How many images per second can NVIDIA A100 SXM (40GB) generate?
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Real performance numbers for NVIDIA A100 SXM (40GB): 312 FP16 TFLOPS / 19.5 FP32 TFLOPS / 1,555 GB/s bandwidth / 40 GB VRAM.
For context, that's the compute envelope used to train and serve most commercial LLMs in production today. Whether you're running research-grade training or latency-sensitive low-latency inference, NVIDIA A100 SXM (40GB) generally hits the throughput envelope expected from its Ampere generation, with the specific number driven by batch size, sequence length, and quantisation choices.
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| Pangkalahatang-ideya | ||
| Rating sa Trustpilot | 4.4 | 3.7 |
| Punong-tanggapan | United States | United States |
| Uri ng Provider | GPU Marketplace | Nakatuon sa GPU |
| Pinakamainam Para sa | AI training inference fine-tuning Stable Diffusion batch processing research LLM serving generative AI | AI training inference fine-tuning Stable Diffusion batch processing rendering research LLM serving generative AI |
| GPU Hardware | ||
| Mga Modelo ng GPU | B200 H200 H100 SXM H100 NVL A100 SXM A100 PCIe RTX 5090 RTX 5080 RTX 5070 Ti RTX 6000 Pro RTX 6000 Ada RTX 4500 Ada RTX A6000 RTX A5000 RTX A4000 L40S L40 A40 A10 RTX 4090 RTX 4080 RTX 4070 Ti RTX 4070 RTX 4060 Ti RTX 4060 RTX 3090 Ti RTX 3090 RTX 3080 Ti RTX 3080 RTX 3070 Ti RTX 3070 Tesla V100 Tesla T4 A2 GTX 1080 | B300 B200 H200 H100 SXM H100 PCIe H100 NVL MI300X A100 SXM A100 PCIe RTX 5090 RTX PRO 6000 L40S L40 RTX 6000 Ada RTX 5000 Ada RTX A6000 RTX A5000 RTX 4090 RTX 4080 SUPER RTX 4080 RTX 4070 Ti RTX 3090 Ti RTX 3090 RTX 3080 Ti RTX 3080 RTX 3070 A40 A30 A2 L4 |
| Max VRAM (GB) | 192 | 288 |
| Max GPUs/Bawat Instance | 8 | 8 |
| Interconnect | NVLink, InfiniBand | NVLink |
| Pagpepresyo | ||
| Simulang Presyo ($/oras) | $0.06/hr | $0.06/hr |
| Granularidad ng Pagsingil | Bawat segundo | Bawat segundo |
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| Nakalaang Diskwento | Hanggang 50% (1-6 na buwan na reserved) | 15-29% (mga plano mula 1 buwan hanggang 1 taon) |
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| Bayad sa Paglabas | Nag-iiba depende sa host ($/TB) | Wala (Libre) |
| Storage | Nag-iiba depende sa host ($/GB/oras, sinisingil habang umiiral ang instance) | Container/Volume ($0.10/GB/buwan), Idle Volume ($0.20/GB/buwan), Network Storage ($0.07/GB/buwan 1TB) |
| Imprastruktura | ||
| Mga Rehiyon | 500+ lokasyon, 40+ data center | 31 global na rehiyon |
| Uptime SLA | Walang pormal na SLA (makikita ang host reliability scores) | 99.99% |
| Karanasan ng Developer | ||
| Mga Framework | PyTorch TensorFlow CUDA vLLM ComfyUI | PyTorch TensorFlow JAX ONNX CUDA |
| Suporta sa Docker | Oo | Oo |
| SSH Access | Oo | Oo |
| Jupyter Notebooks | Oo | Oo |
| API / CLI | Oo | Oo |
| Oras ng Setup | Segundo | Agad-agad |
| Suporta sa Kubernetes | Hindi | Hindi |
| Mga Termino ng Negosyo | ||
| Minimum na Commitment | Wala | Wala |
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