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
Compute capabilities define workload suitability between these GPUs. The A100's 312 TFLOPS FP16 significantly outpaces the RTX PRO 6000's 125 TFLOPS, favoring A100 for training phases where half-precision tensor operations dominate deep learning models. Conversely, RTX PRO 6000 achieves FP32 parity at 125 TFLOPS against A100's 19.5 TFLOPS, benefiting single-precision tasks like scientific simulations. The RTX PRO 6000's 2000 TFLOPS FP8 capability accelerates inference on quantized models, reducing latency in deployment scenarios.
Memory specifications impact batch processing efficiency. A100's 2039 GB/s bandwidth exceeds RTX PRO 6000's 1792 GB/s, enabling larger batch sizes in memory-bound training runs up to 80 GB models. RTX PRO 6000 counters with 96 GB VRAM, surpassing A100's maximum 80 GB, which supports bigger models or higher resolutions in inference without swapping. In real-world terms, A100 handles memory-intensive training cycles faster, while RTX PRO 6000 optimizes low-precision inference throughput.
Power efficiency remains equivalent at 400W TDP for both, but interconnects vary: A100's PCIe 4.0 and InfiniBand enhance multi-GPU scaling over RTX PRO 6000's NVLink alone.
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 646GB Storage | Czechia | $1.07/GPU/hr | Available | ||
 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
Select the A100 for cost-sensitive, high-volume AI training where FP16 performance is critical. Its 312 TFLOPS FP16 rate and 2039 GB/s bandwidth support large-batch training on models up to 80 GB, with pricing from $0.60 per hour across 58 offers ensuring broad availability. Mature Ampere ecosystem integration via SXM4, PCIe 4.0, and InfiniBand suits data center clusters.
Legacy software optimized for Ampere also favors A100 over newer Blackwell deployments.
When to Choose the RTX PRO 6000
Opt for the RTX PRO 6000 in inference-heavy pipelines leveraging low-precision formats. The 2000 TFLOPS FP8 and 125 TFLOPS FP32 enable efficient serving of quantized LLMs or vision models up to 96 GB VRAM. Blackwell architecture provides future-proofing for emerging frameworks.
Workstation or PCIe-only setups benefit from its single form factor despite limited 2 cloud offers at $1.69 per hour.
Use Cases
A100's 312 TFLOPS FP16 and 2039 GB/s bandwidth handle large-batch training efficiently. RTX PRO 6000 trails with 125 TFLOPS FP16.
RTX PRO 6000's 2000 TFLOPS FP8 accelerates quantized model serving with 96 GB VRAM. A100 lacks FP8 support.
A100 excels with 312 TFLOPS FP16 for parameter-efficient fine-tuning on 40-80 GB models. Higher bandwidth supports bigger batches.
RTX PRO 6000's 96 GB VRAM and 125 TFLOPS FP32 suit high-resolution image generation. FP8 aids real-time inference.
RTX PRO 6000 balances FP32 at 125 TFLOPS for simulations, with 96 GB VRAM for large datasets. Blackwell offers modern optimizations.
Frequently Asked Questions
Which GPU has more VRAM?▾
The RTX PRO 6000 provides 96 GB GDDR7 VRAM, exceeding the A100's maximum 80 GB HBM2e. This benefits larger models in inference tasks.
What is the FP16 performance difference?▾
A100 achieves 312 TFLOPS FP16, over twice the RTX PRO 6000's 125 TFLOPS. A100 suits FP16-dominant training workloads.
How do memory bandwidths compare?▾
A100 offers 2039 GB/s, higher than RTX PRO 6000's 1792 GB/s. Superior bandwidth on A100 enables larger training batches.
Which is cheaper in the cloud?▾
A100 starts at $0.60 per hour averaging $1.93 across 58 offers, versus RTX PRO 6000 at $1.69 per hour across 2 offers. A100 provides better availability and entry pricing.
Does RTX PRO 6000 support FP8?▾
RTX PRO 6000 delivers 2000 TFLOPS FP8, absent on A100. This boosts low-precision inference efficiency.
What are the form factors?▾
A100 supports SXM4 and PCIe, while RTX PRO 6000 is PCIe only. A100 offers more flexibility for data centers.
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.