Key Takeaways:
- Decentralised compute networks use blockchain technology to distribute computing tasks across multiple nodes, enhancing scalability and enabling parallel processing.
- These networks provide cost efficiency by using underutilised resources globally, offer resistance to censorship, and capitalise on untapped global computing power.
- Since 2023, the market for decentralised compute networks has seen substantial growth, driven by an increased interest in AI and significant advancements within the sector.
- Render Network and Akash Network are prominent in the decentralised computing space, specialising in Graphics Processing Unit (GPU) services and contributing to the infrastructure of decentralised networks.
- The technology is not only pivotal for decentralised machine learning (ML) training but also for innovative applications like ZKML and coprocessors, indicating a bright future for AI development as it increasingly moves towards decentralisation.
- You can buy decentralised compute tokens on Crypto.com, including RNDR and AKT.
What Is Decentralised Compute?
Decentralised compute networks utilise blockchain technology to provide computing services in a distributed and secure manner. Rather than relying on a central server or data center, this network distributes computing tasks across multiple nodes, enabling parallel processing and enhancing scalability.
Decentralised compute networks bring several key advantages for those developing artificial intelligence (AI) solutions, such as:
Cost Efficiency: Although more machines may be required, these networks capitalise on inexpensive and idle resources worldwide, offering significant cost reductions by accessing underutilised computing and storage capacities.
Resilience Against Censorship: One critical benefit of decentralised compute networks is their resistance to censorship, which serves as a countermeasure to the growing concentration of AI development within a handful of large tech companies.
Resource Utilisation: These networks aim to harness the untapped computing power available globally, fostering the creation of open-source models through computational incentivisation.
The market for decentralised compute networks has witnessed remarkable growth since 2023, driven by a burgeoning interest in AI and significant advancements within the AI sector. This surge in activity is evident in the dramatic increase in market capitalisation of decentralised compute network tokens, which have soared by 507% in the past year.
During this period, Render Network achieved the highest market cap, exceeding US$4.19 billion. Meanwhile, Akash Network has experienced the most substantial growth, escalating from $487 million in February 2023 to $1.3 billion today — an increase of 1,217%.
How Do Decentralised Compute Tokens Work?
There are three emerging categories within decentralised compute networks today, mainly decentralised compute, decentralised machine learning (ML) training, as well as zero-knowledge machine learning (zkML) and coprocessors.
Decentralised Compute
Decentralised compute networks harness unused computational resources like Graphics Processing Units (GPUs) and establish open computational marketplaces. These networks are primarily focused on developing and offering infrastructure that liberates a vast supply of GPUs and computational power, allowing individuals globally to become resource providers.
Render Network and Akash Network stand out as two of the leading decentralised computing solutions currently available. They set themselves apart by exclusively offering GPU services, which are powered by a distributed network of GPUs.
Decentralised ML Training
In addition to the broader computing market, certain networks are particularly dedicated to the training aspect of machine learning (ML), which is a specialised application within the broader field of AI. It encompasses the development of computer programmes capable of making autonomous decisions. ML specifically involves programmes that learn to make these decisions based on the data they process, functioning independently.
Within this framework, ‘models’ refer to the programmes that execute these decisions, and ‘training’ is the process through which these models acquire knowledge. Bittensor and Gensyn are two notable projects that specifically focus on ML training.
ZKML
A combination of ML and AI techniques with Zero-Knowledge (ZK) proofs, zkML enables the verification of complex ML models and algorithms without exposing their details or the underlying training data.
Today, zkML solutions are focused on verifying inference rather than the training phase (primarily due to the computational complexity of verifying training in-circuit), bringing ZK proofs to the inference part of the ML model. Various types of ZK systems have been developed, though in most cases, the ZK in zkML usually refers to ZK-SNARKS.
What Are the Most Popular Decentralised Compute Tokens?
1. Render Token (RNDR)
Launched in 2020, Render Network provides one of the largest distributed GPU networks globally, with over 100,000 node operators (GPU rendering service providers) on its waitlist. The network facilitates advanced AI and 3D rendering capabilities, thanks to its decentralised GPU offering invaluable compute resources.
For creators (customers) who require additional computation power for rendering their scenes, the Render Network (RNDR) connects them to node operators, who receive RNDR tokens in exchange for their GPU power. An ERC-20 token, RNDR is pivotal to the network’s operations, required by both creators and node operators.
2. Golem Network (GLM)
Golem Network (GLM) is a decentralised compute marketplace, establishing a peer-to-peer (P2P) network where application owners and users can rent resources of other providers’ machines. Part of its vision is to create a global supercomputer that will offer purchasable computing power through a globally distributed network of computers. To demonstrate Golem Network’s capabilities, researchers were able to rent out 20,000 CPU cores to simulate 11 billion-plus chemical reactions.
Golem Network is amplifying its efforts to expand its ecosystem and develop a decentralised physical infrastructure for open-source developers and AI companies, mainly through its GPU Beta Testing Programme. The GLM token plays a vital role in the network, as it is the main payment method and store of value on the network. It is also used in the reward system to incentivise providers.
3. Akash Network (AKT)
Akash Network (AKT) is an open-source decentralised cloud computing platform providing decentralised processing and storage to users seeking alternatives for Google and Amazon Web Services (AWS). It features Akash ML, a supercloud for AI designed to provide decentralised and open access to the most sought-after commodity for machine learning GPUs.
In December 2023, Akash Network unveiled Akash Chat, its own version of a ChatGPT alternative that runs on an open-source version of the ChatGPT model (Mistral-7B open-source large language model). AKT is Akash Network’s multi-utility token used for governance, security, incentives, and value exchange within the network.
4. NEAR Protocol (NEAR)
NEAR Protocol was created by the NEAR Collective as a community-run cloud computing platform that can achieve up to 100,000 transactions per second (tps). It also features human-readable addresses and a more straightforward onboarding process for decentralised application (dapp) developers.
The native token, NEAR, serves as the core medium of exchange within the platform’s ecosystem and is mainly used by validators for staking; NEAR is distributed as rewards to these validators in exchange for serving the network.
5. Internet Computer (ICP)
Internet Computer (ICP) is a public blockchain network that combines the power of individual computers into a seamless universe. It is designed to host smart contracts built on an advanced decentralised protocol, known as Internet Computer Protocol (ICP). Its native token, ICP, is a utility token with multiple functions.
As a governance token, ICP can be staked to exercise those governance rights. As a utility token, it can be burned to obtain ‘cycles‘; this serves as gas for computation and storage in canister smart contracts. In addition, ICP tokens can also be minted to reward ‘node machine‘ providers for providing compute and storage, plus used to participate in decentralisation swaps.
How to Buy Decentralised Compute Tokens
Crypto.com offers a user-friendly platform to buy decentralised compute tokens, including Render Network, ICP, and Golem.
Follow these steps to buy decentralised compute tokens, including Render Network and Akash Network, on Crypto.com:
- Download the Crypto.com App and create an account.
- Complete the necessary verification process to unlock full access to the platform.
- Deposit funds into the Crypto.com account; users can utilise fiat currency or other cryptocurrencies to fund their accounts.
- Navigate to the ‘Buy’ section of the App and search for the token (e.g., RNDR).
- Specify the amount of tokens to acquire and review the transaction details.
- Confirm the transaction and wait for the order to be executed.
- Monitor the token holdings in the Crypto.com App, which doubles as a wallet.
Crypto.com provides a secure and reliable trading environment, ensuring that users can trade decentralised compute tokens with confidence. Take advantage of the platform’s intuitive interface, advanced trading features, and competitive fees to make the most of the decentralised compute token trading experience.
Conclusion
Decentralised compute networks offer a promising solution to overcome the challenges of scaling, resourcing, and cost management in the AI sector. By leveraging distributed networks, these systems enable developers to access additional GPUs, find more cost-effective options, and utilise untapped computational resources. This empowers them to build and expand their own AI infrastructures and deployments.
As AI development increasingly shifts towards decentralisation and moves on-chain, we can anticipate the emergence of new industry verticals and major players in the market.
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