Specifications Compared
| Spec | A100 | RTX-PRO-6000-BLACKWELL |
|---|---|---|
| TDP | 400W | 400W |
| VRAM | 40-80 GB | 96 GB |
| CUDA Cores | 6,912 | 21,760 |
| Memory Type | HBM2e | GDDR7 |
| Architecture | Ampere | Blackwell |
| Form Factors | SXM4, PCIe | PCIe |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | NVLink |
| Tensor Cores | 432 | 680 |
| FP16 Performance | 312 TFLOPS | 125 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 125 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | |
| INT8 Performance | 624 TOPS | 2,000 TOPS |
| Memory Bandwidth | 2,039 GB/s | 1,792 GB/s |
Performance Analysis
The A100 SXM4 40GB holds a clear edge in FP16 performance: 312 TFLOPS compared to 125 TFLOPS on the RTX PRO 6000. This disparity benefits deep learning training, where half-precision computations dominate, enabling faster convergence on large datasets. Higher FP16 throughput reduces training times for models like transformers.
Conversely, the RTX PRO 6000 achieves balanced FP16 and FP32 at 125 TFLOPS each, surpassing the A100's 19.5 TFLOPS FP32 for workloads blending AI with graphics or simulations. Its FP8 capability at 2000 TFLOPS accelerates quantized inference, allowing higher throughput for deployed models.
Memory profiles diverge sharply: the A100's 2039 GB/s bandwidth sustains larger batch sizes during training, minimizing bottlenecks, while the RTX PRO 6000's 96 GB VRAM capacity handles oversized models that exceed the A100's 40 GB limit, trading some speed for scale in memory-intensive inference.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A100 SXM4 40GB
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 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 | 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×) | |||
Denvr | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 128 vCPU 1024GB RAM 15200GB Storage | Virginia | $1.15/GPU/hr $9.20/hr total (8×) |
When to Choose the A100 SXM4 40GB
Select the A100 SXM4 40GB for FP16-dominated training pipelines. Its 312 TFLOPS FP16 and 2039 GB/s bandwidth outperform the RTX PRO 6000's 125 TFLOPS and 1792 GB/s, supporting efficient multi-GPU scaling via NVLink, PCIe 4.0, and InfiniBand in established datacenter clusters.
When to Choose the RTX PRO 6000 Blackwell
The RTX PRO 6000 Blackwell suits memory-hungry applications: 96 GB GDDR7 VRAM accommodates massive models untunable on 40 GB HBM2e. FP8 performance at 2000 TFLOPS excels in inference, paired with pricing from $0.59 per hour averaging $1.25 per hour for cost-effective cloud deployments.
Use Cases
A100's 312 TFLOPS FP16 significantly outpaces RTX PRO 6000's 125 TFLOPS, accelerating large-scale model training. Superior 2039 GB/s bandwidth supports bigger batches.
RTX PRO 6000's 2000 TFLOPS FP8 and 96 GB VRAM enable high-throughput serving of huge models. This outperforms A100's 40 GB limit for quantized deployments.
A100 excels with 312 TFLOPS FP16 and 2039 GB/s bandwidth for batch-heavy fine-tuning. It handles established Ampere-optimized workflows efficiently.
96 GB VRAM on RTX PRO 6000 fits high-resolution generations without memory constraints. Balanced 125 TFLOPS FP32 aids image synthesis tasks.
A100's 2039 GB/s bandwidth aids bandwidth-sensitive simulations; RTX PRO 6000's 125 TFLOPS FP32 and 96 GB VRAM suit large-scale data processing.
Frequently Asked Questions
Which GPU has more VRAM?▾
The RTX PRO 6000 Blackwell offers 96 GB GDDR7 VRAM. This exceeds the A100 SXM4 40GB's 40 GB HBM2e, enabling larger models.
What are the current cloud prices?▾
A100 SXM4 40GB pricing starts at $1.00 per hour, averaging $2.63 per hour across five offers. RTX PRO 6000 begins at $0.59 per hour, averaging $1.25 per hour across five offers.
Which is better for AI training?▾
A100 SXM4 40GB leads with 312 TFLOPS FP16 versus 125 TFLOPS on RTX PRO 6000. Its 2039 GB/s bandwidth further boosts training efficiency.
How do they compare in inference?▾
RTX PRO 6000 dominates inference via 2000 TFLOPS FP8 and 96 GB VRAM. This supports faster quantized serving than A100's capabilities.
Do they have the same power consumption?▾
Both GPUs feature 400W TDP. Form factors differ: A100 in SXM4 or PCIe, RTX PRO 6000 in PCIe.
What architectures do they use?▾
A100 employs Ampere from 2020 with NVLink and InfiniBand. RTX PRO 6000 uses Blackwell from 2025 with NVLink support.
Which is cheaper to rent, the A100 or the RTX PRO 6000?▾
Cloud rental prices for both the A100 and RTX PRO 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 RTX PRO 6000?▾
The A100 has 40 to 80 GB of HBM2e memory. The RTX PRO 6000 has 96 GB of GDDR7 memory.
Can I find A100 and RTX PRO 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 RTX PRO 6000?▾
The A100 uses the Ampere architecture (2020) while the RTX PRO 6000 uses Blackwell (2025). The A100 delivers 2.5x the FP16 throughput and 1.1x the memory bandwidth of the RTX PRO 6000.