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
The A100 SXM4 40GB dominates in FP16 performance at 312 TFLOPS compared to the RTX 3070's 20.3 TFLOPS, accelerating deep learning training that relies on half-precision computations by up to 15 times. FP32 performance remains close at 19.5 TFLOPS for A100 versus 20.3 TFLOPS for RTX 3070, but the A100's tensor cores enable efficient mixed-precision workflows essential for large model optimization. This disparity translates to faster convergence in training cycles on the A100. Memory capacity and bandwidth profoundly impact real-world usage: the A100's 40 GB HBM2e and 2039 GB/s bandwidth support batch sizes exceeding those feasible on the RTX 3070's 8 GB GDDR6 and 448 GB/s, minimizing out-of-memory errors in transformer models. Higher bandwidth reduces data transfer bottlenecks during inference, allowing sustained throughput. Power draw at 400W TDP for A100 versus 220W for RTX 3070 influences deployment scalability in dense cloud environments.
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 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 SXM4 40GB
The A100 SXM4 40GB excels in large-scale LLM training and scientific simulations requiring over 8 GB VRAM. Its 312 TFLOPS FP16 and 2039 GB/s bandwidth handle massive datasets and batch sizes that crash on the RTX 3070. Enterprise users prioritize it for production inference where 40 GB HBM2e ensures reliability at $1.00 to $2.63 per hour.
When to Choose the RTX 3070
The RTX 3070 suits budget-conscious hobbyists for Stable Diffusion or small fine-tuning tasks fitting within 8 GB GDDR6. At $0.04 to $0.09 per hour, it delivers 20.3 TFLOPS FP32 for gaming or lightweight inference without NVLink needs. Developers testing prototypes choose it to minimize costs before scaling.
Use Cases
LLM training demands over 8 GB VRAM and high FP16 throughput; the A100's 40 GB HBM2e and 312 TFLOPS enable large batch sizes, unlike the RTX 3070's limitations.
Inference on large models requires substantial memory bandwidth; the A100's 2039 GB/s supports high concurrency, far exceeding the RTX 3070's 448 GB/s.
Fine-tuning mid-sized models benefits from the A100's 312 TFLOPS FP16 for rapid iterations; the RTX 3070's 8 GB VRAM restricts dataset sizes.
Stable Diffusion runs efficiently on 8 GB GDDR6 with 20.3 TFLOPS; the RTX 3070's low $0.09 per hour cost suits creative prototyping without A100 overhead.
Scientific simulations leverage the A100's 40 GB VRAM and NVLink interconnects for parallel processing; the RTX 3070 lacks capacity for complex datasets.
Frequently Asked Questions
What is the VRAM difference between A100 SXM4 40GB and RTX 3070?▾
The A100 SXM4 40GB offers 40 GB HBM2e VRAM, while the RTX 3070 provides 8 GB GDDR6. This fivefold capacity gap allows the A100 to manage larger models without swapping. Memory bandwidth follows suit at 2039 GB/s versus 448 GB/s.
How do FP16 performances compare?▾
The A100 achieves 312 TFLOPS FP16, dwarfing the RTX 3070's 20.3 TFLOPS. This boosts training speed in half-precision tasks by over 15 times on the A100. FP32 is nearer at 19.5 TFLOPS versus 20.3 TFLOPS.
What are the cloud pricing differences?▾
A100 SXM4 40GB starts at $1.00 per hour averaging $2.63 across 5 offers. RTX 3070 begins at $0.04 per hour averaging $0.09 across 4 offers. Budget users favor RTX 3070 for light workloads.
Is the A100 better for AI training?▾
Yes, the A100's 312 TFLOPS FP16 and 40 GB VRAM excel in AI training versus RTX 3070's 20.3 TFLOPS and 8 GB. It handles larger batches and faster epochs. Consumer tasks may not require this power.
What are the TDP ratings?▾
The A100 SXM4 40GB has a 400W TDP, compared to the RTX 3070's 220W. Higher TDP enables the A100's superior compute but demands robust cooling. RTX 3070 suits power-sensitive setups.
Can RTX 3070 handle machine learning?▾
The RTX 3070 manages small-scale ML with 20.3 TFLOPS and 8 GB VRAM, ideal for prototyping. It falters on large models needing more than 448 GB/s bandwidth. A100 is preferable for production.
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.