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Instant access to NVIDIA 80GB A100 Tensor Core GPUs—ideal for AI training and data analytics—with hourly pricing for the NVIDIA A100 GPU, global availability, and fast deployment. Rent cloud GPUs on Runpod's AI cloud platform to leverage enhanced performance and flexibility, reducing infrastructure costs while accelerating your AI projects, while ensuring compliance and security at Runpod.
Why Choose NVIDIA A100
The NVIDIA A100 GPU, built on the Ampere architecture, offers unprecedented performance and versatility for AI and machine learning workloads. It's among the best GPUs for running AI models, excelling in both training and inference tasks, making it a top choice for enterprises and researchers aiming to push the boundaries of AI.
Benefits
- Optimized for Large-Scale AI Workloads
With third-generation Tensor Cores, the A100 delivers up to 312 teraFLOPS for AI operations, performing significantly faster than its predecessor, the V100. This makes it ideal for models like GPT-3/4 and BERT. For guidance on the best large language model to run on Runpod, explore our recommendations. - High Memory and Compute Performance
Available in 40GB and 80GB HBM2e models, the A100 offers massive memory capacity and up to 2.0 TB/s bandwidth, enabling rapid processing of extensive datasets essential for large-scale model training. - Compatible with Top AI Frameworks
The A100 integrates seamlessly with popular AI frameworks like PyTorch, TensorFlow, and JAX, leveraging its architecture to maximize performance across diverse AI applications.
Specifications
Below are the key specifications of the NVIDIA A100 GPU. For detailed GPU benchmarks and current GPU pricing at Runpod, refer to our benchmarks and pricing pages.
| Feature | Value |
|---|---|
| Architecture | Ampere GA100 with 54.2 billion transistors (7nm process) |
| CUDA Cores | 6,912 cores |
| Tensor Cores | 432 third-generation Tensor Cores |
| FP64 Performance | 9.7 TFLOPS |
| FP32 Performance | 19.5 TFLOPS |
| TF32 Performance | 156 TFLOPS (up to 312 TFLOPS with sparsity) |
| BFLOAT16 / FP16 Performance | 312–624 TFLOPS |
| Memory Capacity | 40GB HBM2 or 80GB HBM2e |
| Memory Bandwidth (40GB) | 1.6 TB/s |
| Memory Bandwidth (80GB) | Over 2 TB/s |
| Memory Efficiency | 95% DRAM utilization |
| Multi-Instance GPU (MIG) | Partition a single GPU into up to 7 isolated instances |
| Structural Sparsity | Up to 2× speedup for sparse models |
| NVLink | High-bandwidth, low-latency GPU-to-GPU communication |
| Models | PCIe: 250–300W TDP, SXM4: 400W TDP |
| Physical Dimensions | Length: 267mm, Width: 111mm |
FAQ
How does the A100 compare to the H100 or V100?
The A100 offers significant performance improvements over the V100, while the H100 surpasses the A100 in raw metrics. The A100 delivers up to 2.5x faster performance in training large language models compared to the V100, with higher memory bandwidth and more Tensor Core performance. The H100 outperforms the A100 in raw throughput, excelling in advanced AI tasks and large language models. The A100 often provides a strong cost-performance ratio for many fine-tuning, inference, and development workloads. For a deeper look, see our detailed comparison of A100 and H100 GPUs. For comparisons with other GPUs, see our RTX 2000 Ada vs A100 PCIe comparison.
Is the A100 good for inference, or just training?
The A100 excels in both training and inference tasks. For inference, the A100 can handle high-throughput workloads with excellent efficiency. The A100's versatility allows it to scale inference workflows to handle unpredictable workloads, particularly beneficial in cloud environments. Utilizing serverless GPU endpoints can further enhance scalability and efficiency.
Can I run multiple workloads on a single A100 using MIG?
Yes, the A100's Multi-Instance GPU (MIG) capability allows you to partition a single A100 GPU into up to seven isolated instances. Each MIG instance has its own dedicated memory, compute cores, and cache. MIG can boost GPU utilization significantly and is ideal for multi-tenant environments or mixed workloads, allowing multiple users or applications to share a single GPU without interference.
What's the difference between the 40GB and 80GB A100?
The primary difference lies in the memory capacity. The 40GB model is suitable for standard training and inference tasks and is more cost-effective for less demanding applications. The 80GB model doubles the memory, better supporting memory-intensive applications like large-scale NLP and scientific simulations, with improved bandwidth utilization for processing larger datasets. For current pricing on both variants, see the Runpod pricing page.
What frameworks are optimized for A100?
The A100 is compatible with all major AI development frameworks, with strong optimization for TensorFlow, PyTorch, and JAX. The A100 supports CUDA versions above 11.0 and uses cuDNN for deep learning primitives. When renting an A100, ensure you're using the latest versions of these frameworks and the appropriate CUDA toolkit to maximize performance.
What are the current rental rates for the A100?
For current A100 rental rates on Runpod—including on-demand, reserved, and serverless options—refer to the Runpod pricing page.
