Raw compute of NVIDIA A16 versus its generation peers
Sagot
Peak performance on NVIDIA A16: 72 FP16 TFLOPS, 18 FP32 TFLOPS, 800 GB/s memory bandwidth. Those figures cap theoretical throughput, but real-world performance varies based on kernel efficiency, batch size, and model shape.
For pre-training, expect near-peak utilisation on well-optimised frameworks (PyTorch with Flash Attention, DeepSpeed, Megatron-style tensor parallelism). For serving, KV-cache bandwidth is usually the bottleneck — which is why the 800 GB/s figure often predicts latency better than FP16 TFLOPS.
On ML benchmarks NVIDIA A16 lands in the tier you'd expect from its Ampere generation, with strong performance-per-watt given the 64 GB VRAM capacity.
Deploy NVIDIA A16 on Vultr (from $0.47/hr) or Cherry Servers — check live availability and spin up in minutes.
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Mataas na pagganap na cloud GPU sa 32 pandaigdigang rehiyon
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Cherry Servers
Bare metal GPU servers na may 24 na taon ng karanasan sa hosting at kumpletong kontrol sa antas ng hardware.
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|---|---|---|
| Pangkalahatang-ideya | ||
| Rating sa Trustpilot | 1.8 | 4.6 |
| Punong-tanggapan | United States | Lithuania |
| Uri ng Provider | Multi-Cloud | Hindi naaangkop |
| Pinakamainam Para sa | Pagsasanay ng AI inference video rendering HPC Stable Diffusion pag-develop ng laro generative AI fine-tuning pananaliksik | AI training inference fine-tuning rendering research HPC generative AI deep learning |
| GPU Hardware | ||
| Mga Modelo ng GPU | A16 A40 L40S A100 PCIe GH200 A100 SXM H100 SXM B200 B300 MI300X MI325X MI355X | A100 A40 A16 A10 A2 Tesla P4 |
| Max VRAM (GB) | 288 | 80 |
| Max GPUs/Bawat Instance | 16 | 2 |
| Interconnect | NVLink | PCIe |
| Pagpepresyo | ||
| Simulang Presyo ($/oras) | $0.47/hr | $0.16/hr |
| Granularidad ng Pagsingil | Kada oras | Kada oras |
| Spot/Preemptible | Oo | Hindi |
| Nakalaang Diskwento | Hindi naaangkop | Hindi naaangkop |
| Libreng Kredito | Hanggang $300 libreng credit para sa 30 araw | Wala |
| Bayad sa Paglabas | Standard (nag-iiba depende sa plano) | Hindi naaangkop |
| Storage | 350 GB - 61 TB NVMe (kasama), Block Storage sa $0.10/GB/buwan, S3-compatible Object Storage | NVMe SSD, Elastic Block Storage ($0.071/GB/buwan) |
| Imprastruktura | ||
| Mga Rehiyon | 32 rehiyon sa 6 na kontinente (Americas, Europe, Asia, Australia, Africa) | Lithuania, Netherlands, Germany, Sweden, US, Singapore (6 na lokasyon) |
| Uptime SLA | 100% | 99.97% |
| Karanasan ng Developer | ||
| Mga Framework | PyTorch TensorFlow CUDA cuDNN ROCm Hugging Face NVIDIA NGC | PyTorch TensorFlow CUDA (bare metal — full stack control) |
| Suporta sa Docker | Oo | Oo |
| SSH Access | Oo | Oo |
| Jupyter Notebooks | Oo | Hindi |
| API / CLI | Oo | Oo |
| Oras ng Setup | Minuto | Minuto |
| Suporta sa Kubernetes | Oo | Oo |
| Mga Termino ng Negosyo | ||
| Minimum na Commitment | Wala | Wala |
| Pagsunod sa Batas | SOC 2+ (HIPAA) PCI ISO 27001 ISO 27017 ISO 27018 ISO 20000-1 CSA STAR Level 1 | ISO 27001 ISO 20000-1 GDPR PCI DSS |
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