Specifications Compared
| Spec | A10 | QUADRO-RTX-6000 |
|---|---|---|
| TDP | 150W | 260W |
| VRAM | 24 GB | 24 GB |
| CUDA Cores | 9,216 | 4,608 |
| Memory Type | GDDR6 | GDDR6 |
| Architecture | Ampere | Turing |
| Form Factors | PCIe | PCIe |
| Interconnect | NVLink | |
| Tensor Cores | 288 | 576 |
| FP16 Performance | 31.2 TFLOPS | 16.3 TFLOPS |
| FP32 Performance | 31.2 TFLOPS | 16.3 TFLOPS |
| INT8 Performance | 250 TOPS | |
| Memory Bandwidth | 600 GB/s | 672 GB/s |
Performance Analysis
The A10 demonstrates a clear performance advantage: its 31.2 TFLOPS FP16 and FP32 ratings nearly double the Quadro RTX 6000's 16.3 TFLOPS, accelerating deep learning training and inference by up to 91 percent in compute-bound scenarios. This FP16/FP32 parity on the A10 supports mixed-precision workflows efficiently, reducing training times for large models compared to the Quadro RTX 6000's older tensor cores.
Memory bandwidth impacts batch sizes in inference: the Quadro RTX 6000's 672 GB/s allows marginally larger batches than the A10's 600 GB/s in bandwidth-limited cases, though the A10's Ampere efficiency often offsets this. Power efficiency is stark: the A10's 150W TDP versus 260W enables denser cloud deployments, yielding 0.208 TFLOPS per watt compared to 0.063 for the Quadro RTX 6000.
For real-world AI tasks, the A10 handles modern frameworks like TensorFlow or PyTorch with better scalability, while the Quadro RTX 6000 suits legacy workstation applications where NVLink interconnect provides multi-GPU benefits.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A10
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 LeaderGPU | 10×NVIDIA A10 24GB VRAM | 24GB | 64 vCPU 384GB RAM 2000GB Storage | Netherlands | $0.60/GPU/hr $6.00/hr total (10×) | Available | ||
 Vast.ai | 2×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 126GB RAM 5672GB Storage | Slovenia | $0.73/GPU/hr $1.47/hr total (2×) | Available | ||
Vast.ai | 2×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 126GB RAM 769GB 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 |
When to Choose the A10
Select the A10 for cloud-based AI workloads requiring high throughput and availability. Its 31.2 TFLOPS FP16 performance excels in LLM training and inference, and at $0.60 per hour minimum pricing, it offers cost savings over on-premises alternatives. The 150W TDP supports scalable multi-GPU setups without excessive power draw.
When to Choose the Quadro RTX 6000
Choose the Quadro RTX 6000 for workstation environments needing NVLink interconnect for tightly coupled multi-GPU scientific simulations. Its 672 GB/s bandwidth aids memory-intensive rendering tasks, and 24 GB VRAM matches the A10 for large datasets. Availability may favor local deployments where cloud options are absent.
Use Cases
The A10's 31.2 TFLOPS FP16 performance nearly doubles the Quadro RTX 6000's 16.3 TFLOPS, speeding up large model training. Lower 150W TDP supports longer sessions.
A10 delivers 31.2 TFLOPS FP32 for faster batched inference than the 16.3 TFLOPS on Quadro RTX 6000. Cloud availability at $1.06 per hour average enables scalable serving.
Ampere architecture on A10 optimizes mixed-precision fine-tuning with 31.2 TFLOPS versus 16.3 TFLOPS. 24 GB VRAM handles parameter-heavy models efficiently.
Both offer 24 GB VRAM for high-resolution generation; A10 edges with higher FLOPS, but Quadro RTX 6000's 672 GB/s bandwidth aids texture-heavy workflows.
Quadro RTX 6000's NVLink interconnect enables multi-GPU synchronization critical for simulations. 672 GB/s bandwidth supports data-parallel scientific codes.
Frequently Asked Questions
Which GPU has higher FP32 performance?▾
The A10 achieves 31.2 TFLOPS FP32, surpassing the Quadro RTX 6000's 16.3 TFLOPS by 91 percent. This benefits compute-heavy tasks like training.
Do both have the same VRAM?▾
Yes, both provide 24 GB GDDR6 VRAM. This capacity suits large models, though A10's architecture utilizes it more efficiently for AI.
What is the power consumption difference?▾
A10 draws 150W TDP, half the Quadro RTX 6000's 260W. Lower power aids cloud density and cost.
Is the Quadro RTX 6000 available in the cloud?▾
No live cloud offers exist for Quadro RTX 6000 currently. A10 averages $1.06 per hour across three providers.
Which has better memory bandwidth?▾
Quadro RTX 6000 leads with 672 GB/s over A10's 600 GB/s. This helps bandwidth-bound inference batches.
What architectures do they use?▾
A10 uses Ampere from 2021 with tensor core improvements; Quadro RTX 6000 employs Turing from 2018. A10 offers modern AI accelerations.
Which is cheaper to rent, the A10 or the Quadro RTX 6000?▾
Cloud rental prices for both the A10 and Quadro RTX 6000 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 A10 have compared to the Quadro RTX 6000?▾
The A10 has 24 GB of GDDR6 memory. The Quadro RTX 6000 has 24 GB of GDDR6 memory.
Can I find A10 and Quadro RTX 6000 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 A10 and the Quadro RTX 6000?▾
The A10 uses the Ampere architecture (2021) while the Quadro RTX 6000 uses Turing (2018). The A10 delivers 1.9x the FP16 throughput and 1.1x the memory bandwidth of the Quadro RTX 6000.