Flagship AI Power with Up to 80 GB HBM2e & 3rd-Gen Tensor Cores — Ideal for Deep Learning, Inference, HPC & Data Analytics
- Provisioned in 10 minutes
- 20+ Tier‑1 global data centers
- Unmetered 1/10/25 Gbps bandwidth
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NVIDIA A100 Tensor Core GPU Server Price
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🚀 Key Specifications
- GPU Architecture: NVIDIA Ampere GA100 with 312 TFLOPS mixed‑precision tensor performance, 3rd‑gen Tensor Cores.
- Variants & Memory:
- 40 GB HBM2: 1.555 TB/s bandwidth, 250 W (PCIe) / 400 W (SXM).
- 80 GB HBM2e: 1.935 TB/s, 300 W (PCIe) / 400 W (SXM).
- Multi‑Instance GPU (MIG): Up to 7 isolated GPU partitions per A100.
- NVLink/NVSwitch: Supports 2× NVLink (PCIe) or up to 16‑GPU interconnect at 600 GB/s (SXM).
- Compute Per Precision:
- FP64: 9.7 TFLOPS
- TF32: 156 TFLOPS (312 effective)
- FP16/BF16: 312 TFLOPS (624 effective)
- INT8: 624 TOPS (1,248 sparse)
- INT4: 1,248 TOPS (2,496 sparse)
- FP64: 9.7 TFLOPS
⚡ Why Choose A100?
Flagship AI/HPC performance
Delivers ~20× Volta speedups and up to 312 TFLOPS for training and inference workloads .
Elastic and cost‑efficient
MIG allows slicing GPU resources across diverse workloads , maximizing utilization.
Scalable interconnects
NVLink and NVSwitch enable multi‑GPU scaling up to 600 GB/s for HPC or training clusters.
Versatile memory options
40 or 80 GB HBM2(e) match workload needs from inference to LLM training.
Broad software ecosystem
Compatible with CUDA, TensorRT, MPI, MLPerf, PyTorch, TensorFlow, and HPC tools .
🎯 Ideal Use Cases
🔧 Deep Learning Training & Finetuning
Handles large LLMs and transformer-based networks with swift TF32/BF16 performance.
🤖 Multi‑Model Inference Pipelines
MIG enables concurrent serving of multiple AI workloads with guaranteed isolation.
🧪 Scientific & HPC Applications
GNNs, fluid dynamics, and quantum simulations see dramatic speedups on A100 clusters.
📊 Data Analytics & Large‑Scale ETL
Accelerates RAPIDS-based pipelines and real-time analytics with GPU compute.
⚙️ Low‑latency Finance & Real‑Time Workloads
World-class STAC-ML benchmarks show A100 excelling in financial inference and modeling
☁️ GPU‑Powered Repatriation
Replace cloud GPU VMs with predictable bare‑metal performance and no bandwidth fees.
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Deep Dive & FAQs
PCIe vs SXM?
- SXM: Top-tier performance with 400 W TDP and full NVLink/NVSwitch support.
- PCIe: Easier integration into existing racks. Offers similar MIG and software stack compatibility.
Why choose MIG?
Enables up to 7 hardware‑isolated GPU instances, ideal for secure multi-tenant or microservice AI deployment.
Is scaling efficient?
Yes, with NVLink or NVSwitch, clusters scale linearly up to 16 GPUs at 600 GB/s.
Power and cooling?
Our racks support up to 400 W GPUs with advanced cooling, suitable for both PCIe and SXM configurations.
Is there cloud-stack support?
Fully compatible with CUDA 11+, TensorRT, MLPerf, NVIDIA Magnum IO, InfiniBand, and popular frameworks.
Not sure exactly what you need?
No problem! Our talented engineers are here to help!
We will consult, architect, migrate, manage and do whatever it takes to help your business grow and succeed.
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