NVIDIA Tesla A100 80 GB SXM4 GPU (699‑2G506‑0210‑300) - Used / Tested
NVIDIA Tesla A100 80 GB SXM4 GPU (699‑2G506‑0210‑300)
Industry-Leading Ampere GPU with 80 GB HBM2e and NVLink for AI & HPC
The NVIDIA Tesla A100 80 GB SXM4 GPU is built on the Ampere architecture, delivering unparalleled performance for deep learning, high-performance computing, and data analytics. With its 80 GB of HBM2e memory and 2 TB/s bandwidth, it excels at large-scale model training and multi-GPU inference. Available in the SXM4 form factor with NVLink for optimized multi-GPU scaling, this module suits data center GPU-accelerated servers and AI clusters.
🔧 Product Specifications for NVIDIA Tesla A100 80 GB SXM4 GPU
| Feature | Details |
|---|---|
| Model / Part Number | NVIDIA Tesla A100 80 GB SXM4 (699‑2G506‑0210‑300) |
| Architecture | NVIDIA Ampere (GA100 GPU) |
| CUDA Cores | 6,912 |
| Tensor Cores | 432 |
| GPU Memory | 80 GB HBM2e |
| Memory Bandwidth | 2,039 GB/s |
| FP64 Performance | 9.7 TFLOPS |
| FP32 Performance | 19.5 TFLOPS |
| TF32 (Tensor Float32) | 156 TFLOPS |
| FP16 / BF16 | 312 TFLOPS |
| INT8 Tensor Core | 1,248 TOPS |
| Form Factor | SXM4 module (for GPU-accelerated servers) |
| Cooling | Passive (server-provided airflow) |
| NVLink Bandwidth | Up to 600 GB/s |
| MIG Support | Up to 7 GPU instances |
| TDP | 400 W |
| Weight | ~2 lb (heatsink only); full module ~11 lb estimated |
| Die Size / Transistors | 826 mm², ~54B transistors |
| Certifications | NVLink, MIG, DGX/AEP compatible |
❓ Frequently Asked Questions (FAQs)
Q1: What workloads is the A100 SXM4 best suited for?
A: It’s ideal for deep learning training, HPC simulations, large language model workloads, and AI inference at scale.
Q2: How does SXM4 differ from PCIe variants?
A: SXM4 provides higher power (400 W), superior cooling, and NVLink support—offering up to 600 GB/s interconnect bandwidth, enhancing multi-GPU performance.
Q3: Does it support Multi-Instance GPU (MIG)?
A: Yes, it supports up to 7 independent GPU instances, enabling fine-grained resource partitioning.
Q4: What precision formats does the A100 support?
A: Supports FP64, FP32, TF32, FP16, BF16, INT8, and INT4—excellent for both training and inference performance.
Q5: What are thermal and power requirements?
A: Requires a 400 W thermal envelope with robust server airflow; the heatsink alone weighs ~2 lb.
