Specifications Compared
| Spec | A100 | QUADRO-RTX-6000 |
|---|---|---|
| TDP | 400W | 260W |
| VRAM | 40-80 GB | 24 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 compute reaches 312 TFLOPS, far exceeding the Quadro RTX 6000's 16.3 TFLOPS, which accelerates deep learning training where half-precision dominates. FP32 performance shows A100 at 19.5 TFLOPS over Quadro's 16.3 TFLOPS, benefiting general-purpose simulations. This delta translates to faster convergence in neural network training on A100, often by factors of 10 to 20 times in mixed-precision workflows.
Memory bandwidth defines real-world throughput: A100's 2039 GB/s supports massive batch sizes for models like transformers, minimizing data loading stalls that plague the Quadro's 672 GB/s. With 40 to 80 GB VRAM, A100 handles datasets exceeding 24 GB without swapping, ideal for inference on large language models. Quadro's lower TDP of 260 W versus 400 W aids dense deployments but limits peak sustained loads.
Interconnects further differentiate: A100 supports NVLink, PCIe 4.0, and InfiniBand for multi-GPU scaling, while Quadro relies on NVLink alone in PCIe form. This enables A100 clusters for distributed training unattainable on single Quadro units.
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 | 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 | ||
 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
Choose the A100 for AI training and inference workloads requiring high memory capacity. Its 40 to 80 GB HBM2e VRAM and 2039 GB/s bandwidth accommodate large batch sizes in LLM fine-tuning, where the Quadro RTX 6000's 24 GB GDDR6 falls short. Cloud pricing from $0.45 per hour across 57 offers makes it scalable without upfront hardware costs.
Datacenter deployments benefit from A100's 312 TFLOPS FP16 and SXM4 form factor for dense racks.
When to Choose the Quadro RTX 6000
Select the Quadro RTX 6000 for professional visualization tasks like CAD or 3D rendering optimized for Turing drivers. Its 260 W TDP suits power-constrained workstations, lower than A100's 400 W. With no cloud offers, it fits on-premises setups avoiding subscription models.
Use Cases
A100's 312 TFLOPS FP16 and 40 to 80 GB VRAM handle massive models and large batches. Quadro's 16.3 TFLOPS and 24 GB limit scalability.
2039 GB/s bandwidth on A100 supports high-throughput serving. Quadro's 672 GB/s causes bottlenecks for real-time queries.
A100's 19.5 TFLOPS FP32 and high VRAM enable efficient parameter updates on large datasets. Quadro lacks capacity for modern sizes.
A100's FP16 performance accelerates diffusion steps by wide margins over Quadro's 16.3 TFLOPS. Extra VRAM fits high-resolution generations.
A100's PCIe 4.0, InfiniBand, and 312 TFLOPS suit simulations and HPC clusters. Quadro's PCIe-only limits multi-node work.
Frequently Asked Questions
How much faster is A100 than Quadro RTX 6000 in FP16?▾
A100 achieves 312 TFLOPS FP16, about 19 times the Quadro RTX 6000's 16.3 TFLOPS. This gap shines in AI training tasks using half-precision.
What is the VRAM difference between A100 and Quadro RTX 6000?▾
A100 offers 40 to 80 GB HBM2e versus Quadro's 24 GB GDDR6. A100 handles larger models without out-of-memory errors.
Does Quadro RTX 6000 have cloud pricing?▾
No live cloud offers exist for Quadro RTX 6000. A100 starts at $0.45 per hour across 57 providers, averaging $1.92 per hour.
Which has higher memory bandwidth: A100 or Quadro RTX 6000?▾
A100 delivers 2039 GB/s, over three times Quadro RTX 6000's 672 GB/s. This boosts batch sizes in deep learning.
What are the TDP ratings for these GPUs?▾
A100 consumes 400 W, while Quadro RTX 6000 uses 260 W. Quadro fits lower-power workstations better.
Can Quadro RTX 6000 do multi-GPU with NVLink?▾
Quadro RTX 6000 supports NVLink in PCIe form. A100 adds PCIe 4.0 and InfiniBand for advanced clustering.
Which is cheaper to rent, the A100 or the Quadro RTX 6000?▾
Cloud rental prices for both the A100 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 A100 have compared to the Quadro RTX 6000?▾
The A100 has 40 to 80 GB of HBM2e memory. The Quadro RTX 6000 has 24 GB of GDDR6 memory.
Can I find A100 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 A100 and the Quadro RTX 6000?▾
The A100 uses the Ampere architecture (2020) while the Quadro RTX 6000 uses Turing (2018). The A100 delivers 19.1x the FP16 throughput and 3.0x the memory bandwidth of the Quadro RTX 6000.