Raw compute of NVIDIA A16 versus its generation peers
💡 Answer
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 $0.47/hr) or Cherry Servers — check live availability and spin up in minutes.
More FAQs about NVIDIA A16
Vultr vs Cherry Servers - GPU Provider Comparison (April 2026)
Head-to-head comparison of Vultr and Cherry Servers. Compare GPU models, hourly pricing, billing granularity, spot instances, VRAM, infrastructure, developer tools, Kubernetes support, and compliance before choosing a provider. Data refreshed April 2026.
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Vultr
High-performance cloud GPU across 32 global regions
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Cherry Servers
Bare metal GPU servers with 24 years of hosting experience and full hardware-level control.
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|---|---|---|
| Overview | ||
| Trustpilot Rating | 1.8 | 4.6 |
| Headquarters | United States | Lithuania |
| Provider Type | Multi-Cloud | N/A |
| Best For | AI training inference video rendering HPC Stable Diffusion game development generative AI fine-tuning research | AI training inference fine-tuning rendering research HPC generative AI deep learning |
| GPU Hardware | ||
| GPU Models | A16 A40 L40S A100 PCIe GH200 A100 SXM H100 SXM B200 B300 MI300X MI325X MI355X | A100 A40 A16 A10 A2 Tesla P4 |
| Max VRAM (GB) | 288 | 80 |
| Max GPUs/Instance | 16 | 2 |
| Interconnect | NVLink | PCIe |
| Pricing | ||
| Starting Price ($/hr) | $0.47/hr | $0.16/hr |
| Billing Granularity | Per-hour | Per-hour |
| Spot/Preemptible | Yes | No |
| Reserved Discounts | N/A | N/A |
| Free Credits | Up to $300 free credit for 30 days | None |
| Egress Fees | Standard (varies by plan) | N/A |
| Storage | 350 GB - 61 TB NVMe (included), Block Storage at $0.10/GB/mo, S3-compatible Object Storage | NVMe SSD, Elastic Block Storage ($0.071/GB/mo) |
| Infrastructure | ||
| Regions | 32 regions across 6 continents (Americas, Europe, Asia, Australia, Africa) | Lithuania, Netherlands, Germany, Sweden, US, Singapore (6 locations) |
| Uptime SLA | 100% | 99.97% |
| Developer Experience | ||
| Frameworks | PyTorch TensorFlow CUDA cuDNN ROCm Hugging Face NVIDIA NGC | PyTorch TensorFlow CUDA (bare metal — full stack control) |
| Docker Support | Yes | Yes |
| SSH Access | Yes | Yes |
| Jupyter Notebooks | Yes | No |
| API / CLI | Yes | Yes |
| Setup Time | Minutes | Minutes |
| Kubernetes Support | Yes | Yes |
| Business Terms | ||
| Min Commitment | None | None |
| Compliance | 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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