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 define real-world performance: the A100's 312 TFLOPS FP16 capability accelerates half-precision training by over 40 times compared to the RTX 2070's 7.5 TFLOPS, enabling faster convergence in deep learning models. FP32 performance at 19.5 TFLOPS on A100 versus 7.5 TFLOPS on RTX 2070 suits general-purpose simulations, though the gap narrows there. This delta means A100 handles large-scale neural network training efficiently, while RTX 2070 suits lighter inference or prototyping.
Memory specifications impact batch sizes profoundly: A100's 40-80 GB HBM2e VRAM supports models exceeding 8 GB, such as large language models, without splitting across devices. Its 2039 GB/s bandwidth minimizes bottlenecks during data-intensive operations like gradient updates. RTX 2070's 448 GB/s and 8 GB limit it to smaller batches, risking out-of-memory errors in complex workloads.
Power draw reflects intent: A100's 400W TDP demands robust cooling for sustained peaks, contrasting RTX 2070's 175W for desktop efficiency. Interconnects enhance A100 scalability via PCIe 4.0 and InfiniBand, vital for multi-GPU clusters.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A100
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vast.ai | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 63GB RAM 2826GB Storage | Slovenia | $0.73/GPU/hr | Available | ||
Vast.ai | 2×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 126GB RAM 794GB Storage | Slovenia | $0.73/GPU/hr $1.47/hr total (2×) | 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 | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 64 vCPU 63GB RAM 646GB Storage | Czechia | $1.07/GPU/hr | Available | ||
 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
Choose the A100 for enterprise-scale AI training where models demand over 8 GB VRAM, such as transformer-based LLMs: its 40-80 GB capacity and 312 TFLOPS FP16 enable batch sizes impossible on RTX 2070. High memory bandwidth of 2039 GB/s supports rapid data throughput in distributed setups via NVLink or InfiniBand.
Scientific computing or inference on massive datasets favors A100, leveraging 19.5 TFLOPS FP32 and 57 cloud offers starting at $0.45 per hour for reliable availability.
When to Choose the RTX 2070
Opt for RTX 2070 in budget-constrained prototyping or gaming-adjacent tasks: its $0.02 per hour starting price across offers makes experimentation accessible. The 8 GB GDDR6 suffices for fine-tuning small models under 7.5 TFLOPS FP16/FP32.
Light inference or Stable Diffusion on modest hardware suits RTX 2070, with 175W TDP fitting low-power cloud instances without A100's 400W overhead.
Use Cases
A100's 40-80 GB VRAM and 312 TFLOPS FP16 handle large models without fragmentation. RTX 2070's 8 GB limits scale.
2039 GB/s bandwidth on A100 supports high-throughput serving. RTX 2070's 448 GB/s bottlenecks larger batches.
Smaller models fit RTX 2070's 8 GB at 7.5 TFLOPS. A100 excels for parameter-heavy fine-tuning with 19.5 TFLOPS FP32.
RTX 2070's 8 GB GDDR6 runs standard resolutions efficiently at low $0.04 per hour average. A100 overkill for single-image generation.
A100's 19.5 TFLOPS FP32 and InfiniBand scaling suit simulations. RTX 2070's 7.5 TFLOPS constrains complex datasets.
Frequently Asked Questions
Is A100 better than RTX 2070 for AI training?▾
Yes, A100 outperforms with 312 TFLOPS FP16 versus 7.5 TFLOPS and 40-80 GB VRAM against 8 GB. This enables larger models and faster iterations.
What is the price difference for A100 vs RTX 2070 in cloud?▾
A100 starts at $0.45 per hour averaging $1.92 across 57 offers. RTX 2070 begins at $0.02 per hour averaging $0.04 over 2 offers.
Can RTX 2070 handle large language models?▾
RTX 2070's 8 GB VRAM limits it to small LLMs or low batches at 7.5 TFLOPS FP16. A100's 40-80 GB is required for full-scale.
How does memory bandwidth compare?▾
A100 provides 2039 GB/s with HBM2e, far exceeding RTX 2070's 448 GB/s GDDR6. This boosts data-heavy workloads significantly.
What are the power requirements?▾
A100 draws 400W TDP for peak performance. RTX 2070 uses 175W, suiting lighter or power-sensitive deployments.
Which has better interconnects?▾
A100 supports NVLink, PCIe 4.0, and InfiniBand for multi-GPU scaling. RTX 2070 offers PCIe and NVLink but lacks enterprise breadth.
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.