Specifications Compared
| Spec | A10 | QUADRO-P6000 |
|---|---|---|
| TDP | 150W | 250W |
| VRAM | 24 GB | 24 GB |
| CUDA Cores | 9,216 | 3,840 |
| Memory Type | GDDR6 | GDDR5X |
| Architecture | Ampere | Pascal |
| Form Factors | PCIe | PCIe |
| Interconnect | ||
| Tensor Cores | 288 | |
| FP16 Performance | 31.2 TFLOPS | 12.6 TFLOPS |
| FP32 Performance | 31.2 TFLOPS | 12.6 TFLOPS |
| INT8 Performance | 250 TOPS | |
| Memory Bandwidth | 600 GB/s | 432 GB/s |
Performance Analysis
Compute throughput defines a clear leader: the A10's 31.2 TFLOPS in FP16 and FP32 exceeds the Quadro P6000's 12.6 TFLOPS by 2.5 times, accelerating machine learning training and inference tasks. In training scenarios, this delta translates to faster convergence on datasets, as FP16 precision handles mixed-precision computations common in deep learning frameworks. Inference benefits similarly, with the A10 processing more samples per second for real-time deployments.
Memory bandwidth impacts batch size capabilities profoundly. The A10's 600 GB/s allows larger batches without spilling to system RAM, reducing overhead in memory-bound workloads like transformer models. The Quadro P6000's 432 GB/s limits effective batch sizes, potentially increasing iteration times by up to 28 percent in bandwidth-saturated operations.
Efficiency considerations favor the A10. Its 150W TDP versus the Quadro P6000's 250W yields 2.5 times the performance per watt, crucial for cost-sensitive cloud environments. Both support PCIe form factors without interconnects, but the A10's newer architecture ensures compatibility with contemporary software stacks.
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 | 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 |
Quadro P6000
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Paperspace | NVIDIA Quadro P6000 24GB VRAM | 24GB | 8 vCPU 30GB RAM 50GB Storage | New York | $1.10/GPU/hr | Available | ||
Paperspace | NVIDIA Quadro P6000 24GB VRAM | 24GB | 8 vCPU 30GB RAM 50GB Storage | Amsterdam | $1.10/GPU/hr | Available | ||
Paperspace | NVIDIA Quadro P6000 24GB VRAM | 24GB | 8 vCPU 30GB RAM 50GB Storage | Canada | $1.10/GPU/hr | Available | ||
Paperspace | 2×NVIDIA Quadro P6000 24GB VRAM | 24GB | 16 vCPU 60GB RAM 50GB Storage | New York | $1.10/GPU/hr $2.20/hr total (2×) | Available | ||
Paperspace | 2×NVIDIA Quadro P6000 24GB VRAM | 24GB | 16 vCPU 60GB RAM 50GB Storage | Amsterdam | $1.10/GPU/hr $2.20/hr total (2×) | Available |
When to Choose the A10
The A10 suits modern machine learning pipelines requiring high throughput. With 31.2 TFLOPS FP16 performance and 600 GB/s bandwidth, it excels in LLM fine-tuning and Stable Diffusion generation, where the Quadro P6000's 12.6 TFLOPS and 432 GB/s fall short. Lower 150W TDP and $0.60 per hour starting price make it ideal for scalable, cost-efficient deployments across multiple instances.
When to Choose the Quadro P6000
The Quadro P6000 fits legacy professional visualization or certified CAD workflows optimized for Pascal drivers. Its six live cloud offers at $1.10 per hour provide greater availability than the A10's three offers. Despite higher 250W TDP and lower 12.6 TFLOPS, it serves environments locked into older software ecosystems incompatible with Ampere.
Use Cases
The A10's 31.2 TFLOPS FP16 performance doubles the Quadro P6000's 12.6 TFLOPS, enabling faster training epochs. Higher 600 GB/s bandwidth supports larger batches critical for convergence.
A10 achieves 31.2 TFLOPS FP32 for high-throughput serving, outperforming P6000's 12.6 TFLOPS. 600 GB/s bandwidth handles peak query loads without latency spikes.
Ampere's 31.2 TFLOPS and 24 GB GDDR6 optimize mixed-precision fine-tuning, surpassing Pascal's 12.6 TFLOPS. Lower 150W TDP aids prolonged sessions.
A10's 600 GB/s bandwidth and 31.2 TFLOPS accelerate image generation pipelines. Quadro P6000's 432 GB/s limits diffusion step efficiency.
Both offer 24 GB VRAM for simulations, but A10's 31.2 TFLOPS suits FP32-heavy codes while P6000 works for legacy HPC codes. Availability favors P6000 with six offers.
Frequently Asked Questions
Which GPU has higher compute performance, A10 or Quadro P6000?▾
The A10 provides 31.2 TFLOPS in FP16 and FP32, 2.5 times the Quadro P6000's 12.6 TFLOPS. This advantage speeds up AI workloads significantly. Bandwidth at 600 GB/s on A10 further enhances data movement.
How do VRAM and memory bandwidth compare between A10 and P6000?▾
Both GPUs feature 24 GB VRAM, but A10 uses GDDR6 with 600 GB/s bandwidth versus P6000's GDDR5X at 432 GB/s. Higher bandwidth on A10 supports larger models. This impacts batch processing in ML tasks.
What are the power consumption differences?▾
A10 has a 150W TDP, lower than P6000's 250W. This results in better performance per watt at 0.208 TFLOPS/W for A10 versus 0.050 TFLOPS/W for P6000. Efficiency favors A10 in cloud scaling.
Which is cheaper in the cloud, A10 or Quadro P6000?▾
A10 starts at $0.60 per hour with $1.06 average across three offers, undercutting P6000's $1.10 per hour across six offers. A10 delivers more value for performance. Availability is higher for P6000.
What architectures do these GPUs use?▾
A10 runs on Ampere from 2021, supporting latest Tensor Cores. P6000 uses Pascal from 2016 for professional apps. A10 excels in modern ML due to architecture recency.
Can both GPUs handle large language models?▾
Both have 24 GB VRAM suitable for LLMs up to certain sizes. A10's 31.2 TFLOPS and 600 GB/s bandwidth enable faster inference than P6000's 12.6 TFLOPS and 432 GB/s. Choose A10 for speed.
Which is cheaper to rent, the A10 or the Quadro P6000?▾
Cloud rental prices for both the A10 and Quadro P6000 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 P6000?▾
The A10 has 24 GB of GDDR6 memory. The Quadro P6000 has 24 GB of GDDR5X memory.
Can I find A10 and Quadro P6000 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 P6000?▾
The A10 uses the Ampere architecture (2021) while the Quadro P6000 uses Pascal (2016). The A10 delivers 2.5x the FP16 throughput and 1.4x the memory bandwidth of the Quadro P6000.