Specifications Compared
| Spec | A100 | RTX-3080 |
|---|---|---|
| TDP | 400W | 320W |
| VRAM | 40-80 GB | 10-12 GB |
| CUDA Cores | 6,912 | 8,704 |
| Memory Type | HBM2e | GDDR6X |
| Architecture | Ampere | Ampere |
| Form Factors | SXM4, PCIe | PCIe |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | |
| Tensor Cores | 432 | 272 |
| FP16 Performance | 312 TFLOPS | 29.8 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 29.8 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | |
| INT8 Performance | 624 TOPS | |
| Memory Bandwidth | 2,039 GB/s | 760 GB/s |
Performance Analysis
A100's 312 TFLOPS FP16 performance vastly outpaces RTX 3080's 29.8 TFLOPS, enabling faster deep learning training where half-precision computations dominate. The FP32 figures reverse this trend: A100's 19.5 TFLOPS trails RTX 3080's 29.8 TFLOPS, making the latter preferable for simulation or rendering workloads reliant on single-precision math. Inference benefits from A100's tensor core optimizations, processing larger models without precision loss. Memory bandwidth shapes real-world throughput: A100's 2039 GB/s sustains high batch sizes in training large neural networks, reducing iteration times. RTX 3080's 760 GB/s constrains it to modest batches, often bottlenecking memory-intensive tasks like transformer models. Power draw reflects this: A100 consumes 400W for peak output, while RTX 3080 manages 320W for balanced consumer use.
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 557GB Storage | Czechia | $1.00/GPU/hr | Available | ||
 Denvr | 4×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 64 vCPU 512GB RAM 7600GB Storage | Virginia | $1.15/GPU/hr $4.60/hr total (4×) |
When to Choose the A100 PCIe 80GB
Data center AI training demands the A100 PCIe 80GB's 80 GB HBM2e VRAM and 312 TFLOPS FP16 performance to handle massive datasets and models. High-performance computing clusters leverage its NVLink interconnect and PCIe 4.0 support for multi-GPU scaling across InfiniBand fabrics. Cloud users prioritizing throughput over cost select A100 for workloads exceeding 12 GB VRAM, such as large language model pretraining.
When to Choose the RTX 3080
Gaming and lightweight machine learning favor the RTX 3080's 29.8 TFLOPS FP32 performance and lower 320W TDP. Budget-conscious developers run inference or fine-tuning on models fitting within 10-12 GB GDDR6X VRAM at $0.06 per hour starting price. Stable diffusion generation or real-time graphics benefit from its PCIe form factor and cost efficiency for single-user setups.
Use Cases
A100's 80 GB HBM2e VRAM and 312 TFLOPS FP16 support massive parameter models with large batch sizes via 2039 GB/s bandwidth. RTX 3080's 10-12 GB VRAM cannot accommodate such scales.
A100 handles high-concurrency inference on large models due to 80 GB VRAM. RTX 3080 suits smaller models but limits throughput with 10-12 GB.
Fine-tuning large models requires A100's 312 TFLOPS FP16 and high bandwidth for efficient gradients. RTX 3080 works for small models but bottlenecks on memory.
RTX 3080's 29.8 TFLOPS FP16 and low $0.06 per hour pricing suffice for image generation at 512x512 resolutions. A100 overkill for consumer-scale diffusion.
A100's 2039 GB/s bandwidth and NVLink accelerate simulations with large datasets. RTX 3080's 760 GB/s limits complex HPC workloads.
Frequently Asked Questions
What is the VRAM difference between A100 PCIe 80GB and RTX 3080?▾
A100 provides 80 GB HBM2e VRAM, enabling large model handling. RTX 3080 offers 10-12 GB GDDR6X, suitable for smaller workloads. This gap affects batch sizes in training.
How do FP16 performances compare?▾
A100 achieves 312 TFLOPS FP16 for rapid AI training. RTX 3080 delivers 29.8 TFLOPS, adequate for inference but slower for heavy compute. The difference speeds A100 training by over 10x.
What are the cloud pricing differences?▾
A100 PCIe 80GB starts at $0.89 per hour, averaging $2.08 across 28 offers. RTX 3080 begins at $0.06 per hour, averaging $0.13 across 4 offers. RTX suits budget tasks.
Is A100 better for machine learning training?▾
Yes, A100's 312 TFLOPS FP16 and 2039 GB/s bandwidth excel in training. RTX 3080's 29.8 TFLOPS limits it to lighter fine-tuning. Professionals choose A100 for scale.
What about power consumption?▾
A100 draws 400W TDP for maximum performance. RTX 3080 uses 320W, better for power-sensitive setups. Higher TDP correlates with A100's compute density.
Can RTX 3080 handle large language models?▾
RTX 3080's 10-12 GB VRAM restricts it to small LLMs or quantized inference. A100's 80 GB supports full-scale models. Use RTX for prototyping only.
Which is cheaper to rent, the A100 or the RTX 3080?▾
Cloud rental prices for both the A100 and RTX 3080 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 3080?▾
The A100 has 40 to 80 GB of HBM2e memory. The RTX 3080 has 10 to 12 GB of GDDR6X memory.
Can I find A100 and RTX 3080 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 3080?▾
The A100 uses the Ampere architecture (2020) while the RTX 3080 uses Ampere (2020). The A100 delivers 10.5x the FP16 throughput and 2.7x the memory bandwidth of the RTX 3080.