Complete Checklist for Choosing AI Hosting in 2025

Choosing AI hosting in 2025 is no longer about picking a server with a GPU. With foundation models growing to trillions of parameters, inference latency becoming a competitive edge, and costs, you need the best AI hosting checklist.

In this guide, we will explore the essential checklists that will help you choose the best AI hosting.

AI Hosting Checklist To Choose the Best Option

This guide gives you a complete framework to measure AI hosting providers from GPU specs to compliance, so you can deploy training and inference workloads with confidence.

Whether you are a developer fine-tuning a 7B model or an enterprise training a multi-billion parameter LLM, this checklist ensures you ask the right questions, run the right tests, and avoid costly mistakes.

GPU Requirements for AI Workloads

In this step, we focus on three main parts, including GPU architecture, VRAM capacity, and Tensor Core capability, which engineers can match model size and precision to the right class of hardware, from RTX 4090 up to H100, without guesswork.

1. GPU Model and Architecture: Verify the GPU architecture supports your AI framework and model size.

What to Look For:

• NVIDIA RTX 4090 (24GB GDDR6X): Best for fine-tuning up to 70B models with quantization. Excellent price-performance for inference.
• NVIDIA A5000 (24GB GDDR6): Enterprise-grade, stable drivers, good for medium-scale training.
• NVIDIA A6000 (48GB GDDR6): Ideal for large model training without multi-GPU complexity.
• NVIDIA H100 (80GB HBM3): For large-scale distributed training and high-throughput inference.
• NVIDIA RTX 5090 (32GB GDDR7): Emerging in 2025, best for next-gen models.

Check GPU model and VRAM with the command below:

nvidia-smi --query-gpu=name,memory.total --format=csv,noheader

In your output, you must see something similar to this:

NVIDIA GeForce RTX 4090, 24576 MiB

One of the hosting options you can consider is PerLod Hosting, which offers RTX 4090, A5000, and A6000 servers starting at $543/month with unlimited bandwidth.

2. VRAM Capacity: Ensure VRAM is at least 2x your model size (FP16) or 4x (FP32).

Base rule:

• 7B parameter model: 14GB VRAM (FP16). RTX 4090 (24GB) is sufficient.
• 70B parameter model: 140GB VRAM (FP16). Requires 2x A6000 or 1x H100.

The calculation formula is like this:

VRAM Needed = (Parameters × 2 bytes for FP16 × 2 for overhead) / 1024

For example, for a 13B model, it’s become:

(13,000,000,000 × 2 × 2) / 1024 = 50.78 GB → Needs A6000 or 2x RTX 4090

Tip: PerLod’s A6000 servers with 48GB VRAM handle 70B models with 4-bit quantization.

3. Tensor Core Count: Verify Tensor Core support for mixed-precision training.

What to Check:

• Ampere Architecture (A5000/A6000): 3rd Gen Tensor Cores, supports FP8, FP16, BF16.
• Ada Lovelace (RTX 4090): 4th Gen Tensor Cores, supports FP8, faster inference.
• Hopper (H100): 5th Gen Tensor Cores, best for large-scale training.

Check Tensor Core availability with the command below:

python -c "import torch; print('Tensor Cores:', torch.cuda.get_device_properties(0).major >= 8)"

In your output, you must see:

Tensor Cores: True

CPU and System RAM for AI Models

At this point, you can ensure the CPU, RAM, and platform can keep up with your GPUs, so performance is not wasted on bottlenecks.

By aligning core count, memory capacity, and PCIe bandwidth with your GPU setup, you get stable, predictable throughput for both training and inference workloads.

1. CPU Cores and Clock Speed: CPU should have at least 16 cores at 3.0GHz+ to feed data to GPUs without a bottleneck.

Recommended CPUs include:

• AMD Ryzen 9 7950X (16 cores, 4.5GHz).
• AMD EPYC 7302 (16 cores, 3.0GHz).
• Intel Xeon Gold 6348 (24 cores, 2.6GHz).

Check CPU cores and speed with the command below:

lscpu | grep -E 'CPU(s)|Model name|CPU MHz'

In your output, you must see something similar to this:

CPU(s): 16
Model name: AMD Ryzen 9 7950X
CPU MHz: 4500.000

Tip: You can consider using PerLod’s RTX 4090 servers, which come with AMD Ryzen 9 7950X CPUs.

2. System RAM Capacity: System RAM should be 2-4x total GPU VRAM.

Base Rule:

• 1x RTX 4090 (24GB VRAM): 64GB-128GB RAM.
• 2x A6000 (96GB VRAM): 256GB-384GB RAM.

This matters because system RAM loads datasets, preprocesses data, and manages OS tasks. Insufficient RAM causes GPU starvation.

Check total RAM with the command below:

free -h | grep Mem

In the output, you must see something similar to this:

Mem: 128Gi

Tip: PerLod’s GPU servers offer 64GB-384GB RAM configurations.

3. PCIe Generation and Lanes: Ensure PCIe 4.0 or 5.0 with x16 lanes per GPU.

PCIe bandwidth moves data between CPU RAM and GPU VRAM. PCIe 4.0 x16 provides 32 GB/s, which is enough for most workloads.

Check PCIe version and width with the following command:

nvidia-smi -q | grep -i 'pcie generation|link width'

Example output:

PCIe Generation: 4
Link Width : 16x

Storage Requirements for AI Training Pipelines

In this step, you will learn how much and what kind of storage you need. By combining fast NVMe SSDs for active data, enough capacity for datasets and checkpoints, and reliable S3-style backups, your AI pipeline gets both high throughput and flexibility against data loss.

1. Storage Type and Speed: Use NVMe SSDs with 3,000+ MB/s read/write speeds. AI training loads massive datasets, so slow storage bottlenecks GPU utilization.

Recommended Configurations for storage and speed:

• Boot/OS Drive: 480GB NVMe for OS and software.
• Data Drive: 1-2TB NVMe for datasets and checkpoints.
• Scratch Drive: 1TB NVMe for temporary files.

To test NVMe speed, you can run:

dd if=/dev/zero of=testfile bs=1G count=5 oflag=direct

The write speed should be bigger than 3 GB/s.

Tip: You can consider using PerLod, which includes 1TB NVMe drives standard, with options for 2-4TB upgrades.

2. Storage Capacity: Allocate 10x your dataset size for training AI pipelines.

Calculation looks like this:

Dataset: 100GB
Preprocessed Data: 200GB
Checkpoints (10x): 500GB
Total Needed: 800GB minimum

Tip: PerLod’s 2TB NVMe option handles most datasets for $50/month extra.

3. Backup Storage: Ensure S3-compatible backup storage with 99.9% durability. Model checkpoints and datasets are irreplaceable. Use cross-region replication.

AI Hosting Network Requirements

With high-bandwidth connections like 10Gbps Ethernet for transfers, low-latency interconnects such as NVLink or InfiniBand for GPU coordination, and robust IP security, you avoid delays and ensure seamless scalability across nodes.

1. Bandwidth and Data Transfer: Confirm unmetered 10Gbps bandwidth for multi-node training. Transferring a 1TB dataset at 1Gbps takes 2+ hours. At 10Gbps, it’s 15 minutes.

Test network speed with:

iperf3 -c speedtest.perlod.com -p 5201

2. Latency and Interconnect: For multi-GPU servers, verify InfiniBand or NVLink support. NVLink provides 600GB/s GPU-to-GPU bandwidth, which is critical for distributed training.

3. Public IP and DDoS Protection: Ensure dedicated IPv4 and built-in DDoS protection.

Software Stack Checklist for AI Workloads

In this step, you can ensure the software stack is modern enough to unlock your GPUs’ full capabilities. With up-to-date CUDA and drivers, pre-installed frameworks, and a Kubernetes cluster, you can move from provisioning to training and deployment with minimal setup overhead.

1. CUDA and Driver Support: Verify CUDA 12.x and NVIDIA driver 550+ compatibility. Newer models require CUDA 12 for FP8 support and optimized kernels.

Check CUDA version and NVIDIA driver with the commands below:

nvcc --version
nvidia-smi

Example output:

CUDA version: 12.2
Driver version: 550.90.07

2. AI Frameworks: Ensure PyTorch, TensorFlow, and JAX are pre-installed.

Check PyTorch GPU access with:

python -c "import torch; print('PyTorch GPU:', torch.cuda.is_available())"

Check TensorFlow GPU with:

python -c "import tensorflow as tf; print('TF GPU:', tf.config.list_physical_devices('GPU'))"

Kubernetes and Orchestration: For scale, confirm Kubernetes 1.28+ with the GPU operator. Kubernetes manages multi-node training, autoscaling, and resource allocation.

Scalability and Orchestration for AI Hosting Deployment

By combining fast auto-scaling, multi-region deployments, and features like MIG, you keep costs under control while still meeting reliability and throughput requirements for both training and inference.

1. Auto-Scaling: Verify GPU auto-scaling from 0 to 10+ nodes in under 5 minutes. Auto-scaling saves costs by shutting down idle GPUs.

2. Multi-Region Deployment: Confirm ability to deploy across 3+ regions for redundancy.

3. Multi-Instance GPU (MIG): For inference, verify Multi-Instance GPU support. Split an A100 into 7x 10GB instances, reducing costs for small models.

Cost and Pricing in AI Hosting

This section helps you understand the real cost of GPU hosting, not just the monthly bill, but also hidden fees like data transfer and backup charges. By comparing fixed monthly pricing against hourly rates, you can choose a payment model that fits your budget and workload pattern.

• Pricing Model: Compare hourly vs. monthly vs. reserved pricing.
• Hidden Costs: heck for data transfer, storage, and API call fees. PerLod Hosting has no data transfer fees, free backups up to 100GB.
• Payment Options: Verify crypto payment support for privacy. PerLod accepts Bitcoin, Ethereum, and USDT.

Compliance and Security for AI Services

This section verifies that your hosting provider protects your data legally and technically. By confirming data stays in compliant regions, enabling private signup methods, and deploying strong DDoS and firewall protections, you safeguard both your models and sensitive datasets from regulatory violations and cyberattacks.

• Data Residency: Confirm data stays in your region.
• Privacy-Friendly Signup: Verify anonymous signup and crypto payment options.
• DDoS and Firewall: Ensure 1+ Tbps DDoS protection and configurable firewall.

Support and SLAs Checklist

At this point, you can verify your hosting provider delivers reliability through SLA commitments and responsive support trained in AI infrastructure.

• Uptime Guarantee: Look for a 99.9% uptime SLA with financial credits.
• Support Response Time: Verify 24/7 support with an under-15-minute response for critical issues.
• AI Expertise: Confirm support team understands CUDA, PyTorch, and distributed training.

Backup and Disaster Recovery Checklist

You must also ensure your training progress, datasets, and model checkpoints survive hardware failures.

• Snapshot Frequency: Enable hourly snapshots for active training.
• Cross-Region Replication: Ensure backups replicate to a different region automatically.
• Recovery Time: Test recovery from a snapshot in under 30 minutes.

Data Center and Location Checklist

Choose a provider with 3+ continents for low latency and verify direct peering with major cloud providers. Also, confirm redundant power and liquid cooling for GPUs.

FAQs

Conclusion

This AI hosting checklist is necessary for choosing the best infrastructure for your AI pipelines. VRAM is your most essential resource; match it with adequate CPU, RAM, and fast NVMe storage. Ensure networking can handle your data volumes, and verify the software stack is pre-configured. Also, review costs for hidden fees.

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