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
Memory capacity defines key limitations: the A100's 40 GB HBM2e supports massive models and large batch sizes, while the RTX 3080's 10-12 GB GDDR6X restricts workloads to smaller datasets. Bandwidth amplifies this: 2039 GB/s on the A100 enables rapid data movement for training large neural networks, compared to 760 GB/s on the RTX 3080 which bottlenecks high-throughput tasks.
FP16 performance reveals specialization: the A100's 312 TFLOPS excels in mixed-precision training common in deep learning, accelerating convergence on large-scale models. The RTX 3080's 29.8 TFLOPS FP16 suits inference or graphics but falls short for intensive training. FP32 parity at 29.8 TFLOPS on the RTX 3080 aids general compute, yet the A100's 19.5 TFLOPS prioritizes tensor cores over scalar operations.
These specs translate to real-world impacts: A100 handles enterprise inference with bigger batches, RTX 3080 fits prototyping or edge deployment where cost trumps peak throughput.
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 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 SXM4 40GB
The A100 SXM4 40GB suits large-scale AI training and inference requiring 40 GB VRAM. Its 312 TFLOPS FP16 and 2039 GB/s bandwidth manage billion-parameter LLMs without splitting across GPUs. NVLink and InfiniBand interconnects enable multi-GPU clusters for scientific simulations or production pipelines.
Enterprise users prioritize reliability over cost, where $1.00 per hour starting price justifies 400W TDP scalability.
When to Choose the RTX 3080
The RTX 3080 excels in budget-conscious prototyping, fine-tuning small models, or creative tasks like Stable Diffusion within 10-12 GB VRAM limits. Its 29.8 TFLOPS FP16/FP32 balance supports gaming-integrated ML or single-user inference at $0.06 per hour.
Solo developers or hobbyists value 320W efficiency and PCIe simplicity for non-clustered workloads.
Use Cases
A100's 40 GB VRAM and 312 TFLOPS FP16 support large batch sizes for billion-parameter models. RTX 3080's 10-12 GB VRAM requires excessive model parallelism.
A100's 2039 GB/s bandwidth and 40 GB capacity enable high-throughput serving of large LLMs. RTX 3080 suits smaller models but bottlenecks at scale.
RTX 3080's 29.8 TFLOPS handles small-to-medium models cost-effectively at $0.06 per hour. A100 overkill unless datasets exceed 10 GB.
RTX 3080's 10-12 GB GDDR6X and 29.8 TFLOPS FP16 generate images efficiently for consumers. A100's enterprise features unnecessary for creative workflows.
A100's NVLink, 400W scalability, and 2039 GB/s bandwidth accelerate simulations. RTX 3080 lacks interconnects for distributed HPC.
Frequently Asked Questions
Does the A100 have more VRAM than the RTX 3080?▾
The A100 SXM4 40GB provides 40 GB HBM2e VRAM. The RTX 3080 offers 10-12 GB GDDR6X. This difference allows A100 to load larger models without offloading.
Which GPU is faster for AI training?▾
A100 achieves 312 TFLOPS FP16 versus RTX 3080's 29.8 TFLOPS. A100 completes training epochs faster on large datasets due to superior tensor core performance.
How do prices compare on the cloud?▾
A100 SXM4 40GB starts at $1.00 per hour, averaging $2.63 across five offers. RTX 3080 starts at $0.06 per hour, averaging $0.13 across four offers.
Is RTX 3080 good for Stable Diffusion?▾
RTX 3080's 760 GB/s bandwidth and 29.8 TFLOPS FP16 generate images effectively within 10 GB VRAM. It outperforms for consumer creative tasks versus A100's datacenter focus.
What is the power difference?▾
A100 TDP is 400W, supporting dense clusters. RTX 3080 TDP is 320W, suiting single-node or desktop setups.
Can RTX 3080 replace A100 for inference?▾
RTX 3080 works for models under 10 GB at 29.8 TFLOPS. A100's 40 GB and 2039 GB/s bandwidth serve larger LLMs with higher concurrency.
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.