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
The A100's FP16 performance of 312 TFLOPS vastly outpaces the RTX 3080's 29.8 TFLOPS, enabling up to 10 times faster deep learning training in tensor core-accelerated operations. This disparity proves critical for model training, where FP16 precision dominates, allowing the A100 to process larger datasets and complex neural networks efficiently. The RTX 3080's balanced 29.8 TFLOPS across FP16 and FP32 suits general-purpose rendering but falters in high-throughput AI pipelines.
Memory specifications amplify real-world impacts: the A100's 40 GB HBM2e VRAM and 2039 GB/s bandwidth support massive batch sizes in training and inference without offloading to system RAM. The RTX 3080's 10-12 GB GDDR6X and 760 GB/s limit it to smaller models, causing out-of-memory errors for datasets exceeding 10 GB. Consequently, inference latency drops significantly on the A100 for production-scale deployments.
Power efficiency considerations favor neither universally: the A100's 400W TDP demands robust cooling in dense clusters, while the 3080's 320W fits lighter setups. Overall, these specs position the A100 for enterprise AI and the RTX 3080 for experimentation.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A100 PCIe 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 PCIe 40GB
The A100 PCIe 40GB stands out for large-scale AI training and inference requiring substantial memory. Its 40 GB HBM2e VRAM accommodates models like 70B-parameter LLMs, preventing fragmentation issues common on the RTX 3080's 10-12 GB. The 2039 GB/s bandwidth ensures high throughput for multi-GPU setups via NVLink.
When to Choose the RTX 3080
The RTX 3080 appeals to budget-limited users prototyping AI applications or running Stable Diffusion. At $0.06 per hour average $0.17 per hour, it delivers 29.8 TFLOPS FP16 for fine-tuning models under 10 GB, offering 10x cost savings over the A100's $1.85 per hour average. Its PCIe form factor simplifies single-node deployments.
Use Cases
A100's 312 TFLOPS FP16 and 40 GB VRAM manage billion-parameter models without splitting; RTX 3080's 10 GB capacity causes failures.
2039 GB/s bandwidth on A100 supports high batch sizes for low-latency serving; 3080's 760 GB/s bottlenecks production traffic.
RTX 3080 suffices for small models under 10 GB at low cost; A100 accelerates larger ones with 312 TFLOPS FP16.
RTX 3080's 29.8 TFLOPS FP32 handles image generation efficiently at $0.06 per hour; A100 overkill for consumer tasks.
A100's 19.5 TFLOPS FP32 and HBM2e excel in simulations; 3080 lacks interconnects for distributed jobs.
Frequently Asked Questions
What is the VRAM difference between A100 PCIe 40GB and RTX 3080?▾
The A100 provides 40 GB HBM2e VRAM, while the RTX 3080 offers 10-12 GB GDDR6X. This enables the A100 to load much larger AI models without memory errors.
How do FP16 performances compare?▾
A100 delivers 312 TFLOPS FP16 versus RTX 3080's 29.8 TFLOPS. The gap accelerates deep learning training by over 10x on the A100.
What are the cloud rental prices?▾
A100 PCIe 40GB starts at $0.60 per hour averaging $1.85 per hour across 11 offers. RTX 3080 begins at $0.06 per hour averaging $0.17 per hour across 6 offers.
Is RTX 3080 good for ML training?▾
RTX 3080 works for small models under 10 GB with 29.8 TFLOPS FP16. Larger training requires A100's 40 GB and higher bandwidth.
Which has higher memory bandwidth?▾
A100 achieves 2039 GB/s with HBM2e, compared to RTX 3080's 760 GB/s GDDR6X. This supports bigger batches on A100.
What are the TDPs?▾
A100 consumes 400W TDP for datacenter use. RTX 3080 uses 320W, suiting consumer or edge setups.
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.