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
FP16 performance defines machine learning suitability: the A100 SXM4 40GB achieves 312 TFLOPS, nearly 9x the RTX 4070 SUPER's 35 TFLOPS, accelerating training and inference on tensor-heavy models. The A100's FP32 lags at 19.5 TFLOPS below the SUPER's 35 TFLOPS, yet ML rarely bottlenecks on scalar FP32.
Memory bandwidth impacts batch sizes profoundly: A100's 2039 GB/s enables processing models with billions of parameters at scale, avoiding out-of-memory errors beyond the SUPER's 504 GB/s and 12 GB limit. Higher 400W TDP sustains A100 peaks, contrasting the 220W SUPER suited for intermittent loads.
Real-world implications favor A100 for distributed training; the SUPER handles single-user Stable Diffusion or small-batch inference efficiently.
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×) |
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 40GB
Select the A100 SXM4 40GB for large-scale LLM training or fine-tuning: 40 GB HBM2e VRAM accommodates models like GPT-3 variants, while 312 TFLOPS FP16 speeds iterations. NVLink interconnect scales to clusters, and 2039 GB/s bandwidth supports batch sizes exceeding 512.
It excels in scientific computing and HPC where sustained 400W performance and InfiniBand networking matter.
When to Choose the RTX 4070 SUPER
The RTX 4070 SUPER fits gaming, content creation, or small-scale inference: 12 GB GDDR6X handles Stable Diffusion at 512x512 resolutions, with balanced 35 TFLOPS FP16 and FP32 for versatile compute. Lower 220W TDP suits desktop power envelopes.
Choose it for local setups absent cloud needs, as no live offers appear.
Use Cases
A100 SXM4 40GB's 40 GB VRAM and 312 TFLOPS FP16 handle massive models and large batches. RTX 4070 SUPER's 12 GB VRAM restricts scale.
High 2039 GB/s bandwidth on A100 supports high-throughput serving. SUPER's 504 GB/s suits only low-concurrency needs.
A100's 40 GB VRAM fits parameter-heavy adapters; 312 TFLOPS FP16 accelerates gradients. 12 GB on SUPER limits model size.
RTX 4070 SUPER's 35 TFLOPS FP16 and 12 GB VRAM generate images at 35-40 it/s. A100 overkill for single-user creative tasks.
A100's NVLink, InfiniBand, and 2039 GB/s bandwidth scale simulations. SUPER lacks multi-GPU enterprise features.
Frequently Asked Questions
Which GPU has more VRAM: A100 SXM4 40GB or RTX 4070 SUPER?▾
The A100 SXM4 40GB provides 40 GB HBM2e VRAM. RTX 4070 SUPER offers 12 GB GDDR6X. This 3.3x difference favors A100 for large models.
Is A100 better than RTX 4070 SUPER for AI training?▾
A100 SXM4 40GB delivers 312 TFLOPS FP16 versus 35 TFLOPS on RTX 4070 SUPER. Its 40 GB VRAM supports bigger batches. Consumer SUPER suits prototyping only.
What is the memory bandwidth comparison?▾
A100 SXM4 40GB achieves 2039 GB/s with HBM2e. RTX 4070 SUPER reaches 504 GB/s on GDDR6X. A100's 4x advantage prevents data bottlenecks.
RTX 4070 SUPER cloud pricing versus A100?▾
No live cloud offers exist for RTX 4070 SUPER. A100 SXM4 40GB starts at $1.00 per hour, averaging $3.06 per hour across three providers.
Power consumption of A100 SXM4 40GB vs RTX 4070 SUPER?▾
A100 SXM4 40GB has 400W TDP for sustained loads. RTX 4070 SUPER uses 220W. Datacenter cooling handles A100; desktops prefer SUPER efficiency.
Can RTX 4070 SUPER replace A100 for inference?▾
RTX 4070 SUPER's 12 GB VRAM and 35 TFLOPS FP16 work for small LLMs at low throughput. A100's 40 GB and 312 TFLOPS scale to production serving.
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.