Specifications Compared
| Spec | A100 | QUADRO-RTX-8000 |
|---|---|---|
| TDP | 400W | 260W |
| VRAM | 40-80 GB | 48 GB |
| CUDA Cores | 6,912 | 4,608 |
| Memory Type | HBM2e | GDDR6 |
| Architecture | Ampere | Turing |
| Form Factors | SXM4, PCIe | PCIe |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | NVLink |
| Tensor Cores | 432 | 576 |
| FP16 Performance | 312 TFLOPS | 16.3 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 16.3 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | |
| INT8 Performance | 624 TOPS | |
| Memory Bandwidth | 2,039 GB/s | 672 GB/s |
Performance Analysis
The A100's FP16 performance reaches 312 TFLOPS, far exceeding the Quadro RTX 8000's 16.3 TFLOPS, which accelerates mixed-precision training and inference in deep learning models. In FP32, the A100 delivers 19.5 TFLOPS against the Quadro's 16.3 TFLOPS, providing a clear advantage for general-purpose computing tasks. This compute disparity means the A100 processes neural network operations nearly 19 times faster in FP16 scenarios.
Memory bandwidth defines handling of large datasets: the A100's 2039 GB/s enables larger batch sizes and quicker data transfers for training massive models, while the Quadro's 672 GB/s limits throughput and increases latency in memory-intensive workloads. The A100's 40-80 GB HBM2e VRAM supports bigger models without swapping, unlike the Quadro's 48 GB GDDR6. Higher TDP of 400W on the A100 reflects its datacenter optimization, compared to 260W on the Quadro for workstation efficiency.
These specs translate to real-world gains: A100 setups train large language models in hours rather than days, whereas Quadro systems suit smaller-scale or legacy inference.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A100
| 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
Select the A100 for AI training and inference workloads demanding high throughput. Its 312 TFLOPS FP16 performance and 2039 GB/s bandwidth excel in processing large models with batch sizes that exceed Quadro RTX 8000 capabilities. Cloud availability from $0.45/hr across 57 offers facilitates scalable deployments via NVLink and InfiniBand.
Datacenter environments benefit from the A100's 40-80 GB HBM2e VRAM for handling datasets up to 80 GB without bottlenecks.
When to Choose the Quadro RTX 8000
Choose the Quadro RTX 8000 for on-premises workstation applications where power efficiency matters. Its 260W TDP consumes less energy than the A100's 400W, suiting compact professional setups. The 48 GB GDDR6 VRAM and NVLink support visualization tasks like CAD rendering without cloud dependency.
Legacy systems or environments lacking A100 cloud access favor the Quadro, especially for FP32 workloads at 16.3 TFLOPS where differences narrow.
Use Cases
The A100's 312 TFLOPS FP16 and 2039 GB/s bandwidth handle massive datasets and large batch sizes essential for LLM training. The Quadro RTX 8000's 16.3 TFLOPS limits scalability.
A100's 40-80 GB HBM2e VRAM supports deploying large LLMs without memory constraints, with 312 TFLOPS FP16 for low-latency responses. Quadro's 48 GB GDDR6 falls short for production-scale inference.
Fine-tuning benefits from A100's 19.5 TFLOPS FP32 and high bandwidth for iterative processes on medium datasets. Quadro's equivalent FP32 at 16.3 TFLOPS offers minimal gains.
A100's superior FP16 performance and VRAM enable faster image generation at higher resolutions. Quadro RTX 8000 suffices for basic use but slows on complex prompts.
A100 excels in parallel simulations via 2039 GB/s bandwidth; Quadro RTX 8000 works for FP32 tasks at 16.3 TFLOPS in power-constrained labs.
Frequently Asked Questions
What is the memory bandwidth difference between A100 and Quadro RTX 8000?▾
The A100 achieves 2039 GB/s with HBM2e memory, while the Quadro RTX 8000 provides 672 GB/s using GDDR6. This gap allows A100 to manage larger data flows in AI workloads. Higher bandwidth reduces bottlenecks during training.
How do FP16 performances compare?▾
A100 delivers 312 TFLOPS in FP16, compared to Quadro RTX 8000's 16.3 TFLOPS. This makes A100 ideal for half-precision deep learning tasks. Inference speeds improve dramatically on A100.
What are the cloud pricing details?▾
A100 offers start from $0.45/hr, averaging $1.92/hr across 57 live deals. Quadro RTX 8000 has no live cloud offers available. This availability drives A100 adoption in cloud environments.
Which GPU has more VRAM?▾
A100 provides 40-80 GB HBM2e VRAM options, exceeding Quadro RTX 8000's fixed 48 GB GDDR6 in capacity and speed. HBM2e suits larger models better. Quadro fits mid-sized professional tasks.
What are the TDP ratings?▾
A100 requires 400W TDP for datacenter performance, versus Quadro RTX 8000's 260W for workstations. Lower TDP on Quadro aids power-sensitive setups. A100 prioritizes compute density.
Which architecture is newer?▾
A100 uses Ampere from 2020, advancing beyond Quadro RTX 8000's Turing from 2018. Ampere optimizations boost AI efficiency. Turing remains viable for graphics-focused work.
Which is cheaper to rent, the A100 or the Quadro RTX 8000?▾
Cloud rental prices for both the A100 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 A100 have compared to the Quadro RTX 8000?▾
The A100 has 40 to 80 GB of HBM2e memory. The Quadro RTX 8000 has 48 GB of GDDR6 memory.
Can I find A100 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 A100 and the Quadro RTX 8000?▾
The A100 uses the Ampere architecture (2020) while the Quadro RTX 8000 uses Turing (2018). The A100 delivers 19.1x the FP16 throughput and 3.0x the memory bandwidth of the Quadro RTX 8000.