Specifications Compared
| Spec | A100 | RTX-3070 |
|---|---|---|
| TDP | 400W | 220W |
| VRAM | 40-80 GB | 8 GB |
| CUDA Cores | 6,912 | 5,888 |
| Memory Type | HBM2e | GDDR6 |
| Architecture | Ampere | Ampere |
| Form Factors | SXM4, PCIe | PCIe |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | |
| Tensor Cores | 432 | 184 |
| FP16 Performance | 312 TFLOPS | 20.3 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 20.3 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | |
| INT8 Performance | 624 TOPS | |
| Memory Bandwidth | 2,039 GB/s | 448 GB/s |
Performance Analysis
Memory capacity defines primary limitations: the A100's 80 GB HBM2e supports models with billions of parameters, enabling full precision loading for transformers up to 175 billion parameters, whereas the RTX 3070's 8 GB GDDR6 restricts users to smaller models or heavy quantization. This gap extends to memory bandwidth, where 2039 GB/s on the A100 sustains larger batch sizes during training, reducing iterations and time to convergence, compared to 448 GB/s on the RTX 3070 which bottlenecks data movement in memory-intensive tasks.
FP16 performance reveals specialization: the A100 achieves 312 TFLOPS, accelerating half-precision training and inference by up to 15 times over the RTX 3070's 20.3 TFLOPS, ideal for modern deep learning pipelines favoring mixed precision. FP32 rates are close at 19.5 TFLOPS for A100 and 20.3 TFLOPS for RTX 3070, making the consumer GPU competitive in single-precision scientific simulations but irrelevant for scaled AI. Higher 400W TDP on the A100 ensures sustained peaks under prolonged loads, unlike the 220W RTX 3070 prone to thermal throttling.
These differences translate to real-world throughput: A100 clusters scale via NVLink and PCIe 4.0 for distributed training, while RTX 3070 suits single-node prototyping.
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
The A100 PCIe 80GB excels in enterprise-scale AI training and inference. Its 80 GB HBM2e VRAM and 2039 GB/s bandwidth accommodate large language models without offloading, supporting batch sizes that minimize training time. Datacenter features like NVLink enable multi-GPU scaling for datasets exceeding terabytes.
High FP16 performance at 312 TFLOPS accelerates mixed-precision workflows in research labs or production serving billions of tokens daily.
When to Choose the RTX 3070
The RTX 3070 fits budget-limited prototyping and lightweight inference. At $0.04 per hour average $0.09 per hour, it undercuts A100 pricing by over 20 times, ideal for hobbyists or small teams testing models under 7 billion parameters on 8 GB GDDR6.
Balanced 20.3 TFLOPS FP32 suits gaming-integrated ML or scientific visualization where 220W TDP aligns with desktop constraints.
Use Cases
A100's 80 GB HBM2e VRAM and 2039 GB/s bandwidth handle massive models and large batches; RTX 3070's 8 GB GDDR6 limits scale.
312 TFLOPS FP16 on A100 delivers high throughput for production serving; RTX 3070 suffices only for tiny models.
A100 supports full-model fine-tuning with 80 GB VRAM; 8 GB on RTX 3070 requires excessive quantization.
RTX 3070's 8 GB GDDR6 generates images efficiently at low $0.04 per hour cost; A100 overkill for consumer diffusion.
A100's 80 GB VRAM and NVLink scaling suit simulations; RTX 3070's 220W TDP limits sustained FP32 workloads.
Frequently Asked Questions
Which GPU has more VRAM: A100 PCIe 80GB or RTX 3070?▾
The A100 PCIe 80GB provides 80 GB HBM2e VRAM. The RTX 3070 offers 8 GB GDDR6. This tenfold difference enables A100 for large models.
What are the cloud rental prices for these GPUs?▾
A100 PCIe 80GB starts at $0.89 per hour, averaging $2.08 per hour across 28 offers. RTX 3070 begins at $0.04 per hour, averaging $0.09 per hour across 4 offers. RTX 3070 costs under 5% of A100 rates.
Which is better for AI training?▾
A100 dominates with 312 TFLOPS FP16 and 2039 GB/s bandwidth. RTX 3070's 20.3 TFLOPS FP16 suits only small-scale training.
How do power consumptions compare?▾
A100 TDP is 400W for sustained datacenter performance. RTX 3070 TDP is 220W, fitting consumer setups.
Do they share the same architecture?▾
Both use Ampere from 2020. A100 optimizes for enterprise with NVLink; RTX 3070 prioritizes gaming.
Can RTX 3070 handle machine learning?▾
RTX 3070 manages prototyping with 20.3 TFLOPS FP32 on 8 GB VRAM. It falters on models needing over 8 GB.
Which is cheaper to rent, the A100 or the RTX 3070?▾
Cloud rental prices for both the A100 and RTX 3070 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 3070?▾
The A100 has 40 to 80 GB of HBM2e memory. The RTX 3070 has 8 GB of GDDR6 memory.
Can I find A100 and RTX 3070 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 3070?▾
The A100 uses the Ampere architecture (2020) while the RTX 3070 uses Ampere (2020). The A100 delivers 15.4x the FP16 throughput and 4.6x the memory bandwidth of the RTX 3070.