Specifications Compared
| Spec | A10 | QUADRO-RTX-8000 |
|---|---|---|
| TDP | 150W | 260W |
| VRAM | 24 GB | 48 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
Higher floating-point performance defines the A10's edge: 31.2 TFLOPS FP16 and FP32 enable roughly twice the throughput of the Quadro RTX 8000's 16.3 TFLOPS, accelerating model training epochs and inference latency in compute-limited scenarios. For deep learning training, this delta halves time on forward and backward passes; inference benefits similarly for batch processing.
VRAM and bandwidth shape memory-bound operations. The Quadro RTX 8000's 48 GB GDDR6 supports larger batch sizes or models than the A10's 24 GB, crucial for inference on massive LLMs where data exceeds 24 GB thresholds. Bandwidth remains close at 672 GB/s versus 600 GB/s, so transfer bottlenecks affect both minimally in typical pipelines.
Efficiency tilts toward the A10 with 150W TDP against 260W, yielding superior performance per watt at 0.208 TFLOPS/W FP32 versus 0.063 TFLOPS/W. Ampere's architectural advances over Turing further optimize tensor cores for mixed-precision AI tasks.
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 |
When to Choose the A10
The A10 stands out for cloud-based AI training and inference prioritizing speed and availability. Its 31.2 TFLOPS doubles the Quadro RTX 8000's output, paired with $0.60 per hour starting pricing across three live offers. Lower 150W TDP suits dense multi-GPU racks without excessive cooling demands.
Select the A10 for modern workloads like fine-tuning where Ampere architecture and 600 GB/s bandwidth deliver efficient scaling.
When to Choose the Quadro RTX 8000
The Quadro RTX 8000 fits memory-intensive applications demanding 48 GB VRAM, such as loading oversized models infeasible on the A10's 24 GB. NVLink interconnect facilitates multi-GPU communication absent on the A10, enhancing distributed training coherence.
Choose it for on-premises professional viz or legacy Turing-optimized software where 672 GB/s bandwidth supports high-resolution rendering.
Use Cases
A10's 31.2 TFLOPS FP16 doubles Quadro RTX 8000's 16.3 TFLOPS for faster epochs. 150W TDP enables longer runs efficiently.
Quadro RTX 8000's 48 GB VRAM handles larger models than A10's 24 GB. Suited for high-memory inference batches.
A10 achieves 31.2 TFLOPS FP32, outperforming 16.3 TFLOPS for quicker iterations. Cloud pricing from $0.60/hr adds value.
Ampere's 31.2 TFLOPS accelerates diffusion steps over Turing's 16.3 TFLOPS. 600 GB/s bandwidth supports image generation flows.
A10 offers higher 31.2 TFLOPS for compute-heavy sims; Quadro RTX 8000's 48 GB VRAM aids large datasets.
Frequently Asked Questions
Which GPU performs better in FP32?▾
The A10 delivers 31.2 TFLOPS FP32, double the Quadro RTX 8000's 16.3 TFLOPS. This boosts training and simulation speeds. Ampere architecture enhances tensor efficiency over Turing.
What is the VRAM difference between A10 and Quadro RTX 8000?▾
Quadro RTX 8000 has 48 GB GDDR6, twice the A10's 24 GB. Larger VRAM fits bigger models or batches. Bandwidth favors Quadro RTX 8000 slightly at 672 GB/s over 600 GB/s.
How do power consumptions compare?▾
A10 uses 150W TDP, half the Quadro RTX 8000's 260W. This yields better efficiency at 0.208 TFLOPS/W FP32. Ideal for cloud density.
Is the A10 available in the cloud?▾
A10 offers start from $0.60 per hour, averaging $1.06 per hour across three live providers. Quadro RTX 8000 has no live cloud offers. Pricing drives A10 adoption.
Which supports multi-GPU better?▾
Quadro RTX 8000 includes NVLink for faster interconnect, unlike A10. PCIe on both limits scaling otherwise. NVLink aids distributed training.
What architectures do they use?▾
A10 employs Ampere from 2021; Quadro RTX 8000 uses Turing from 2018. Newer Ampere doubles FP performance to 31.2 TFLOPS. Turing suits legacy apps.
Which is cheaper to rent, the A10 or the Quadro RTX 8000?▾
Cloud rental prices for both the A10 and Quadro RTX 8000 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 8000?▾
The A10 has 24 GB of GDDR6 memory. The Quadro RTX 8000 has 48 GB of GDDR6 memory.
Can I find A10 and Quadro RTX 8000 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 8000?▾
The A10 uses the Ampere architecture (2021) while the Quadro RTX 8000 uses Turing (2018). The A10 delivers 1.9x the FP16 throughput and 1.1x the memory bandwidth of the Quadro RTX 8000.