Specifications Compared
| Spec | A100 | RTX-5060 |
|---|---|---|
| TDP | 400W | 180W |
| VRAM | 40-80 GB | 12 GB |
| CUDA Cores | 6,912 | 4,608 |
| Memory Type | HBM2e | GDDR7 |
| Architecture | Ampere | Blackwell |
| Form Factors | SXM4, PCIe | PCIe |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | |
| Tensor Cores | 432 | 144 |
| FP16 Performance | 312 TFLOPS | 23.1 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 23.1 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | |
| INT8 Performance | 624 TOPS | 370 TOPS |
| Memory Bandwidth | 2,039 GB/s | 448 GB/s |
Performance Analysis
FP16 performance reveals a stark divide: the A100 achieves 312 TFLOPS, enabling rapid training of large models, whereas the RTX 5060 manages only 23.1 TFLOPS, limiting it to smaller-scale deep learning. In FP32, the RTX 5060 matches its FP16 at 23.1 TFLOPS, slightly surpassing the A100's 19.5 TFLOPS, which aids graphics rendering or simulations but trails in mixed-precision AI pipelines. Memory specs dictate real-world viability: A100's 40 GB HBM2e and 2039 GB/s bandwidth support enormous batch sizes in LLM training, preventing out-of-memory errors on models over 12 GB. The RTX 5060's 12 GB GDDR7 and 448 GB/s restrict it to inference on compact networks or fine-tuning with reduced batches. Power draw amplifies this: A100's 400W suits scaled clusters, RTX 5060's 180W favors edge computing.
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 557GB Storage | Czechia | $1.00/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 5060
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
Vast.ai | 4×NVIDIA GeForce RTX 5060 Ti 16GB VRAM | 16GB | 128 vCPU 126GB RAM 2690GB Storage | Maryland | $0.27/GPU/hr $1.07/hr total (4×) | Available |
When to Choose the A100 PCIe 40GB
Datacenter-scale AI training favors the A100 PCIe 40GB: its 40 GB HBM2e VRAM accommodates massive LLMs, and 312 TFLOPS FP16 accelerates convergence times. Cloud availability from $0.60 per hour across 11 providers enables scalable deployments with NVLink and InfiniBand interconnects. Professionals handling batch sizes beyond 12 GB constraints select it for reliability.
When to Choose the RTX 5060
Consumer inference and gaming workloads suit the RTX 5060: 12 GB GDDR7 VRAM and 23.1 TFLOPS FP32 handle Stable Diffusion or lightweight LLMs efficiently at 180W TDP. Lacking cloud offers, it excels in local setups via PCIe, avoiding A100's 400W power and $1.85 average hourly costs. Budget users prioritize its Blackwell architecture for future-proofing smaller tasks.
Use Cases
A100's 40 GB HBM2e VRAM and 312 TFLOPS FP16 support massive models and large batches. RTX 5060's 12 GB limits scale.
A100 handles high-throughput inference on large models with 2039 GB/s bandwidth. RTX 5060 suits only smaller LLMs due to 12 GB VRAM.
40 GB capacity fits parameter-heavy fine-tuning; 312 TFLOPS FP16 speeds iterations. RTX 5060 constrains to tiny datasets.
RTX 5060's 23.1 TFLOPS FP32 and GDDR7 excel in generative tasks at 180W. A100 overkill for consumer image generation.
RTX 5060's 23.1 TFLOPS FP32 matches needs for simulations; A100 viable if scaling to 40 GB datasets required.
Frequently Asked Questions
What is the VRAM difference between A100 PCIe 40GB and RTX 5060?▾
A100 provides 40 GB HBM2e VRAM, enabling large model handling. RTX 5060 offers 12 GB GDDR7, suitable for smaller workloads. This gap affects batch sizes in training.
How do FP16 performances compare?▾
A100 delivers 312 TFLOPS FP16 for fast ML training. RTX 5060 reaches 23.1 TFLOPS, adequate for inference but not heavy compute. The ratio exceeds 13x in A100's favor.
What are the cloud pricing details for A100?▾
NVIDIA A100 PCIe 40GB starts at $0.60 per hour, averaging $1.85 across 11 offers. RTX 5060 has no live cloud availability. Costs reflect datacenter vs consumer positioning.
Which has higher memory bandwidth?▾
A100 achieves 2039 GB/s with HBM2e, supporting huge data flows. RTX 5060 provides 448 GB/s GDDR7, limiting large-batch operations. Bandwidth scales over 4.5x higher on A100.
What are the TDP values?▾
A100 consumes 400W for peak performance in clusters. RTX 5060 uses 180W, ideal for efficient local use. Lower TDP reduces operational costs for lighter tasks.
Can RTX 5060 replace A100 in AI training?▾
RTX 5060 cannot due to 12 GB VRAM versus A100's 40 GB. Its 23.1 TFLOPS FP16 trails 312 TFLOPS severely. Use RTX for inference only.
Which is cheaper to rent, the A100 or the RTX 5060?▾
Cloud rental prices for both the A100 and RTX 5060 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 5060?▾
The A100 has 40 to 80 GB of HBM2e memory. The RTX 5060 has 12 GB of GDDR7 memory.
Can I find A100 and RTX 5060 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 5060?▾
The A100 uses the Ampere architecture (2020) while the RTX 5060 uses Blackwell (2025). The A100 delivers 13.5x the FP16 throughput and 4.6x the memory bandwidth of the RTX 5060.