Explore GPU Hosting Alternatives to AWS for AI Training

As you must know, training AI models requires powerful GPUs, and many businesses start with AWS because it is popular and easy to access. But as AI models get bigger and training costs increase, AWS is no longer an efficient option. Nowadays, many GPU cloud platforms offer faster hardware, lower prices, and simpler setups specifically designed for AI workloads. This article explores the best AWS GPU Hosting Alternative.

Whether you are fine-tuning a large language model or running long-term AI workloads, PerLod Hosting offers resources, tutorials, and insights to help you pick the best GPU hosting solution. This guide walks you through AWS alternatives, including cloud GPUs and dedicated servers.

The High Cost of GPU Training on AWS: Why It’s Time to Look Elsewhere?

Initially, AWS is the most common choice for AI teams because it is widely available, offers strong networking capabilities, and integrates seamlessly with other AWS services. Using AWS for heavy training has high costs and some issues:

• Top chips like the H100 and A100 cost a lot per hour.
• You need long-term contracts to get lower prices.
• Popular locations often don’t have enough available machines.
• Extra fees, like data transfer costs, can show up later and surprise you.

For example, AWS p5 instances with 8 H100 (80 GB) GPUs used to cost about 60.54 USD per hour, which is 7.57 USD per GPU hour. In June 2025, AWS cut H100 prices by 44%, bringing the cost down to roughly 4.2 USD per GPU hour, though the exact price depends on the region.

Many other providers are offering production-ready hardware at much lower prices.

The Three Tiers of GPU Clouds: Where to Go After AWS?

In 2025, providers can be clearly grouped into three tiers, each offering a different balance of price, performance, and operational complexity. Understanding these categories is the first step in building a multi-cloud strategy that optimizes both your budget and your workflow.

1. Big cloud companies (Hyperscalers): AWS, Google Cloud, and Azure.

• Most expensive.
• Best integration with other services.
• Not optimized for cost-efficient AI training.

2. Specialized GPU clouds: Lambda, CoreWeave, RunPod, and others.

• Lower prices.
• High-performance clusters.
• Designed for AI workloads.

3. GPU Marketplaces and Dedicated Servers: Vastai, SaladCloud, and PerLod Hosting.

• Very low hourly or monthly pricing.
• Bare-metal access is possible.
• Ideal for long-running workloads or cost-sensitive training.

You can combine these options based on your needs. For long, serious training jobs, it’s usually better to use a specialized GPU cloud or a well-managed GPU Dedicated Server like PerLod Hosting rather than relying only on AWS.

GPU Pricing and Performance Comparison in 2025

The case for using alternatives becomes very clear once you look at the numbers. A basic 2025 price comparison shows that specialized providers can offer H100 GPUs for less than half of what AWS charges.

Also, for long training jobs, renting bare-metal machines by the month can be the cheapest option, which can greatly change the overall cost of training models.

Provider	GPU	Pricing Model	Typical Cost
AWS	H100	Hourly	~4–5 USD/hr per GPU after price drops
Lambda	H100	Hourly	~2.99 USD/hr
RunPod	H100	Hourly	~1.99 USD/hr
CoreWeave	H100	Hourly	~2.2 USD/hr (with discounts available)
Vastai	Varies	Hourly	0.25–1.50 USD/hr (market prices)
SaladCloud	RTX 5090	Hourly	~0.25 USD/hr
PerLod	Dedicated GPU Hardware	Monthly	~595 USD/month

Key point: If you train continuously for weeks or months, PerLod’s monthly bare-metal pricing can beat hourly cloud prices.

Now this question is happening: Which GPU Gets Your AI Model Trained Fastest and Cheapest? The true cost of training isn’t just the price per hour; it’s the total time and money spent to complete the job.

Here is a hardware benchmark overview:

H100:

• 9× speedup vs A100 on training.
• Best-in-class throughput.
• Excellent scaling.

A100:

• Good for large LLM training.
• More affordable in many clouds.

RTX 4090 / 5090:

• Exceptional performance for fine-tuning.
• Best price-to-performance.
• Limited VRAM for very large models.
• No NVLink.

Dedicated GPU hardware like PerLod GPU servers:

• Zero virtualization overhead.
• Full hardware control.
• Perfect for long and stable runs.
• Often more predictable performance.

AWS GPU Hosting Alternative: Real World Examples

Here is a direct comparison of the providers that are now competing with AWS for GPU training. This gives you a clear view of where to get high-end clusters, where to prototype cheaply, and how new bare-metal options like PerLod offer a different kind of value.

1. Lambda GPU Cloud: It is a GPU-focused cloud used by many AI teams, with data center-grade clusters and good support for multi-node training.

GPUs: H100, A100, H200, GH200.
Pricing: H100 at ~2.99 USD/hr.

Advantages:

• Strong cluster support.
• Reliable multi-GPU networking.
• Clean pricing.
• Excellent for training Llama, Mistral, and diffusion models.

Use Lambda when you need powerful datacenter GPUs without AWS costs.

2. RunPod: It offers on-demand GPUs, multi-node clusters, and serverless-style GPU endpoints with a strong focus on AI inference and fine-tuning.

GPUs: 4090, 3090, L4, A100, H100.
Pricing: H100 from ~1.99 USD/hr, 4090 from ~0.34 USD/hr.

Advantages:

• Easy templates.
• Serverless GPU endpoints.
• Very attractive pricing.

3. CoreWeave: Best for enterprise-scale GPU clusters, and great for massive training jobs that need reliability and scaling.

GPUs: H100, L40S, A100.
Pricing: ~2.2 USD/hr for H100 with long-term discounts.

Advantages:

• No egress fees.
• Kubernetes-native.
• Very high-speed networking.

4. Vastai and SaladCloud: Best for very low-cost experimental workloads.

GPUs: 4090, 5090, A100, etc.
Pricing: As low as ~0.25 USD/hr for 5090. Marketplace pricing varies.

Advantages:

• Best price in the industry.
• Extremely flexible hardware options.

Notes: Hosts can vary in quality, and Uptime is not as reliable as big enterprise clouds.

5. PerLod, Dedicated Bare-Metal GPU Servers: PerLod provides dedicated GPU servers with full hardware access. Unlike GPU clouds that bill hourly and use virtualized instances, PerLod gives you real bare-metal machines, ideal for long-term and stable workloads.

GPUs Available: NVIDIA RTX 4090, NVIDIA RTX A5000, Other GPU server options.

Example Plan, NL-GPU V3-4090T:

• RTX 4090 (24 GB)
• 128 GB RAM
• 1 TB NVMe SSD
• $595.50 per month
• 50 TB bandwidth
• Locations include the Netherlands and Russia

Advantages of PerLod GPU Dedicated Servers:

• No virtualization overhead.
• Full root access.
• Stable performance for long training jobs.
• Predictable monthly pricing.
• Ideal for continuous LLM training, diffusion model training, and HPC workloads.
• Great for teams who prefer dedicated servers for security and consistency.

Use PerLod GPU Dedicated Servers for:

• Long-term LLM fine-tuning.
• Diffusion model training.
• Vision models that need 24/7 dedicated compute.
• Cases where GPU isolation is important.
• Teams that prefer owning the hardware experience without buying machines.

Practical Deployment Guide: GPU Cloud Setup

At this point, you can learn how to go from a blank GPU instance to a running training job on any major cloud provider.

We assume you have an Ubuntu 22.04 image with an NVIDIA GPU, root or sudo access, and NVIDIA drivers preinstalled by the provider.

First, you must launch your GPU machine. For PerLod, choose Ubuntu 22.04, select a GPU server like RTX 4090, get the dedicated server credentials, and SSH into the machine:

ssh root@YOUR_SERVER_IP

Verify the NVIDIA GPU with the command below:

nvidia-smi

You should see a table with the GPU name and driver version. If this fails, follow your provider’s driver installation guide.

You can use Docker’s official script for a lab or personal project. Install Docker with the commands below:

curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh

Verify it with:

docker version

Add your user to the Docker group:

sudo usermod -aG docker $USER

Log out and back in to apply the changes.

Install the NVIDIA container toolkit that lets Docker talk to the GPU. Install the required packages and dependencies:

sudo apt update && sudo install -y --no-install-recommends \
    curl gnupg2

Add its GPG key and repository with the commands below:

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \
  | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg

curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \
  | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' \
  | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

Run the system update and install the toolkit:

sudo apt update
sudo apt install nvidia-container-toolkit -y

Configure Docker and restart it with the commands below:

sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

You can test GPU access inside Docker with the following command:

docker run --rm --gpus all nvidia/cuda:12.5.0-base-ubuntu22.04 nvidia-smi

You should see the same GPU table as before, which confirms that Docker sees the GPU.

Now you can run a small PyTorch training task. Pull a container image that already has PyTorch and CUDA:

docker pull pytorch/pytorch:2.3.0-cuda12.1-cudnn9-runtime

Create a project directory and navigate to it:

sudo mkdir -p ~/ai-project
cd ~/ai-project

Inside the project directory, create a simple training script, train.py:

sudo vi train.py

Add the following script to the file:

import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using device:", device)

# Simple dummy training loop on random data
model = torch.nn.Linear(1024, 1024).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
loss_fn = torch.nn.MSELoss()

for step in range(200):
    x = torch.randn(32, 1024, device=device)
    y = torch.randn(32, 1024, device=device)

    optimizer.zero_grad()
    out = model(x)
    loss = loss_fn(out, y)
    loss.backward()
    optimizer.step()

    if step % 20 == 0:
        print(f"step {step}, loss {loss.item():.4f}")

Finally, run the script in a GPU-enabled container:

docker run --rm -it --gpus all \
-v $PWD:/workspace \
-w /workspace \
pytorch/pytorch:2.3.0-cuda12.1-cudnn9-runtime \
python train.py

You should see the loss printing, and the GPU will be under load if you check nvidia-smi in another shell.

Tip: You can also check this guide on how to serve AI models using Docker on a dedicated GPU server. This guide walks you through container setup, GPU access, and running AI workloads efficiently.

FAQs

Conclusion

At this point, you have understood that AWS is powerful but costly, specialized GPU clouds like Lambda, RunPod, and CoreWeave are faster and cheaper, marketplaces like Vastai offer the lowest hourly rates, and PerLod Hosting provides monthly dedicated GPUs with full control, which makes it ideal for long-running training with stable, predictable costs.

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