Specifications Compared
| Spec | RTX-2070 | RTX-A5000 |
|---|---|---|
| TDP | 175W | 230W |
| VRAM | 8 GB | 24 GB |
| CUDA Cores | 2,304 | 8,192 |
| Memory Type | GDDR6 | GDDR6 |
| Architecture | Turing | Ampere |
| Form Factors | PCIe | PCIe |
| Interconnect | NVLink | NVLink |
| Tensor Cores | 288 | 256 |
| FP16 Performance | 7.5 TFLOPS | 27.8 TFLOPS |
| FP32 Performance | 7.5 TFLOPS | 27.8 TFLOPS |
| Memory Bandwidth | 448 GB/s | 768 GB/s |
Performance Analysis
The RTX A5000's 27.8 TFLOPS FP16 and FP32 performance dwarfs the RTX 2070 SUPER's 9.1 TFLOPS: this gap accelerates deep learning training by enabling faster iterations on models using half-precision arithmetic common in frameworks like PyTorch. FP32 parity on both GPUs supports precise scientific simulations, but the A5000 processes larger datasets quicker. Memory bandwidth defines practical limits: the A5000's 768 GB/s sustains high throughput for big batch sizes in inference, avoiding stalls that plague the 2070 SUPER's 448 GB/s during memory-intensive operations like transformer training. Higher TDP of 230 W on the A5000 reflects its efficiency for sustained loads versus the 2070 SUPER's 215 W suited to intermittent use. These specs position the A5000 for production-scale AI, while the 2070 SUPER handles prototyping.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
RTX A5000
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vast.ai | 4×NVIDIA RTX A5000 24GB VRAM | 24GB | 64 vCPU 224GB RAM 2256GB Storage | Romania | $0.23/GPU/hr $0.92/hr total (4×) | Available | ||
 RunPod | NVIDIA RTX A5000 24GB VRAM | 24GB | 9 vCPU 25GB RAM | 🌍global | $0.27/GPU/hr | |||
 Cirrascale | 8×NVIDIA RTX A5000 24GB VRAM | 24GB | 40 vCPU 256GB RAM 2610GB Storage | United States | $0.41/GPU/hr $3.28/hr total (8×) | |||
Cirrascale | 8×NVIDIA RTX A5000 24GB VRAM | 24GB | 40 vCPU 256GB RAM 2610GB Storage | United States | $0.46/GPU/hr $3.68/hr total (8×) | |||
Cirrascale | 8×NVIDIA RTX A5000 24GB VRAM | 24GB | 40 vCPU 256GB RAM 2610GB Storage | United States | $0.49/GPU/hr $3.92/hr total (8×) |
When to Choose the RTX 2070 SUPER
The RTX 2070 SUPER excels in budget-constrained environments for small-scale machine learning. Its 8 GB VRAM suffices for models under 7 billion parameters during inference, and 9.1 TFLOPS FP32 handles fine-tuning lightweight networks efficiently. With no cloud offers, it fits on-premises setups where power draw stays below 215 W.
When to Choose the RTX A5000
Opt for the RTX A5000 in demanding professional workflows requiring extensive VRAM. The 24 GB capacity supports training large language models up to 70 billion parameters, and 768 GB/s bandwidth enables batch sizes over 64 without bottlenecks. Cloud availability from $0.02 per hour makes it scalable for teams.
Use Cases
RTX A5000's 24 GB VRAM and 27.8 TFLOPS FP16 support large models exceeding the RTX 2070 SUPER's 8 GB limit. Higher 768 GB/s bandwidth prevents memory bottlenecks during gradient computations.
The 27.8 TFLOPS FP16 on RTX A5000 delivers lower latency for high-throughput serving compared to 9.1 TFLOPS on RTX 2070 SUPER. 24 GB VRAM accommodates bigger context windows.
RTX 2070 SUPER's 8 GB VRAM fits small LoRA adapters on 7B models at 9.1 TFLOPS. RTX A5000 scales to full fine-tuning of 30B models with 24 GB.
RTX A5000's 768 GB/s bandwidth accelerates diffusion steps faster than 448 GB/s on RTX 2070 SUPER. 24 GB VRAM enables high-resolution generations without swapping.
27.8 TFLOPS FP32 on RTX A5000 processes simulations like molecular dynamics quicker than 9.1 TFLOPS on RTX 2070 SUPER. NVLink aids multi-GPU scaling.
Frequently Asked Questions
Does RTX 2070 SUPER have enough VRAM for LLM inference?▾
RTX 2070 SUPER's 8 GB GDDR6 limits it to models under 7 billion parameters at batch size 1. Larger models require quantization to fit. RTX A5000's 24 GB handles full precision up to 13B models easily.
How much faster is RTX A5000 than RTX 2070 SUPER in training?▾
RTX A5000's 27.8 TFLOPS FP16 provides over 3x the performance of RTX 2070 SUPER's 9.1 TFLOPS, reducing epochs by similar margins on ResNet tasks. Bandwidth of 768 GB/s versus 448 GB/s further boosts large-batch training.
What is the power consumption difference?▾
RTX 2070 SUPER draws 215 W TDP, suitable for compact systems. RTX A5000 requires 230 W, demanding better cooling for sustained 27.8 TFLOPS workloads.
Is RTX A5000 available on cloud cheaper than RTX 2070 SUPER?▾
RTX A5000 starts at $0.02 per hour average $0.41 per hour across 36 offers. RTX 2070 SUPER has no live cloud offers, favoring local use.
Both GPUs support NVLink interconnects for multi-GPU setups. RTX A5000 leverages it better with 27.8 TFLOPS per card scaling to clusters.▾
RTX A5000 architecture advantages over Turing?▾
Ampere in RTX A5000 delivers 27.8 TFLOPS FP16 with improved tensor cores over Turing's 9.1 TFLOPS in RTX 2070 SUPER. 24 GB VRAM addresses Turing's memory constraints for AI.
Which is cheaper to rent, the RTX 2070 or the RTX A5000?▾
Cloud rental prices for both the RTX 2070 and RTX A5000 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 RTX 2070 have compared to the RTX A5000?▾
The RTX 2070 has 8 GB of GDDR6 memory. The RTX A5000 has 24 GB of GDDR6 memory.
Can I find RTX 2070 and RTX A5000 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 RTX 2070 and the RTX A5000?▾
The RTX 2070 uses the Turing architecture (2018) while the RTX A5000 uses Ampere (2021). The RTX A5000 delivers 3.7x the FP16 throughput and 1.7x the memory bandwidth of the RTX 2070.