Specifications Compared
| Spec | A100 | RTX-2070 |
|---|---|---|
| TDP | 400W | 175W |
| VRAM | 40-80 GB | 8 GB |
| CUDA Cores | 6,912 | 2,304 |
| Memory Type | HBM2e | GDDR6 |
| Architecture | Ampere | Turing |
| Form Factors | SXM4, PCIe | PCIe |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | NVLink |
| Tensor Cores | 432 | 288 |
| FP16 Performance | 312 TFLOPS | 7.5 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 7.5 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | |
| INT8 Performance | 624 TOPS | |
| Memory Bandwidth | 2,039 GB/s | 448 GB/s |
Performance Analysis
Compute disparities dominate: the A100 SXM4 40GB achieves 312 TFLOPS in FP16 versus 9.1 TFLOPS on the RTX 2070 SUPER, enabling faster AI model training where half-precision dominates. FP32 performance shows 19.5 TFLOPS for A100 against 9.1 TFLOPS for SUPER, benefiting general-purpose computing but amplifying A100's edge in mixed workloads. This delta translates to training times reduced by factors of 30 or more for large neural networks on A100.
Memory specs reveal stark contrasts: 2039 GB/s bandwidth and 40 GB VRAM on A100 support massive batch sizes in deep learning, preventing out-of-memory errors for models exceeding 8 GB. The RTX 2070 SUPER's 496 GB/s and 8 GB cap throughput, restricting it to smaller batches or models during inference and fine-tuning. Higher bandwidth on A100 sustains higher utilization in memory-bound tasks like transformer processing.
Power efficiency varies with 400W TDP for A100 versus 215W for SUPER, but A100's raw output per watt excels in datacenter scaling via NVLink.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A100 SXM4 40GB
| 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×) |
When to Choose the A100 SXM4 40GB
Opt for the NVIDIA A100 SXM4 40GB in professional AI training and large-scale inference where 40 GB VRAM handles billion-parameter LLMs without splitting. Its 312 TFLOPS FP16 and 2039 GB/s bandwidth excel in cloud environments for teams needing rapid iterations, available from $1.00 per hour.
Datacenter deployments benefit from SXM4 form factor and NVLink for multi-GPU synchronization in HPC simulations.
When to Choose the RTX 2070 SUPER
Select the NVIDIA GeForce RTX 2070 SUPER for local gaming setups or lightweight ML prototyping on desktops with PCIe compatibility. Its 215W TDP and 8 GB VRAM suffice for Stable Diffusion at 512x512 resolutions or small model inference under 9.1 TFLOPS FP32.
Budget-conscious hobbyists prefer it absent cloud fees, though no live rental offers exist.
Use Cases
A100 SXM4 40GB's 40 GB VRAM and 312 TFLOPS FP16 support large batch training of LLMs exceeding 8 GB models. RTX 2070 SUPER cannot accommodate such scales due to 8 GB GDDR6 limits.
The A100's 2039 GB/s bandwidth enables high-throughput serving of LLMs with large contexts. RTX 2070 SUPER's 496 GB/s bottlenecks inference for models over 7B parameters.
A100 handles fine-tuning of 30B+ parameter models with 19.5 TFLOPS FP32 and ample VRAM. RTX 2070 SUPER restricts to smaller models under 8 GB.
RTX 2070 SUPER's 9.1 TFLOPS FP16 suffices for 512x512 image generation at interactive speeds on desktops. A100 overkill for single-user creative tasks.
A100's NVLink and 400W scalability accelerate simulations with 2039 GB/s bandwidth. RTX 2070 SUPER lacks interconnects for distributed scientific workloads.
Frequently Asked Questions
What is the VRAM difference between A100 SXM4 40GB and RTX 2070 SUPER?▾
The A100 SXM4 40GB provides 40 GB HBM2e VRAM. The RTX 2070 SUPER has 8 GB GDDR6. This fivefold gap allows A100 to process vastly larger datasets.
How do FP16 performances compare?▾
A100 SXM4 40GB delivers 312 TFLOPS in FP16. RTX 2070 SUPER reaches 9.1 TFLOPS. A100 accelerates AI training by over 34 times in half-precision tasks.
What are the cloud pricing details?▾
NVIDIA A100 SXM4 40GB starts at $1.00 per hour, averaging $2.63 per hour across five offers. No live cloud offers exist for RTX 2070 SUPER.
Which has higher memory bandwidth?▾
A100 SXM4 40GB offers 2039 GB/s. RTX 2070 SUPER provides 496 GB/s. A100's bandwidth supports larger batches in ML workloads.
What are the TDP ratings?▾
A100 SXM4 40GB consumes 400W TDP. RTX 2070 SUPER uses 215W. Lower TDP makes SUPER suitable for consumer power supplies.
Can RTX 2070 SUPER replace A100 for ML training?▾
RTX 2070 SUPER cannot replace A100 due to 8 GB VRAM versus 40 GB and 9.1 TFLOPS FP16 against 312 TFLOPS. It suits only small-scale training.
Which is cheaper to rent, the A100 or the RTX 2070?▾
Cloud rental prices for both the A100 and RTX 2070 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 2070?▾
The A100 has 40 to 80 GB of HBM2e memory. The RTX 2070 has 8 GB of GDDR6 memory.
Can I find A100 and RTX 2070 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 2070?▾
The A100 uses the Ampere architecture (2020) while the RTX 2070 uses Turing (2018). The A100 delivers 41.6x the FP16 throughput and 4.6x the memory bandwidth of the RTX 2070.