Specifications Compared
| Spec | A100 | RTX-5080 |
|---|---|---|
| TDP | 400W | 360W |
| VRAM | 40-80 GB | 16 GB |
| CUDA Cores | 6,912 | 10,752 |
| Memory Type | HBM2e | GDDR7 |
| Architecture | Ampere | Blackwell |
| Form Factors | SXM4, PCIe | PCIe |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | |
| Tensor Cores | 432 | 336 |
| FP16 Performance | 312 TFLOPS | 56.3 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 56.3 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | |
| INT8 Performance | 624 TOPS | 900 TOPS |
| Memory Bandwidth | 2,039 GB/s | 960 GB/s |
Performance Analysis
The A100's FP16 performance reaches 312 TFLOPS, far exceeding the RTX 5080's 56.3 TFLOPS: this favors A100 in training deep learning models where half-precision tensor operations dominate. Conversely, A100's FP32 at 19.5 TFLOPS lags behind RTX 5080's 56.3 TFLOPS, benefiting graphics or single-precision scientific tasks on the latter.
Memory bandwidth of 2039 GB/s on A100 supports larger batch sizes in training, minimizing data loading bottlenecks for models exceeding 16 GB VRAM. RTX 5080's 960 GB/s limits it to smaller batches, suitable for inference on compact models.
Overall, A100 excels in memory-intensive ML pipelines; RTX 5080 suits balanced or lighter workloads with its 360W TDP versus A100's 400W.
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 | 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 | 2×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 64 vCPU 126GB RAM 1114GB Storage | Czechia | $1.00/GPU/hr $2.00/hr total (2×) | 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×) | |||
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 5080
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA GeForce RTX 5080 16GB VRAM | 16GB | 0 vCPU 0GB RAM | 🌍global | $0.59/GPU/hr |
When to Choose the A100 PCIe 80GB
Select the A100 PCIe 80GB for workloads demanding over 16 GB VRAM, such as training large language models with billions of parameters. Its 2039 GB/s bandwidth and NVLink, PCIe 4.0, InfiniBand interconnects enable efficient multi-GPU scaling unavailable on RTX 5080.
Datacenter environments benefit from A100's 312 TFLOPS FP16 for high-throughput training, despite higher average pricing of $2.08/hr.
When to Choose the RTX 5080
The RTX 5080 suits cost-sensitive applications like inference on models fitting within 16 GB GDDR7 VRAM. Its balanced 56.3 TFLOPS FP16 and FP32 performance handles Stable Diffusion or fine-tuning efficiently at $0.25/hr from pricing.
Lower 360W TDP and PCIe form factor make RTX 5080 ideal for single-GPU setups or gaming-adjacent compute without datacenter interconnect needs.
Use Cases
A100's 80 GB HBM2e VRAM and 312 TFLOPS FP16 enable training large LLMs with big batches. RTX 5080's 16 GB VRAM requires excessive model parallelism.
High 2039 GB/s bandwidth on A100 supports batched inference for models over 16 GB. RTX 5080 suffices only for smaller deployments.
Fine-tuning often fits in 16 GB, favoring RTX 5080's low $0.38/hr cost; A100's VRAM aids larger datasets at 312 TFLOPS FP16.
RTX 5080's 56.3 TFLOPS FP32 and GDDR7 excel in image generation at 360W TDP. A100 overkill for typical 16 GB needs.
A100's 80 GB VRAM and NVLink scaling suit simulations with high memory demands. RTX 5080's balanced FP32 works for lighter tasks.
Frequently Asked Questions
What is the VRAM difference between A100 PCIe 80GB and RTX 5080?▾
A100 provides 80 GB HBM2e VRAM; RTX 5080 has 16 GB GDDR7. This gap affects handling of large models in training. A100 supports massive datasets without splitting.
How do FP16 performances compare?▾
A100 delivers 312 TFLOPS FP16; RTX 5080 offers 56.3 TFLOPS. A100 accelerates ML training significantly more. RTX 5080 balances with equal FP32.
What are the cloud pricing ranges?▾
A100 PCIe 80GB starts at $0.89/hr average $2.08/hr across 28 offers. RTX 5080 from $0.25/hr average $0.38/hr across 4 offers. RTX 5080 costs far less for entry use.
Which has higher memory bandwidth?▾
A100 achieves 2039 GB/s; RTX 5080 reaches 960 GB/s. Higher bandwidth on A100 enables larger batches in deep learning. It reduces training iteration times.
What are the TDP values?▾
A100 consumes 400W TDP; RTX 5080 uses 360W. Lower TDP on RTX 5080 aids power-efficient single-node setups. A100 suits dense datacenter racks.
Can RTX 5080 use NVLink?▾
RTX 5080 lacks NVLink, PCIe 4.0, or InfiniBand; it uses PCIe only. A100's interconnects enable multi-GPU communication at scale. This limits RTX 5080 clustering.
Which is cheaper to rent, the A100 or the RTX 5080?▾
Cloud rental prices for both the A100 and RTX 5080 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 5080?▾
The A100 has 40 to 80 GB of HBM2e memory. The RTX 5080 has 16 GB of GDDR7 memory.
Can I find A100 and RTX 5080 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 5080?▾
The A100 uses the Ampere architecture (2020) while the RTX 5080 uses Blackwell (2025). The A100 delivers 5.5x the FP16 throughput and 2.1x the memory bandwidth of the RTX 5080.