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A16 on Vultr

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Vultr's NVIDIA A16 GPU offering delivers enterprise-grade Ampere architecture with 64GB GDDR6 VRAM (4x16GB GPUs) optimized for high-density virtual desktop infrastructure (VDI), remote workstations, and graphics-intensive workloads suitable for ML inference and visualization. This combination is noteworthy for ML engineers seeking global scalability, as Vultr's 32+ data center footprint spans six continents, enabling low-latency deployments near users or data sources. Key value propositions include flexible hourly billing for bursty workloads, integrated cloud services like managed databases, Kubernetes, and object storage, and seamless multi-region replication. The A16 excels in supporting up to 64 vGPUs per card, ideal for concurrent ML model serving or distributed training setups. Compared to compute-focused GPUs, it prioritizes density and cost-efficiency over raw FP32/FP16 throughput (approx. 4 TFLOPS FP32 total), making it a pragmatic choice for production inference endpoints, data annotation tools, or collaborative AI environments without overspending on idle capacity. Limitations include lower peak compute than A100/H100, best for medium-scale AI tasks.

Why NVIDIA A16 on Vultr?

Choose Vultr for NVIDIA A16 when global reach and cost flexibility are priorities. Vultr's 32+ regions minimize latency for worldwide ML deployments, such as edge inference or multi-region training data pipelines, outperforming regional providers. Hourly billing (starting ~$1.50-$2/hour per GPU instance) suits intermittent workloads better than monthly commitments. The provider's high-performance infrastructure—NVMe storage, 10-25Gbps networking, and bare-metal options—complements A16's vGPU slicing for up to 64 users/instances per card, enabling dense ML serving. Integrated services like Vultr Kubernetes Engine (VKE) and block storage streamline scaling, reducing setup time versus DIY clouds. This avoids vendor lock-in while leveraging A16's efficiency in Ampere tensor cores for INT8/FP16 inference, ideal for production without H100 premiums.

Live Pricing

Real-time NVIDIA A16 offers from Vultr

50 offers available
Vultr
Vultr
Atlanta
Sold Out
NVIDIA A168x
64GB VRAM
48 vCPU
496GB RAM
1500GB Storage
$0.47/GPU/hr
$3.77/hr total (8×)
Vultr
Vultr
Bangalore
Sold Out
NVIDIA A1616x
64GB VRAM
96 vCPU
960GB RAM
1700GB Storage
$0.47/GPU/hr
$7.53/hr total (16×)
Vultr
Vultr
Frankfurt
Sold Out
NVIDIA A168x
64GB VRAM
48 vCPU
496GB RAM
1500GB Storage
$0.47/GPU/hr
$3.77/hr total (8×)
Vultr
Vultr
Singapore
Sold Out
NVIDIA A1616x
64GB VRAM
96 vCPU
960GB RAM
1700GB Storage
$0.47/GPU/hr
$7.53/hr total (16×)
Vultr
Vultr
New Jersey
Sold Out
NVIDIA A168x
64GB VRAM
48 vCPU
496GB RAM
1500GB Storage
$0.47/GPU/hr
$3.77/hr total (8×)

Performance Notes

On Vultr, expect strong A16 performance for VDI/ML inference: ~4 TFLOPS FP32, 128 TFLOPS INT8 total, with excellent multi-instance scaling via vGPUs (up to 64 per card). Networking hits 10-25Gbps on GPU-optimized plans, supporting fast data transfers for distributed training. Pair with NVMe SSDs (up to 25K IOPS) for quick dataset loading; object storage integrates for S3-compatible ML pipelines. Multi-GPU clustering possible via Vultr VPCs, though benchmarks are provider-specific and sparse—comparable to AWS/GCP A16 instances for latency-sensitive tasks. No native MIG support like newer Ampere, limiting fine-grained partitioning. CPU pairing (e.g., AMD EPYC) adequate but not top-tier; test for your workload as raw compute trails A40/A100 by 2-5x in heavy training.

About Vultr

A global cloud provider with a massive footprint for deployments across numerous regions.

Best For

Global deployments across 32+ regions

Unique Features

  • Massive global footprint
  • Integrated cloud services
NVIDIA A16 Specs

VRAM

64GB

Architecture

Ampere

Tier

enterprise

Platform Features

Access Methods
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers
Billing Options
Incrementper-hour
Spot Instances
Reserved Instances
Prepaid Credits
Compliance
SOC 2
HIPAA
GDPR
ISO 27001

Getting Started

Getting started with NVIDIA A16 on Vultr is quick via their intuitive cloud console. Select from 32+ regions, deploy hourly instances with pre-configured images, and scale globally. Ideal for ML teams prototyping inference or remote dev environments without long-term commitments.

Steps

  1. 1Sign up for a Vultr account, verify email, and add payment method/funds.
  2. 2Navigate to 'Products' > 'Cloud GPU', select a region close to your users/data.
  3. 3Choose NVIDIA A16 plan (e.g., 1x A16 with 64GB RAM/16 vCPU), pick OS or Marketplace image.
  4. 4Deploy instance; note public IP and root password.
  5. 5SSH in, run 'nvidia-smi' to verify GPU; install CUDA/drivers if needed via NVIDIA repo.

Pro Tips

  • Use Vultr Marketplace ML/Docker images (e.g., Ubuntu + CUDA) to skip manual driver setup and launch in minutes.
  • Enable High Frequency Compute and attach Block Storage for faster ML dataset I/O and persistent models.
  • Monitor with Vultr dashboard + nvidia-dcgm for GPU metrics; set auto-scaling in VKE for dynamic workloads.

Frequently Asked Questions

What is Vultr's billing model for NVIDIA A16?

Vultr bills per-hour for GPU instances including NVIDIA A16. Hourly billing means you pay for full hours even if your job completes mid-hour. Plan your workloads accordingly to maximize cost efficiency.

Does Vultr offer spot instances for NVIDIA A16?

No, Vultr does not currently offer spot instances for NVIDIA A16. All instances are billed at on-demand rates. However, they do offer reserved instances for committed usage, which can provide significant discounts for long-term workloads.

How can I access NVIDIA A16 instances on Vultr?

Vultr provides access to NVIDIA A16 instances via SSH, web-based terminal, programmatic API. SSH access gives you full control over the instance for custom configurations and production deployments. API access enables automation and integration with your existing ML pipelines and CI/CD workflows.

What compliance certifications does Vultr have for NVIDIA A16 workloads?

Vultr maintains SOC 2, HIPAA, GDPR, ISO 27001 certifications, making it suitable for regulated workloads. HIPAA compliance is particularly important for healthcare and medical AI applications. SOC 2 certification demonstrates strong security controls for handling sensitive data. Contact Vultr directly for detailed compliance documentation and BAA agreements if needed.

Can I use NVIDIA A16 with Kubernetes on Vultr?

Yes, Vultr supports Kubernetes for orchestrating NVIDIA A16 workloads. This enables you to deploy scalable ML pipelines, manage distributed training jobs across multiple GPUs, and integrate with MLOps tools like Kubeflow, Argo Workflows, and KServe. Kubernetes support is essential for teams building production-grade ML infrastructure.

What are the specifications of the NVIDIA A16?

The NVIDIA A16 features 64GB of high-bandwidth memory, built on NVIDIA's Ampere architecture. As an enterprise-tier GPU, it's designed for large-scale AI training, inference at scale, and demanding HPC workloads. The substantial VRAM capacity supports large language models, complex neural networks, and multi-model deployments.

What workloads is NVIDIA A16 on Vultr best suited for?

The NVIDIA A16 on Vultr is well-suited for large-scale AI/ML training, LLM fine-tuning, batch inference at scale, and high-performance computing workloads. Vultr specifically excels at: Global deployments across 32+ regions. Consider your model size, training data volume, and latency requirements when evaluating this combination for your specific use case.

Does Vultr offer reserved instances for NVIDIA A16?

Yes, Vultr offers reserved instance pricing for NVIDIA A16, which can provide significant discounts (typically 20-40% off on-demand rates) for committed usage periods. Reserved instances are ideal for predictable, long-running workloads like production inference services, ongoing training pipelines, or development environments that run continuously. Contact Vultr for current reserved pricing and commitment terms.

What unique features does Vultr offer for NVIDIA A16?

Vultr differentiates itself with: Massive global footprint; Integrated cloud services. These features may provide advantages depending on your specific workflow requirements and technical needs. Evaluate how these capabilities align with your ML infrastructure goals when making your decision.

How do I get started with NVIDIA A16 on Vultr?

To get started with NVIDIA A16 on Vultr, visit https://www.vultr.com/?ref=9847371&utm_source=gpuperhour&utm_medium=referral to create an account. Most providers offer a straightforward signup process, and some provide initial credits for new users. Once registered, you can typically launch a NVIDIA A16 instance within minutes through their dashboard or API. We recommend starting with a small experiment to familiarize yourself with the platform before scaling up to larger workloads.

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A16 on Vultr: $0.47/hr (6 in Stock) | GPUPerHour