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Still the Most Deployed AI Training GPU in Production Data Centers
The H100 gets the headlines. The A100 gets the work done. Despite the H100 launch, the NVIDIA A100 80GB remains the most widely deployed AI training GPU in enterprise data centers — and for good reason. It supports FP64 Tensor Cores for HPC, MIG (Multi-Instance GPU) for secure multi-tenant inference, and NVLink for multi-GPU training at scale. At roughly 55-65% of the H100 price, it is the pragmatic choice for training 13B-70B parameter models and running mixed HPC-plus-AI workloads.
기술 사양
| 매개변수 | 사양 |
|---|---|
| GPU Architecture | NVIDIA Ampere (GA100) |
| CUDA Cores | 6,912 |
| Tensor Cores | 432 (3rd Gen) |
| 메모리 | 80 GB HBM2e |
| Memory Bandwidth | 2,039 GB/초 (HBM2e, 5 stacks) |
| Memory Bus | 5,120-조금 |
| 인터페이스 | PCIe 4.0 x16 (or SXM4 for NVLink) |
| NVLink | 600 GB/초 (PCIe variant: via NVLink Bridge for 2 GPU; SXM4 variant: 까지 8 GPU) |
| FP64 Performance (Tensor Core) | 19.5 TFLOPS |
| FP32 Performance | 19.5 TFLOPS |
| TF32 Tensor Core (with sparsity) | 312 TFLOPS (624 TFLOPS with sparsity) |
| FP16 Tensor Core (with sparsity) | 312 TFLOPS (624 TFLOPS with sparsity) |
| INT8 Tensor Core (with sparsity) | 624 TOPS (1,248 TOPS with sparsity) |
| MIG (Multi-Instance GPU) | 까지 7 isolated GPU instances (10/20/40/80 GB each) |
| TDP (최대) | 300승 (PCIe) / 400승 (SXM4) |
| 폼 팩터 | PCIe: dual-slot FHFL, passive cooling | SXM4: mezzanine module |
| Power Connector | 1x PCIe CEM 8-pin CPU power (PCIe variant) |
| ECC 메모리 | Full HBM2e ECC (not optional — always on for data integrity) |
| 물리적 크기 | PCIe: 267 엑스 112 mm (dual-slot), 1.24 킬로그램 |
| 작동 온도 | 0° C ~ 45 ° C |
| 보증 | 3-년도 (manufacturer) |
A100 vs H100 vs L40S — When Each One Wins
| Decision Factor | A100 80GB | H100 80GB | L40S 48GB |
|---|---|---|---|
| Best for full training (70B+ models) | Good with NVLink | Best. FP8 + Transformer Engine + NVSwitch | Not recommended |
| Best for inference (7B-13B) | Good. 80GB headroom | Overkill for most inference | Best price-performance |
| Best for HPC (FP64) | Best. FP64 Tensor Cores = 19.5 TFLOPS | Limited FP64. A100 is the HPC GPU | No FP64 Tensor Cores |
| Best for MIG / multi-tenant | Excellent. 7 MIG instances | Excellent. 7 MIG instances, higher throughput | 지원되지 않습니다 |
| NVLink multi-GPU | 까지 8 GPU (SXM4) | 까지 8 GPU (SXM5 + NVSwitch) | 지원되지 않습니다 |
| 메모리 | 80 GB HBM2e | 80 GB HBM3 | 48 GB GDDR6 ECC |
| Memory Bandwidth | 2.0 TB/s | 3.35 TB/s | 864 GB/초 |
| 가격 (relative to H100) | ~55-65% | 100% (baseline) | ~25-30% |
Where the A100 Still Outperforms the H100
The A100 has one capability that the H100 deliberately limited: full-speed FP64. The H100 delivers ~34 TFLOPS FP64, but only on its Tensor Cores and at reduced rates compared to A100. For workloads that depend on double-precision math — computational fluid dynamics, molecular dynamics, climate modeling, financial risk simulation — the A100 is actually faster per-dollar than the H100. If your workload mix includes both AI training and traditional HPC, the A100 may be the better fit even at the same price point.
MIG: The Feature That Makes A100 a Cloud GPU
Multi-Instance GPU partitions a single A100 into up to 7 fully isolated GPU instances, each with its own dedicated memory, 은닉처, and compute resources. A cloud provider can sell one physical A100 to seven different customers with guaranteed performance isolation. An enterprise can run seven different inference models on one GPU without crosstalk. MIG is supported on A100 but was deprecated on L40S — if MIG matters to your deployment, A100 (or H100) is the only path.
Compatible Server Platforms
PCIe variant compatible with Dell PowerEdge R760xa, R770, R7715, XE9680, xFusion G5500 V7, 2288H V7, and any server with 300W GPU power delivery and adequate chassis airflow. SXM4 variant requires SXM4-compatible baseboard — contact our engineering team for platform compatibility verification.
Source Through Xincuan
We supply A100 GPUs pre-installed in Dell and xFusion servers, or as upgrade kits for existing platforms. Factory-direct pricing, 3-1년 보증, global shipping, and free GPU architecture consultation.
