RTX 4070 Ti SUPER matches its FP16 at 29.1 TFLOPS against the A100's 19.5 TFLOPS, providing an edge for single-precision inference or simulations less reliant on half-precision.
Memory bandwidth reveals a clear divide: 2039 GB/s on the A100 supports massive batch sizes for training billion-parameter models, minimizing data starvation, whereas 504 GB/s on the RTX 4070 Ti SUPER limits scalability for memory-bound workloads. The A100's 400W TDP enables sustained high throughput in clusters via NVLink and InfiniBand, while the RTX 4070 Ti SUPER's 200W suits efficient single-node use.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A100 SXM4 80GB
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vast.ai | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 63GB RAM 397GB Storage | Slovenia | $0.73/GPU/hr | Available | ||
 LeaderGPU | 8×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 64 vCPU 384GB RAM 2000GB Storage | Netherlands | $0.90/GPU/hr $7.20/hr total (8×) | Available | ||
Vast.ai | 2×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 64 vCPU 126GB RAM 1114GB Storage | Czechia | $1.00/GPU/hr $2.00/hr total (2×) | Available | ||
 Denvr | 4×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 64 vCPU 512GB RAM 7600GB Storage | Virginia | $1.15/GPU/hr $4.60/hr total (4×) | |||
Denvr | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 128 vCPU 1024GB RAM 15200GB Storage | Virginia | $1.15/GPU/hr $9.20/hr total (8×) |
RTX 4070 Ti SUPER
| 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 A100 SXM4 80GB
Select the A100 SXM4 80GB for large-scale LLM training or scientific simulations requiring over 40 GB VRAM: its 80 GB HBM2e capacity loads entire models without partitioning, and 2039 GB/s bandwidth handles batch sizes infeasible on 12 GB setups. High FP16 at 312 TFLOPS speeds convergence in data center environments with NVLink interconnects.
When to Choose the RTX 4070 Ti SUPER
Opt for the RTX 4070 Ti SUPER in cost-sensitive scenarios like prototyping or small inference deployments: pricing from $0.09 per hour enables experimentation at one-fifth the A100's average cost. Its 29.1 TFLOPS FP32 and Ada Lovelace efficiency suffice for models fitting in 12 GB GDDR6X, with 200W TDP reducing power expenses.
Use Cases
The A100 SXM4 80GB's 80 GB HBM2e VRAM and 312 TFLOPS FP16 support training billion-parameter models with large batches. The RTX 4070 Ti SUPER's 12 GB limits model size.
A100's 2039 GB/s bandwidth enables high-throughput serving of large models. RTX 4070 Ti SUPER suits smaller models but bottlenecks on memory demands.
80 GB VRAM on A100 accommodates full model loading during fine-tuning. 312 TFLOPS FP16 accelerates iterations over RTX 4070 Ti SUPER's 29.1 TFLOPS.
RTX 4070 Ti SUPER's Ada Lovelace architecture and 29.1 TFLOPS FP32 excel in generative tasks at $0.09 per hour. Lower 12 GB VRAM suffices for typical image generation.
A100's 2039 GB/s bandwidth and NVLink handle large datasets in simulations. High FP16 performance outperforms RTX 4070 Ti SUPER in HPC-scale computations.
Frequently Asked Questions
What is the VRAM capacity of each GPU?▾
The A100 SXM4 80GB offers 80 GB HBM2e VRAM. The RTX 4070 Ti SUPER provides 12 GB GDDR6X VRAM. This difference impacts handling of large AI models.
How do their memory bandwidths compare?▾
A100 SXM4 80GB delivers 2039 GB/s bandwidth. RTX 4070 Ti SUPER achieves 504 GB/s. Higher bandwidth on A100 supports larger batch sizes in training.
What are the FP16 performance figures?▾
A100 SXM4 80GB reaches 312 TFLOPS in FP16. RTX 4070 Ti SUPER provides 29.1 TFLOPS. A100 excels in mixed-precision AI workloads.
What are the current cloud pricing ranges?▾
A100 SXM4 80GB starts at $0.45 per hour, averaging $1.39 per hour across 25 offers. RTX 4070 Ti SUPER begins at $0.09 per hour, averaging $0.17 per hour across 2 offers.
Which GPU has higher TDP?▾
A100 SXM4 80GB consumes 400W TDP. RTX 4070 Ti SUPER uses 200W TDP. Higher TDP on A100 correlates with greater sustained performance.
What architectures do they use?▾
A100 SXM4 80GB employs Ampere from 2020. RTX 4070 Ti SUPER uses Ada Lovelace from 2023. Newer architecture on RTX aids efficiency in consumer tasks.
Which is cheaper to rent, the A100 or the RTX 4070?▾
Cloud rental prices for both the A100 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 A100 have compared to the RTX 4070?▾
The A100 has 40 to 80 GB of HBM2e memory. The RTX 4070 has 12 GB of GDDR6X memory.
Can I find A100 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 A100 and the RTX 4070?▾
The A100 uses the Ampere architecture (2020) while the RTX 4070 uses Ada Lovelace (2023). The A100 delivers 10.7x the FP16 throughput and 4.0x the memory bandwidth of the RTX 4070.