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 capabilities define the core disparity: the A100 achieves 312 TFLOPS in FP16 for accelerated neural network training, far exceeding the RTX 2070's 7.5 TFLOPS, which limits it to smaller-scale operations. FP32 performance follows suit at 19.5 TFLOPS on A100 versus 7.5 TFLOPS on RTX 2070, benefiting simulations and inference requiring higher precision. This delta means training times on A100 shrink dramatically for deep learning workloads, while RTX 2070 suffices for prototyping.
Memory specifications profoundly impact real-world usage. The A100's 80 GB HBM2e VRAM supports massive batch sizes in model training, avoiding out-of-memory errors common with RTX 2070's 8 GB GDDR6. Bandwidth at 2039 GB/s on A100 versus 448 GB/s on RTX 2070 accelerates data transfers, enabling larger models and faster iterations in inference pipelines. Higher TDP of 400W on A100 reflects its density, contrasting RTX 2070's efficient 175W for lighter loads.
Interconnects further the gap: A100 leverages NVLink, PCIe 4.0, and InfiniBand for multi-GPU scaling, absent in RTX 2070's basic PCIe setup.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A100 PCIe 80GB
| 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 PCIe 80GB
Enterprises tackling large-scale AI training select the A100 PCIe 80GB: its 312 TFLOPS FP16 and 80 GB VRAM handle LLMs and datasets infeasible on consumer cards. High memory bandwidth of 2039 GB/s supports enormous batch sizes, cutting training epochs.
Inference at scale favors A100 too, with 19.5 TFLOPS FP32 for precise, high-throughput serving across clusters via NVLink and InfiniBand.
When to Choose the RTX 2070
Budget-conscious developers prototyping models choose the RTX 2070: at $0.02 per hour average $0.04, it delivers 7.5 TFLOPS FP16 for small fine-tuning or inference without excess cost.
Gaming or light creative tasks suit its 8 GB VRAM and 175W TDP, offering quick setups in PCIe form factors for non-datacenter needs.
Use Cases
LLM training demands 80 GB VRAM and 312 TFLOPS FP16 on A100 to manage massive parameters and batches. RTX 2070's 8 GB VRAM causes frequent out-of-memory issues.
High-throughput inference benefits from A100's 2039 GB/s bandwidth and 19.5 TFLOPS FP32 for serving large models. RTX 2070 handles only modest loads with 448 GB/s.
Fine-tuning large models requires A100's 80 GB VRAM to avoid splitting batches. Its 312 TFLOPS FP16 speeds convergence over RTX 2070's 7.5 TFLOPS.
Stable Diffusion runs adequately on RTX 2070's 8 GB VRAM for standard resolutions. A100 excels for high-res or batched generations with superior bandwidth.
Simulations leverage A100's 19.5 TFLOPS FP32 and NVLink scaling for complex datasets. RTX 2070's 7.5 TFLOPS limits scope to simpler computations.
Frequently Asked Questions
What is the VRAM difference between A100 PCIe 80GB and RTX 2070?▾
The A100 provides 80 GB HBM2e VRAM, compared to 8 GB GDDR6 on RTX 2070. This enables A100 to load models 10 times larger without swapping.
How do cloud prices compare for these GPUs?▾
A100 PCIe 80GB starts at $0.89 per hour, averaging $2.08 across 28 offers. RTX 2070 begins at $0.02 per hour, averaging $0.04 across 2 offers.
Which has higher FP16 performance?▾
A100 delivers 312 TFLOPS FP16, vastly outperforming RTX 2070's 7.5 TFLOPS. This accelerates AI training by over 40 times in compatible workloads.
Is RTX 2070 suitable for ML training?▾
RTX 2070's 7.5 TFLOPS FP16 and 8 GB VRAM work for small models or prototyping. Larger tasks exceed its capacity, favoring A100's specs.
What are the power requirements?▾
A100 draws 400W TDP, suited for datacenter cooling. RTX 2070 uses 175W, ideal for consumer or edge setups.
Can these GPUs scale in multi-GPU setups?▾
A100 supports NVLink, PCIe 4.0, and InfiniBand for efficient scaling. RTX 2070 relies on basic PCIe, limiting cluster performance.
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.