Specifications Compared
| Spec | A100 | RTX-4060 |
|---|---|---|
| TDP | 400W | 115W |
| VRAM | 40-80 GB | 8 GB |
| CUDA Cores | 6,912 | 3,072 |
| Memory Type | HBM2e | GDDR6 |
| Architecture | Ampere | Ada Lovelace |
| Form Factors | SXM4, PCIe | PCIe |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | |
| Tensor Cores | 432 | 96 |
| FP16 Performance | 312 TFLOPS | 15.1 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 15.1 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | |
| INT8 Performance | 624 TOPS | 242 TOPS |
| Memory Bandwidth | 2,039 GB/s | 272 GB/s |
Performance Analysis
The A100's FP16 performance of 312 TFLOPS dwarfs the RTX 4060's 15.1 TFLOPS, making it ideal for machine learning training that leverages half-precision tensor cores for accelerated matrix operations. FP32 performance is closer at 19.5 TFLOPS for the A100 and 15.1 TFLOPS for the RTX 4060, but the A100 still leads in single-precision tasks common in scientific simulations. This compute disparity translates to faster training epochs on the A100 for large neural networks.
Memory bandwidth of 2039 GB/s on the A100 supports much larger batch sizes than the RTX 4060's 272 GB/s, reducing the need for gradient accumulation and improving throughput in deep learning pipelines. The A100's 40-80 GB HBM2e VRAM accommodates models exceeding 8 GB GDDR6 on the RTX 4060, preventing out-of-memory errors during inference or fine-tuning of large language models. Power draw at 400W TDP for the A100 versus 115W for the RTX 4060 affects scalability: the A100 excels in dense clusters via NVLink and InfiniBand, while the RTX 4060 suits single-node efficiency.
In real-world terms, these specs mean the A100 processes complex AI workloads 10-20 times faster in FP16-dominated scenarios, though the RTX 4060 offers balanced performance for inference where memory demands stay below 8 GB.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A100
| 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
The A100 is the superior choice for large-scale LLM training or fine-tuning, where 40-80 GB HBM2e VRAM and 2039 GB/s bandwidth handle models with billions of parameters and batch sizes exceeding RTX 4060 limits. Datacenter features like NVLink and InfiniBand enable multi-GPU scaling for distributed training, unavailable on the RTX 4060. High FP16 performance at 312 TFLOPS accelerates tensor operations in professional AI pipelines, justifying $0.45/hr starting pricing.
When to Choose the RTX 4060
The RTX 4060 excels in budget-conscious scenarios like Stable Diffusion image generation or small-model inference, with 8 GB GDDR6 sufficient for payloads under that threshold and pricing from $0.08/hr. Its 115W TDP minimizes costs in low-density cloud instances, and Ada Lovelace architecture provides efficient FP16/FP32 at 15.1 TFLOPS each for gaming or prototyping. Users avoid overprovisioning when workloads fit within 272 GB/s bandwidth.
Use Cases
The A100's 40-80 GB HBM2e VRAM and 312 TFLOPS FP16 support training of large language models with massive parameter counts and batch sizes. The RTX 4060's 8 GB GDDR6 cannot accommodate such scales.
Small models fit within the RTX 4060's 8 GB VRAM at 15.1 TFLOPS FP16 for cost-effective $0.08/hr inference. Larger models require the A100's 40-80 GB and 2039 GB/s bandwidth.
Fine-tuning mid-to-large models demands the A100's 312 TFLOPS FP16 and high memory bandwidth for efficient gradient updates. The RTX 4060 limits batch sizes due to 272 GB/s and 8 GB VRAM.
Stable Diffusion workflows typically use under 8 GB VRAM, where the RTX 4060's Ada architecture and $0.08/hr pricing provide ample 15.1 TFLOPS performance. A100 overkill raises unnecessary costs.
Scientific simulations benefit from the A100's 19.5 TFLOPS FP32, NVLink interconnect, and 40-80 GB VRAM for complex datasets. RTX 4060's lower specs hinder large-scale computations.
Frequently Asked Questions
What is the VRAM difference between A100 and RTX 4060?▾
The A100 offers 40-80 GB HBM2e VRAM, while the RTX 4060 has 8 GB GDDR6. This allows the A100 to manage much larger AI models without memory constraints. The capacity gap suits datacenter versus consumer applications.
How do their memory bandwidths compare?▾
A100 provides 2039 GB/s, compared to RTX 4060's 272 GB/s. Higher bandwidth on A100 supports larger batch sizes in training. RTX 4060 suffices for smaller inference tasks.
Which has better FP16 performance?▾
The A100 delivers 312 TFLOPS FP16, far exceeding the RTX 4060's 15.1 TFLOPS. This excels in ML training with half-precision. FP32 is 19.5 TFLOPS on A100 versus 15.1 TFLOPS on RTX 4060.
What are the cloud pricing differences?▾
A100 starts at $0.45/hr averaging $1.89/hr across 60 offers, RTX 4060 at $0.08/hr averaging $0.14/hr across 8 offers. RTX 4060 wins on cost for light use. A100 justifies expense for heavy workloads.
What is the TDP comparison?▾
A100 has a 400W TDP, RTX 4060 a 115W TDP. Lower power on RTX 4060 reduces cloud instance costs. A100's higher TDP enables greater compute density.
Can RTX 4060 replace A100 for AI training?▾
No, RTX 4060's 8 GB VRAM and 15.1 TFLOPS FP16 cannot match A100's 40-80 GB and 312 TFLOPS for large training jobs. It works for small-scale prototyping only.
Which is cheaper to rent, the A100 or the RTX 4060?▾
Cloud rental prices for both the A100 and RTX 4060 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 4060?▾
The A100 has 40 to 80 GB of HBM2e memory. The RTX 4060 has 8 GB of GDDR6 memory.
Can I find A100 and RTX 4060 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 4060?▾
The A100 uses the Ampere architecture (2020) while the RTX 4060 uses Ada Lovelace (2023). The A100 delivers 20.7x the FP16 throughput and 7.5x the memory bandwidth of the RTX 4060.