Specifications Compared
| Spec | A100 | RTX-4070 |
|---|---|---|
| TDP | 400W | 200W |
| VRAM | 40-80 GB | 12 GB |
| CUDA Cores | 6,912 | 5,888 |
| Memory Type | HBM2e | GDDR6X |
| Architecture | Ampere | Ada Lovelace |
| Form Factors | SXM4, PCIe | PCIe |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | |
| Tensor Cores | 432 | 184 |
| FP16 Performance | 312 TFLOPS | 29.1 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 29.1 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | |
| INT8 Performance | 624 TOPS | 466 TOPS |
| Memory Bandwidth | 2,039 GB/s | 504 GB/s |
Performance Analysis
The A100 SXM4 80GB excels in memory-intensive AI tasks due to its 80 GB HBM2e VRAM and 2039 GB/s bandwidth, enabling larger batch sizes in training compared to the RTX 4070 SUPER's 12 GB GDDR6X and 504 GB/s. This disparity limits the 4070 SUPER to smaller models or reduced batches, often requiring model sharding. FP16 performance defines training efficiency: the A100 delivers 312 TFLOPS via tensor cores, nearly nine times the 4070 SUPER's 35.5 TFLOPS, accelerating half-precision computations central to deep learning. For inference, high FP16 sustains high throughput on large language models. The 4070 SUPER edges FP32 at 35.5 TFLOPS over the A100's 19.5 TFLOPS, suiting graphics or simulations needing single-precision. However, the A100's bandwidth mitigates FP32 gaps by reducing data stalls. Power draw reveals trade-offs: 400W for A100 versus 220W supports dense cloud deployments but demands cooling, while the 4070 SUPER fits local setups efficiently.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A100 SXM4 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×) |
RTX 4070 SUPER
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA GeForce RTX 4070 Ti 12GB VRAM | 12GB | 6 vCPU 30GB RAM | 🌍global | $0.50/GPU/hr |
When to Choose the A100 SXM4 80GB
The A100 SXM4 80GB suits enterprise AI training and inference for models exceeding 12 GB VRAM, such as large LLMs, where 80 GB HBM2e and 2039 GB/s bandwidth support massive batches without splitting. Cloud availability from $0.45 per hour enables scalable clusters via NVLink. It outperforms in FP16-heavy workloads at 312 TFLOPS, ideal for research or production HPC.
When to Choose the RTX 4070 SUPER
The RTX 4070 SUPER fits local workstations for gaming, creative tasks, or small-scale AI like Stable Diffusion inference, leveraging 35.5 TFLOPS FP32 and 220W TDP for cost-effective desktop use. Its Ada Lovelace architecture provides modern RT and tensor cores without cloud dependency. Choose it for budgets avoiding A100's $1.39 per hour average or 400W infrastructure.
Use Cases
The A100 SXM4 80GB handles massive models with 80 GB HBM2e VRAM and 312 TFLOPS FP16, supporting large batch sizes via 2039 GB/s bandwidth. The 4070 SUPER's 12 GB limits it to small-scale training.
High VRAM and FP16 performance on the A100 enable efficient serving of large LLMs at scale. The 4070 SUPER suffices for lightweight inference but struggles with memory demands.
Fine-tuning benefits from the A100's 80 GB VRAM for full model loading and 2039 GB/s bandwidth for fast iterations. Consumer GPUs like the 4070 SUPER require gradient checkpointing on 12 GB.
The RTX 4070 SUPER excels in generative AI with 35.5 TFLOPS FP32/FP16 and efficient 220W TDP for local runs. Cloud A100 at $1.39 per hour average adds unnecessary cost for single-user generation.
The A100's 312 TFLOPS FP16 and NVLink interconnect accelerate HPC simulations. The 4070 SUPER's 35.5 TFLOPS FP32 suits lighter tasks but lacks datacenter scalability.
Frequently Asked Questions
Which GPU has more VRAM: A100 SXM4 80GB or RTX 4070 SUPER?▾
The A100 SXM4 80GB provides 80 GB HBM2e VRAM, far exceeding the RTX 4070 SUPER's 12 GB GDDR6X. This enables the A100 to load massive models without offloading. The 4070 SUPER suits smaller datasets.
How does memory bandwidth compare between A100 and RTX 4070 SUPER?▾
The A100 achieves 2039 GB/s with HBM2e, over four times the 4070 SUPER's 504 GB/s GDDR6X. Higher bandwidth on the A100 reduces latency in data-heavy AI training. It supports larger batches effectively.
What are the FP16 performance differences?▾
The A100 SXM4 80GB delivers 312 TFLOPS FP16, about nine times the RTX 4070 SUPER's 35.5 TFLOPS. This gap favors A100 for ML training and inference. Tensor cores drive the A100's advantage.
Is the RTX 4070 SUPER available for cloud rental?▾
No live cloud offers exist for the RTX 4070 SUPER. The A100 SXM4 80GB starts at $0.45 per hour, averaging $1.39 per hour across 25 providers. Consumer GPUs rarely appear in cloud listings.
Which has higher power consumption?▾
The A100 SXM4 80GB requires 400W TDP, double the RTX 4070 SUPER's 220W. This suits datacenter cooling but increases operational costs. The 4070 SUPER enables efficient desktop power use.
Can RTX 4070 SUPER replace A100 for AI training?▾
The 4070 SUPER cannot replace the A100 due to 12 GB VRAM versus 80 GB and 35.5 TFLOPS FP16 versus 312 TFLOPS. It works for prototyping small models. Scale to A100 for production training.
Which is cheaper to rent, the A100 or the RTX 4070?▾
Cloud rental prices for both the A100 and RTX 4070 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 4070?▾
The A100 has 40 to 80 GB of HBM2e memory. The RTX 4070 has 12 GB of GDDR6X memory.
Can I find A100 and RTX 4070 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 4070?▾
The A100 uses the Ampere architecture (2020) while the RTX 4070 uses Ada Lovelace (2023). The A100 delivers 10.7x the FP16 throughput and 4.0x the memory bandwidth of the RTX 4070.