
When you need high-powered resources to power complex calculations, the more you have the better. In a recent interview, Elon Musk claimed that he heard of a gigawatt class data center being built. Many responders think that Elon is referring to a rumor that –deep in the deserts of Kuwait–, the construction of a new data center is being built to compete with today’s most powerful compute power resources.

Elon Musk likely heard the rumor from [news](https://www.theinformation.com/articles/ai-agenda-the-mysterious-ai-data-center-startup-hiring-from-aws-azure-meta) of a startup poaching technology workers from AWS, Azure, Meta, and other large Silicon Valley companies. In 2023, the race to be a leader in artificial intelligence and machine learning became even more competitive. To be the best, organizations need immense GPU power, usually outsourced to the cloud for its quick deployment and scaling. Large cloud providers built the data centers to power calculations, and businesses can rent the compute resources necessary to power any project – from small personal projects to large enterprise-tier analytics.

The rumor of a Kuwait data center came in the summer of 2023. A company named Omniva –also known as Moneta Tech, and Moneta Systems– was interested in building the most powerful data center in the world. Their goal was to rival Google and Microsoft in technology. Originally, the data center idea was targeted towards cryptomining, but later became an idea to target businesses innovating for the latest artificial intelligence boom.

As more information became available, the gigawatt claims began to unravel. The facility was said to be about 430,000 square feet with total available power of 864 megawatts. A proposed budget of $200 million immediately sent red flags. As a benchmark, it’s reported that a Google data center costs between $1000 and $3000 per square foot. This means that at the very cheapest, a data center of Omniva’s size should cost $430 million, over double the proposed budget.

Cooling systems were ignored for manned spaces in exchange for employee cooling helmets. Fire systems were removed in exchange for fire extinguishers. Anyone with data center experience knows that the proposed planning and safety aren’t feasible.

Next is Omniva’s claim to offer gigawatts of power to customers. NVIDIA continues to dominate GPU performance and offer the best in compute power. To reach the compute power rumored to be available, Omniva would need the latest generation of GPUs, namely the new NVIDIA Blackwell B100. The B100s haven’t hit the market yet, but NVIDIA teased consumers with the promise that their next generation GPUs should launch in 2024.

As of today, any facility rumored to be built with gigawatt power has never been confirmed. The power promised in 2023 wasn’t available, and many of the plans presented to the public didn’t have the backing of professionals. The cloud GPU space still has the computational power to support artificial intelligence and cryptomining, but businesses and consumers should stick to a trusted provider.

For the absolute best in compute power with the latest in GPU technology, [Vast.ai](https://vast.ai/) has the resources you need for machine learning, artificial intelligence, and other high-powered analytics projects for your cloud GPU needs.


Coming up with just the right words, and keeping track of the ideas that you’re weaving together - this is difficult work. The difficulty can even make it seem that there might be better ways to be spending your time.

Is a Gigawatt Class Data Center Being Built in Kuwait?


# Announcing Virtual Machine Rental on Vast.ai

Have you ever thought about what you could accomplish with more flexibility in your GPU rentals? At Vast.ai, we're always looking for ways to innovate and improve our platform, and today we're excited to announce our latest feature: [virtual machine (VM) rental](https://vast.ai/docs/instance-setup/virtual-machines).

We now offer full VMs alongside Docker-based instance rentals. This new offering is designed to support larger customers and more diverse workflows via GPU remote desktop.

Here's everything you need to know to get started!

## Virtual Machine Rental: Expanding the Possibilities

The new VM rental option will help meet the demand for use cases that Docker can't easily support. **VMs also offer features not available in Docker, such as support for init managers and process tracing.**

### Key Benefits of Virtual Machine Support Include:

- **Enhanced Flexibility:** Users can run low level debugging and performance profiling utilities and are not constrained by container environments.
- **Kernel-space Customizations:** Developers gain granular control over their systems, enabling custom driver installations, kernel tweaks, and integration of specialized frameworks.
- **Faster Iteration:** Rather than pre-building and pushing custom Docker images, users can run Docker inside the instance.
- **Improved Security:** Virtual Machines offers an improved  security posture for sensitive workloads.

Host machines can now tap into these advantages, allowing them to attract more renters and capitalize on the increased need for GPU-powered tasks beyond Docker’s capabilities.

### Important VM Differences to Know:

VMs do come with some trade-offs compared to Docker-based instances. For example, they have slower creation and boot times, higher disk overhead, and users will have to deal with a smaller selection of machines and lesser availability of preconfigured templates. In addition, only SSH launch mode is currently supported.

Notably, the on-start script field allows users to specify a script to run on instance start. Unlike in Docker-based instances, the interpreter must be specified by a shebang.

Another difference is that **the cloud copy utility for VMs only allows migrating the entire VM instead of allowing individual folders to be copied.**

Despite these drawbacks, VMs open up new opportunities for both renters and hosts, offering greater flexibility, broader use cases, and the potential for higher earnings.

### Empowering Renters, Rewarding Hosts

One thing that sets us apart from other providers is our focus on cost-efficiency. We strive to keep rental costs low for users across the board – and in fact, Vast.ai GPU rentals are ~3-5X cheaper than current alternatives.

At the same time, we want to provide hosts with the opportunity to earn more.

As our COO, Travis Cannell, explains, "We've always believed that democratizing GPU access means offering maximum flexibility. Virtual Machine support was the natural next step, providing more control, ease of use, and enabling researchers to build and iterate."

With the opportunity for both renters and hosts to benefit, it's equally important to ensure that setup is seamless and operations remain secure. Here's how we make that happen.

## VMs on Vast: Simple Setup and Strong Security

Getting started with VM rental on Vast.ai is simple. **Our hosting software automatically tests host machines for VM compatibility and enables the feature on any machine where it's supported.** (There is no requirement to be VM compatible, however.)

For new host machines, these tests will be run upon installation. For machines configured before the VM feature release, testing for VM compatibility will happen at a time when the machine is unoccupied.

And that's it! Host machines that don't have VM support enabled will simply be hidden from search results when VM-based templates are selected.

Privacy and security are built into the system, as well. Hosts and users cannot view someone else's VM, and no one has access to see what any other VM is doing. Users can rent VMs with peace of mind knowing that their virtual environment is private and secure.

## Getting Started: Creating a VM

Ready to spin up your first VM? It's as easy as selecting the right template!

1.  Make sure you have an SSH key in your account (guide [here](https://vast.ai/docs/instance-setup/ssh)).
2.  Select the **Ubuntu 22.04 VM template** in the interface or [click here](https://cloud.vast.ai/?ref_id=108031&template_id=e2f271ce754aeef772181fbf9c82a354) to load it.
3.  Adjust the disk storage needed and then rent a GPU!

Note: SSH keys cannot be edited on running VMs, and SSH access is currently the only method preconfigured on VM instances. Also, when you select the correct template, it will automatically filter for machines that support VMs and will launch a VM when you rent with that template.

For more detailed information about VM template customization, see our Docs page [here](https://vast.ai/docs/instance-setup/virtual-machines).

## Power Up with VM Rental on Vast.ai

With our new virtual machine rental feature, we're opening up a world of possibilities for both hosts and renters. Whether you need the flexibility of GPU remote desktops for complex workflows or you're looking to maximize your earning potential as a host, Vast.ai has you covered. We're committed to giving you the tools to succeed – easily, affordably, and securely.

Ready to see what you can accomplish? Experience the [power of VM rental](https://vast.ai/docs/instance-setup/virtual-machines) with Vast.ai today!


At Vast.ai, we're always looking for ways to innovate and improve our platform, and today we're excited to announce our latest feature: virtual machine (VM) rental. 

Announcing Virtual Machine Rental on Vast.ai


# Google Colab vs. Vast: Which Platform Is Best for Your Projects?

If you’ve ever dabbled in machine learning, data science, or coding in general, chances are you’ve heard of Google Colaboratory – better known as **Google Colab**. But what exactly is it, what are its limitations, and how can you make the best use of it?

Let's take a closer look at these questions and more…

## What Is Google Colab?

In a nutshell, **Google Colab** is a free, browser-based platform that lets you run and write code in a Jupyter notebook environment without having to set up anything locally on your machine. It's widely used for Python-based projects, especially in machine learning and data analysis.

Colab offers an easy starting point for experiments, since you can use the Jupyter notebook interface to write and execute code, visualize results, and keep everything neatly organized in one place. This type of interactive programming allows you to experiment with code in real time, making it ideal for data science and research projects that require immediate feedback and iteration.

### Some other benefits include:

- **Accessibility** – The browser-based platform allows you to access your projects from anywhere.
- **Integration with Google Drive** – Save your notebooks and datasets directly to your Google Drive.
- **Collaboration** – Easily share your notebooks with others for real-time collaboration across teams.
- **Wealth of Resources** – Plenty of tutorials, sample notebooks, and community support are available in the Colab ecosystem, making it easier for you to learn and troubleshoot along the way.

These features make Google Colab a convenient tool for a wide range of users. However, it does have some significant limitations that may prompt you to look for other solutions – such as running a Jupyter instance right here on **Vast** and uploading your notebook directly.

## Why Choose Vast Over Google Colab?

While Google Colab is helpful for smaller-scale projects or initial experimentation, **Vast's** cloud GPU platform offers a much more flexible, powerful, and reliable experience through our Jupyter integration. Here’s why you should consider using Vast instead.

### No Resource Limits

With Google Colab, free-tier users face dynamic usage limits with idle timeout periods and resource caps that sometimes fluctuate. The paid plans have their own set of restrictions as well. For example, notebooks can run for up to 12 hours on a free plan, and the highest-tier paid plan supports continuous code execution for at most 24 hours.

But with **Vast**, there are no imposed time limits, so your projects can run uninterrupted for as long as you need. Whether your project takes hours or days, you won't be cut off or forced to restart.

> **Note**: Google Colab does include the option to pay for a dedicated virtual machine (VM) to get around usage limits, but Vast's pricing is extremely competitive as we are the market leader in low-cost cloud GPU rental.

### Customizable GPU Power for Every Project

Google Colab offers different VMs based on the plan you choose. The free tier only allows access to VMs with a standard system memory profile, restricting the compute power you can use. Paid versions of Colab provide access to machines with a higher system memory profile, but you still don't get to select specific GPUs unless you opt to manually pay for a dedicated machine.

**Vast** gives you access right from the start to powerful and customizable hardware options that are also highly affordable. You can select the exact GPU setup you need, whether you're running small tasks or large-scale machine learning models. For high-end performance, Vast supports multi-GPU configurations, making it easy to scale up your projects without hitting resource bottlenecks.

### Simplicity and Reliability

**Vast** simplifies the entire process by running your Jupyter notebook directly in the cloud with no need for SSH or complex setups. Jupyter by itself is much more reliable than Google Colab and doesn't require setting up SSH keys – you can always open a terminal right inside the browser.

And while Google Colab does facilitate sharing and collaboration, **Vast** offers the same, with the added benefit of running on a more stable platform without timeouts or interruptions. Team members can collaborate in real time, sharing notebooks and results without worrying about session limits.

## Making the Switch to Vast

If you've been using Google Colab for simple notebooks and want to step up your workflow, switching to Vast is straightforward. Simply download your notebook from Colab as a `.ipynb` file, run a Vast Jupyter instance with the recommended [PyTorch template](https://cloud.vast.ai/?ref_id=43484&template_id=f5540ef1a1398b8499546edb53dae704), and upload your notebook into Jupyter directly.

For a step-by-step guide on running Jupyter notebooks on Vast, check out our detailed doc [here](https://vast.ai/docs/use-cases/google-colab).

While Google Colab is a useful starting point for basic tasks, it often falls short when you're dealing with more complex workflows. **Vast** provides a more robust and customizable solution through its Jupyter notebook integration, with greater control, higher performance, and fewer interruptions.

With **Vast**, you'll enjoy the freedom to push your projects even further. **Explore the possibilities today!**


Google Colab is a free, browser-based platform that lets you run and write code in a Jupyter notebook environment without having to set up anything locally on your machine.

Google Colab Explained: Simplifying Your Workflow with Cloud Tools


# Serving vLLM Embeddings on Vast.ai

## Background:

[vLLM](https://github.com/vllm-project/vllm) is a developer-friendly framework for serving large language models. It is very fast and focused on high throughput serving multiple simultaneous calls for production use cases. `vLLM` is now more flexible than ever as it also supports embedding models. This brings vLLM's dynamic batching and Paged Attention to embedding models for much faster throughput, all from the docker image that developers are used to.

This guide will show you how to setup vLLM to serve embedding models on Vast.

## Setup and Querying

First, we setup our environment and vast api key

```bash
pip install --upgrade vastai
```

Once you create your account, you can go [here](https://cloud.vast.ai/cli/) to find your API Key.

```bash
vastai set api-key <Your-API-Key-Here>
```

To serve an embedding model, we'll want a machine with a static IP address + ports to forward. We only will need a single GPU, which can have a small amount of RAM since embedding models are small. We'll prefer a modern GPU so we will add a filter for a compute cap of more than 800.

```bash
vastai search offers 'compute_cap >= 800 gpu_ram >= 24 num_gpus = 1 static_ip=true direct_port_count > 1 cuda_vers >= 12.4'
```

## Deploying the Image:

The easiest way to deploy this instance is to use the command line. Copy and Paste a specific instance id you choose from the list above into `instance-id` below.

### Hosting a Single Embedding Model:

For now, we'll host just one embedding model.
The easiest way to deploy a single model on this instance is to use the command line. Copy and Paste a specific instance id you choose from the list above into `instance-id` below.

We particularly need `v2` so that we use the correct version of the api, `--port 8000` so it serves on the correct model, and `--model-id intfloat/e5-mistral-7b-instruct` to serve the correct model.

```bash
vastai create instance <instance-id> --image vllm/vllm-openai:latest --env '-p 8000:8000' --disk 40 --args --model intfloat/e5-mistral-7b-instruct
```

## Connecting and Testing:

To connect to your instance, we'll first need to get the IP address and port number. Once your instance is done setting up, you should see something like this:
![Instance_view](/uploads/serve_vllm_embeddings_instance_view.png)

Click on the highlighted button to see the IP address and correct port for our requests.

![IP_address_view](/uploads/serve_vllm_embeddings_ip_address_view.png)

## Connecting with the python requests library

```
import requests

headers = {
    'Content-Type': 'application/json',
}

json_data = {
    'model': 'intfloat/e5-mistral-7b-instruct',
    "input": ["San francisco is a"],
}

response = requests.post('http://<Instance-IP-Address>:<Port>/v1/embeddings', headers=headers, json=json_data)
print(response.content)
```

## Connecting with the OpenAI SDK

The OpenAI SDK also allows for generating embeddings. We can use it as well with the following code.

```bash
pip install openai
```

We will copy over the IP address and the port into the cell below.

```python

from openai import OpenAI

# Modify OpenAI's API key and API base to use Infinity's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://<Instance-IP-Address>:<Port>/v1"
client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)
model = "intfloat/e5-mistral-7b-instruct"
embeddings = client.embeddings.create(model=model, input="San francisco is a").data[0].embedding
print("Embeddings:")
print(embeddings)
```

Now we can integrate our embedding server into apps that use the OpenAI SDK.

## Next Steps:

Embeddings are an important part of GenAI applications along with running inference of Generative Models. Now with vLLM, you can run them with the same docker image on Vast for ultimate flexibility.


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