Specifications Compared
| Spec | A100 | RTX-2060 |
|---|---|---|
| TDP | 400W | 160W |
| VRAM | 40-80 GB | 6-12 GB |
| CUDA Cores | 6,912 | 1,920 |
| Memory Type | HBM2e | GDDR6 |
| Architecture | Ampere | Turing |
| Form Factors | SXM4, PCIe | PCIe |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | |
| Tensor Cores | 432 | 240 |
| FP16 Performance | 312 TFLOPS | 6.5 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 6.5 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | |
| INT8 Performance | 624 TOPS | |
| Memory Bandwidth | 2,039 GB/s | 336 GB/s |
Performance Analysis
The A100's FP16 performance of 312 TFLOPS vastly exceeds the RTX 2060's 6.5 TFLOPS, accelerating deep learning training where half-precision computations dominate. Its FP32 rate of 19.5 TFLOPS outpaces the RTX 2060's 6.5 TFLOPS, improving inference and scientific simulations in single precision. This gap translates to training times reduced by over 40 times on the A100 for large models.
Memory bandwidth defines batch size feasibility: the A100's 2039 GB/s enables processing batches with billions of parameters without stalling, crucial for efficient gradient updates in transformer models. The RTX 2060's 336 GB/s restricts batches to thousands of parameters, increasing iteration counts and total training duration. The A100's 400W TDP supports prolonged peak loads, unlike the RTX 2060's 160W limit.
Interconnects further differentiate them: the A100 uses NVLink, PCIe 4.0, and InfiniBand for multi-GPU scaling, while the RTX 2060 relies solely on PCIe.
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
Choose the A100 for large-scale AI training and inference demanding high VRAM, such as models exceeding 6 GB. Its 40 GB HBM2e handles full precision for billion-parameter LLMs, and 312 TFLOPS FP16 speeds convergence. Multi-GPU setups via NVLink suit distributed workloads in research or production.
The A100 fits HPC tasks like scientific simulations requiring 19.5 TFLOPS FP32 and 2039 GB/s bandwidth for complex datasets.
When to Choose the RTX 2060
The RTX 2060 is ideal for budget prototyping, small model inference, or gaming at $0.02 per hour. Its 6 GB GDDR6 suffices for models under 1 billion parameters, and 6.5 TFLOPS FP16/FP32 handles lightweight tasks efficiently.
Low TDP of 160W and PCIe form factor make it suitable for single-user desktops or quick cloud tests without scaling needs.
Use Cases
A100's 40 GB VRAM and 312 TFLOPS FP16 support training models with billions of parameters. RTX 2060's 6 GB VRAM causes out-of-memory errors.
A100's 2039 GB/s bandwidth enables high-throughput serving of large LLMs. RTX 2060's 336 GB/s limits requests per second.
A100 handles full fine-tuning datasets with 19.5 TFLOPS FP32. RTX 2060 requires heavy quantization, reducing accuracy.
RTX 2060's 6.5 TFLOPS FP16 generates images quickly for individuals. A100 scales for batch production but costs more at $1.00 per hour.
A100's 40 GB VRAM and NVLink manage large simulations. RTX 2060's 6 GB restricts dataset sizes.
Frequently Asked Questions
What is the VRAM difference between A100 SXM4 40GB and RTX 2060?▾
The A100 provides 40 GB HBM2e VRAM, while the RTX 2060 has 6 GB GDDR6. This allows the A100 to load models over 6 times larger without swapping.
How do FP16 performances compare for AI training?▾
A100 achieves 312 TFLOPS FP16 versus RTX 2060's 6.5 TFLOPS, enabling 48 times faster half-precision training. This shortens epochs for deep networks.
What are the cloud rental prices?▾
A100 SXM4 40GB starts at $1.00 per hour, averaging $2.63 across five offers. RTX 2060 begins at $0.02 per hour, averaging $0.04 across two offers.
Can RTX 2060 handle LLM inference?▾
RTX 2060 manages small LLMs under 6 GB with 6.5 TFLOPS FP16, but struggles with larger ones due to 336 GB/s bandwidth. A100 excels at scale.
Which has higher power consumption?▾
A100's TDP is 400W for sustained AI loads, compared to RTX 2060's 160W suited for gaming. This reflects datacenter versus consumer design.
Is A100 better for multi-GPU setups?▾
A100 supports NVLink, PCIe 4.0, and InfiniBand for scaling across nodes. RTX 2060 uses only PCIe, limiting cluster efficiency.
Which is cheaper to rent, the A100 or the RTX 2060?▾
Cloud rental prices for both the A100 and RTX 2060 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 2060?▾
The A100 has 40 to 80 GB of HBM2e memory. The RTX 2060 has 6 to 12 GB of GDDR6 memory.
Can I find A100 and RTX 2060 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 2060?▾
The A100 uses the Ampere architecture (2020) while the RTX 2060 uses Turing (2019). The A100 delivers 48.0x the FP16 throughput and 6.1x the memory bandwidth of the RTX 2060.