Data was the currency of the initial tech boom, but compute will be the currency of the now-brewing AI boom.

Compute refers to the processing power and hardware resources required to perform computational tasks. These resources come from hardware like CPUs, GPUs, TPUs, and cloud computing.

The problem with the compute market in its current state is that price dictates usage levels. Unfortunately, due to a shortage of high-end compute hardware, they’re expensive.

Higher prices hamper accessibility, which ultimately hampers innovation in key sectors like AI.

The way forward is to increase accessibility to compute, which will, in turn, lead to the creation of innovative applications and new use cases.

Well, the compute market may have a savior on the horizon: Prime Intellect.

Current state of compute markets

The ultimate issue with compute markets is that they lack the efficiency seen in other exchanges. But if we were to boil it down, it comes to this:

1. Fragmented pricing

There are a few players in the GPU provider business. The big ones are Nvidia, AMD, and Groq. According to estimates, the cost for high-end GPUs often varies between $10,000-$50,000. The fluctuations widen with lesser-known providers.

2. Lack of scalability

For more advanced computations, specifically for AI products, there is often demand for large GPU clusters. Unfortunately, due to how fragmented the market is, it is difficult to have large GPU clusters readily available for use.

3. Underutilization

Estimates suggest that 70% of GPUs globally sit idle most of the time, while the utilization rate of GPUs in data centers is around 25%- 50%. So, as a commodity, it is heavily underutilized, presenting massive inefficiency, especially when you take into account the level of demand.

4. No standardized market

There is no standardized market mechanism in place for compute. There is no spot market or futures market that makes it easy for people to trade compute power.

5. Reliability of multi-node clusters

A multi-node cluster is essentially multiple interconnected computers/processors that work together to perform computational tasks.

Multi-node clusters are very commonly used to train AI models. Unfortunately, recent surveys with AI companies have shown that they are very unreliable. This is because the difference in providers significantly hampers performance.

Prime Intellect has come to market with its compute exchange to solve all of these problems.

What is Prime Intellect?

Prime Intellect is building the infrastructure to make compute more accessible by aggregating compute resources under one roof, the Prime Intellect compute exchange (i.e. marketplace), making it easy to trade compute and train state-of-the-art AI models.

Think of it like the Airbnb for compute.

Hotels can be expensive and in short supply, while there are plenty of underutilized houses. Airbnb created a marketplace to make this underutilized and fragmented resource more accessible, thereby benefiting guests and hosts alike.

Similarly, compute is fragmented, expensive, and underutilized, but by creating a distributed marketplace where GPU providers can sell their compute and AI developers can access compute on-demand, it solves the accessibility issue at scale while benefiting all parties involved.

The ultimate vision with Prime Intellect is a succinct four-step plan:

1. Aggregate global compute

As stated earlier, there are a lot of GPU providers and unused compute resources in the world. The issue is that the market is so fragmented that it’s difficult to effectively access them.

Under Prime Intellect’s marketplace, there is the ‘unified resource pool’ where all compute resources from centralized and decentralized GPU providers will be aggregated under one frictionless market to increase accessibility for cost-effective compute.

Taking it one step further, the team constantly emphasizes its aim to have large GPU clusters (up to 512+) readily available for use. If successful, this will be an absolute game-changer for teams that are building and training complex AI models.

2. Develop distributed training frameworks

Ultimately, providing access to compute is simply not enough because making compute more accessible has a larger purpose: propelling innovation in compute-intensive fields like AI.

The problem with distributed AI training is that for the open-source developers to effectively compete with the big tech giants, there are a lot of hurdles they need to overcome.

More specifically:

• Breakaway from the shackles of centralized infrastructure providers
• Find an effective way to communicate and train across a globally distributed set of GPUs

There are a bunch of different distributed training methods that have been developed over the years, but Prime Intellect has opted for an adapted version of distributed low communication (DiLoCo) training.

The crux is that it is a technique that allows for training models of devices that are poorly connected by synchronizing updates in intervals. If you would like the detailed technical breakdown, I recommend checking out this article by Prime Intellect.

Prime Intellects' version of DiLoCo has a specific emphasis on making it efficient and cost-effective to get on-demand access to large GPU clusters to build complex AI models. By incorporating different optimizations, they ensure that network latency is minimized and the orchestration across multiple clusters is seamless.

The result is that AI developers and researchers worldwide now have a decentralized infrastructure alternative to collaborate and develop complex models in a distributed setting to compete with the big tech giants.

Think of it as a community sharing solar power. Rather than having one central station that everyone relies on, each house in the community has its own solar panels that share electricity with each other in a distributed setting.

3. Train and contribute to collaboratively owned AI models

The successful implementation of the first two culminates in this, a distributed network of collectively owned AI models supported by Prime Intellect’s infrastructure.

Anybody can apply to contribute their resources to train AI models, and at the same time, any researchers/developers can take part in training the different AI models.

The initial focus from the Prime Intellect team was to nail down models for the following categories:

• Large language models (LLMs)
• Coding agents
• Scientific foundation models focused on longevity drug discovery and material sciences.

Over the past 6 months or so, Prime Intellect has made massive strides in making this a reality, with three different models already operating:

1. INTELLECT-1: The first decentralised training of a 10B parameter model.
2. INTELLECT-2: A 32B reinforcement learning model targeting advanced reasoning in coding, math, and science.
3. INTELLECT-MATH: A 7B parameter model for math reasoning.
4. SYNTHETIC-1: A model using synthetic math datasets for math reasoning tasks.

4. The Prime Intellect Protocol

The Prime Intellect Protocol is the final piece of the master plan that ties everything together.

It is a peer-to-peer compute and intelligence protocol that commoditizes compute and intelligence using crypto-economic primitives to aggregate global compute and training decentralized AI models.

So, what does the Prime Intellect Protocol allow for?

- A compute and intelligence marketplace at a global scale that allows anyone to provide or access compute in a peer-to-peer manner.

- Allows anyone to come to the protocol and create/contribute to open models, agents, datasets, and more.

- The financialization of AI models making it economically viable to produce complex open-source AI models.

- Implements robust verification mechanisms.

- Scalable and self-upgradeable to the point where millions of agents can perform billions of interactions per second.

- Removing the middleman to create sovereign open-source AI models.

There are many important infrastructural prerequisites to the Prime Intellect Protocol that have already been released. These include:

- The Compute Exchange: The platform for aggregating and orchestrating distributed global compute.

- PRIME: The decentralized training framework that makes it easy to train large-scale models in a distributed setting.

- GENESYS: A synthetic reasoning framework that’s easily parallelizable with data centers and other compute hardware devices.

- TOPLOC: A lightweight and efficient validation scheme designed to verify the legitimacy of compute contributions.

When you tie all of the elements together, you have the perfect decentralized AI solution. Compute aggregation for distributed model training supported by crypto-economic primitives.

Concluding thoughts

Given the revolutionary nature of AI technology, decentralized AI is going to be one of the most important battles to win.

If decentralized AI is going to win, I’d be willing to bet good money that Prime Intellect will be at the frontline of this battle, leading the sector to victory.

The team is filled with chads who previously worked at major AI companies like OpenAI and Google DeepMind, mixed with some crypto natives to achieve the perfect balance. They recently announced a raise of $15 million from some of the best AI minds in the world, like Andrej Karpathy, Emad Mostaque, and Dylan Patel, among many others.

The project is ambitious, but the team has constantly been shipping and hitting all their targets.

The race for AI supremacy is a war, and it’s projects like Prime Intellect that are fighting the good fight to ensure that open-source AI prevails over closed-source tech giant AI.

So, take a break from getting rugged by KOLs and cabals in the trenches and familiarize yourself with Prime Intellect. This is going to be a big one.