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 GPU pricing at RunPod, refer to our benchmarks and pricing pages.

FeatureValueArchitectureAmpere GA100 with 54.2 billion transistors (7nm process)CUDA Cores6,912 coresTensor Cores432 third-generation Tensor CoresPrecision Performance (FP64)9.7 TFLOPSPrecision Performance (FP32)19.5 TFLOPSPrecision Performance (TF32)156 TFLOPS (up to 312 TFLOPS with sparsity)Precision Performance (BFLOAT16/FP16)312-624 TFLOPSMemory Capacity40GB HBM2 or 80GB HBM2eMemory Bandwidth (40GB model)1.6 TB/sMemory Bandwidth (80GB model)Over 2 TB/sMemory Efficiency95% DRAM utilizationMulti-Instance GPU (MIG)Partition a single GPU into up to seven isolated instancesStructural SparsityUp to 2x speedup for sparse modelsNVLinkHigh-bandwidth, low-latency GPU-to-GPU communicationModelsPCIe: 250-300W TDP, SXM4: 400W TDPPhysical DimensionsLength: 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. Specifically, the A100 delivers 1.95x-2.5x faster performance in training large language models compared to the V100. It also offers higher memory bandwidth (1.6-2.0 TB/s vs 900 GB/s) and more Tensor Core performance (312 TFLOPS vs 125 TFLOPS for FP16). In contrast, the H100 outperforms the A100 in raw performance (up to 989 TFLOPS vs 312 TFLOPS for FP16), excelling in advanced AI tasks and large language models, being about 9x faster than the A100 for these specific applications. For a deeper look into the differences between A100 and H100 GPUs, see our detailed comparison. The A100 often provides a better cost-performance ratio for many workloads, making it an excellent balance between performance and cost-efficiency for most AI and machine learning tasks. For comparisons with other GPUs, like the RTX 2000 Ada, see our RTX 2000 Ada vs A100 PCIe comparison. However, for cutting-edge AI research or extremely large models, the H100 might be worth the extra investment.

Is the A100 good for inference, or just training?

The A100 excels in both training and inference tasks, making it a versatile choice for AI workflows. For inference, the A100 can handle up to 12,800 inferences per second on complex LSTM models. It has been used in financial services for ultra-low-latency inference, critical for applications like high-frequency trading. The A100's versatility allows it to efficiently scale inference workflows to handle unpredictable workloads, particularly beneficial in cloud environments. Utilizing serverless GPU endpoints can further enhance scalability and efficiency. Unlike some GPUs that specialize in either training or inference, the A100's architecture makes it highly effective for both tasks. This dual capability can be especially valuable if your workloads involve a mix of training and inference or if you need the flexibility to switch between the two. For further considerations for choosing between H100 and A100, see our detailed guide.

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 by up to 7x compared to non-MIG-enabled GPUs. It's ideal for multi-tenant environments or mixed workloads, allowing multiple users or applications to share a single GPU without interference. MIG instances can be dynamically configured using NVIDIA's NVML APIs or the nvidia-smi tool. However, enabling MIG requires a GPU reset and reconfiguration of system services, which might be a limitation in some operational environments. Also, workloads that require the entire GPU's resources, like training very large models, may not benefit from MIG partitioning.

What's the difference between the 40GB and 80GB A100?

The primary difference lies in the memory capacity, which affects the types of workloads each model can handle efficiently. The 40GB model is suitable for standard training and inference tasks and is more cost-effective for less demanding applications but may face bottlenecks with extremely large models. The 80GB model doubles the memory to 80GB, better supporting memory-intensive applications like large-scale NLP and scientific simulations. It offers improved memory bandwidth utilization for processing larger datasets and is ideal for cutting-edge AI research and production deployments involving massive datasets. For specific models, such as the Qwen/QwQ-32B from Hugging Face, see GPU requirements for Qwen/QwQ-32B. The 80GB model is significantly more expensive, so it's most suitable for enterprises or researchers handling workloads that can fully utilize the additional memory. For many standard AI and ML tasks, the 40GB model provides an excellent balance of performance and cost-efficiency.

What frameworks are optimized for A100?

The A100 is compatible with all major AI development frameworks, but some are more optimized than others. TensorFlow is fully optimized for training large-scale deep learning models using mixed precision on the A100. PyTorch is widely compatible but requires the correct CUDA version (sm_80) for optimal performance on the A100. JAX accelerates numerical computing and machine learning experiments on the A100 through XLA (Accelerated Linear Algebra compiler). Additionally, the A100 supports CUDA versions above 11.0 and uses cuDNN for deep learning primitives. Major cloud providers like AWS, Google Cloud, and Microsoft Azure offer A100-based GPU instances, making these optimized frameworks widely accessible. When renting an A100, ensure you're using the latest versions of these frameworks and the appropriate CUDA toolkit to maximize performance. Some older libraries or software optimized for previous architectures, like the V100, may require modifications to fully benefit from the A100's capabilities.

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