TheDinarian
News • Business • Investing & Finance
Serverless Training Cloud Service on FEDML Nexus AI with Seamless Experimental Tracking
👉 A Theta Network Partner
March 20, 2024
post photo preview

We are excited to introduce our “Training as a Cloud Service” at FEDML Nexus AI platform. It provides a variety of GPU types (A100, H100, A6000, RTX4090, etc.) for developers to train your model at any time in a serverless manner. Developers only pay per usage. It includes the following features:

  • Cost-effective training: Developers do not need to rent or purchase GPUs, developers can initiate serverless training tasks at any time, and developers only need to pay according to the usage time;
  • Flexible Resource Management: Developers can also create a cluster to use fixed machines and support the cluster autostop function (such as automatic shutdown after 30 minutes) to help you save the cost loss caused by forgetting to shut down the idle resources.
  • Simplified Code Setup: You do not need to modify your python training source code, you only need to specify the path of the code, environment installation script, and the main entrance through the YAML file
  • Comprehensive Tracking: The training process includes rich experimental tracking functions, including Run Overview, Metrics, Logs, Hardware Monitoring, Model, Artifacts, and other tracking capabilities. You can use the API provided by FEDML Python Library for experimental tracking, such as fedml.log
  • GPU Availability: There are many GPU types to choose from. You can go to Secure Cloud or Community Cloud to view the type and set it in the YAML file to use it.

We will introduce how simple it is as follows:

  • Zero-code Serverless LLM Training on FEDML Nexus AI
  • Training More GenAI Models with FEDML Launch and Pre-built Job Store
  • Experiment Tracking for Large-scale Distributed Training
  • Train on Your Own GPU cluster

Platform: https://fedml.ai
GitHub: https://github.com/FedML-AI

Zero-code Serverless LLM Training on FEDML Nexus AI

As an example of applying FEDML Launch for training service, LLM Fine-tune is the feature of FEDML Studio that is responsible for serverless model training. It is a no-code LLM training platform. Developers can directly specify open-source models for fine-tuning or model Pre-training.

Step 1. Select a model to build a new run

There are two choices for specifying the model to train:
1) Select Default base model from Open Source LLMs

2) Specifying HuggingFace LLM model path

Step 2. Prepare training data

There are three ways to prepare the training data. 

1) Select the default data experience platform 

3) Customized training data can be uploaded through the storage module

3) Data upload API: fedml.api.storage

fedml storage upload '/path/Prompts_for_Voice_cloning_and_TTS'Uploading Package to Remote Storage: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 42.0M/42.0M [00:36<00:00, 1.15MB/s]Data uploaded successfully. | url: https://03aa47c68e20656e11ca9e0765c6bc1f.r2.cloudflarestorage.com/fedml/3631/Prompts_for_Voice_cloning_and_TTS.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=52d6cf37c034a6f4ae68d577a6c0cd61%2F20240307%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240307T202738Z&X-Amz-Expires=604800&X-Amz-SignedHeaders=host&X-Amz-Signature=bccabd11df98004490672222390b2793327f733813ac2d4fac4d263d50516947

Step 3. Hyperparameter Setting (Optional)

Step 4. Select GPU Resource Type (Optional)

The GPU resource type can be found through the Compute - Secure Cloud page

Step 5. Initiate Training and Track Experimental Results

Training More GenAI Models with FEDML Launch and Pre-built Job Store

Besides the zero-code training job experience, we also provide FEMDL Launch to launch any training job on FEDML Nexus AI. For more details, please read another blog: https://blog.fedml.ai/fedml-launch/. Here, we mainly introduce how to run pre-built jobs on FEDML Nexus AI platform.

Taking a pre-built job for GaLore (https://github.com/jiaweizzhao/galore), an efficient traininmethg od for LLM on RTX4090, as an example. A day after it released source code on GitHub, FEDML Team incorporated GaLore training as part of our Job Store. Now developers can launch and customize on top of the example GaLore jobs and enjoy freedom from Out-of-Memory fear.

The instructions to launch GaLore pre-built job are as follows:

  1. On FedML official website (https://fedml.ai/home), you can head to Launch Job Store > Train, and look for Memory-Efficient LLM Training with GaLore job. The Description tab shows some basic usage for the code, referencing the original GaLore project's README. In the Source Code and Configuration tab, you can examine a more detailed layout and setup of the architecture.

  1. Hit the Launch button on the top right, users will be prompted to enter the configuration for the job. Under the Select Job section, click Add, and add “resource_type: RTX-4090”  in the job_yaml > computing section to specify using RTX 4090 for training. Please check the resource type list ​at https://fedml.ai/compute/secure (check the value of Resource Type in each GPU item), or directly visit https://fedml.ai/launch/accelerator_resource_type?_t=1710889566178.
  2. Once done filling out the hyperparameters, you should be able to launch a full-scale GaLore + Checkpointing Activation pre-training for the LLaMA 7B model with a batch size of 16. Then you can find your experimental tracking results at https://fedml.ai/train/my-runs (see more details on the Section "Experiment Tracking for Large-scale Distributed Training")

Experiment Tracking for Large-scale Distributed Training

Running remote tasks often requires a transparent monitoring environment to facilitate troubleshooting and real-time analysis of machine learning experiments. This section guides through the monitoring capabilities of a launched job.

Run Overview

Log into to the FEDML Nexus AI Platform (https://fedml.ai) and go to Train > Runs. And select the run you just launched and click on it to view the details of the run.

Metrics

FedML offers a convenient set of APIs for logging metrics. The execution code can utilize these APIs to log metrics during its operation.

fedml.log()

log dictionary of metric data to the FEDML Nexus AI Platform.

Usage

fedml.log(metrics: dict,step: int = None,customized_step_key: str = None,commit: bool = True) -> None

Arguments

  • metrics (dict): A dictionary object for metrics, e.g., {"accuracy": 0.3, "loss": 2.0}.
  • step (int=None): Set the index for current metric. If this value is None, then step will be the current global step counter.
  • customized_step_key (str=None): Specify the customized step key, which must be one of the keys in the metrics dictionary.
  • commit (bool=True): If commit is False, the metrics dictionary will be saved to memory and won't be committed until commit is True.

Example:

fedml.log({"ACC": 0.1})fedml.log({"acc": 0.11})fedml.log({"acc": 0.2})fedml.log({"acc": 0.3})fedml.log({"acc": 0.31}, step=1)fedml.log({"acc": 0.32, "x_index": 2}, step=2, customized_step_key="x_index")fedml.log({"loss": 0.33}, customized_step_key="x_index", commit=False)fedml.log({"acc": 0.34}, step=4, customized_step_key="x_index", commit=True)

Metrics logged using fedml.log() can be viewed under Runs > Run Detail > Metrics on FEDML Nexus AI Platform.

Logs

You can query the realtime status of your run on your local terminal with the following command.

fedml run logs -rid <run_id>

Additionally, logs of the run also appear in realtime on the FEDML Nexus AI Platform under the Runs > Run Detail > Logs

Hardware Monitoring

The FEDML library automatically captures hardware metrics for each run, eliminating the need for user code or configuration. These metrics are categorized into two main groups:

  • Machine Metrics: This encompasses various metrics concerning the machine's overall performance and usage, encompassing CPU usage, memory consumption, disk I/O, and network activity.
  • GPU Metrics: In environments equipped with GPUs, FEDML seamlessly records metrics related to GPU utilization, memory usage, temperature, and power consumption. This data aids in fine-tuning machine learning tasks for optimized GPU-accelerated performance.

Model Checkpoint:

FEDML additionally provides an API for logging models, allowing users to upload model artifacts.

fedml.log_model()

Log model to the FEDML Nexus AI Platform (fedml.ai).

fedml.log_model(model_name,model_file_path,version=None) -> None

Arguments

  • model_name (str): model name.
  • model_file_path (str): The file path of model name.
  • version (str=None): The version of FEDML Nexus AI Platform, options: dev, test, release. Default is release (fedml.ai).

Examples

fedml.log_model("cv-model", "./cv-model.bin")

Models logged using fedml.log_model() can be viewed under Runs > Run Detail > Model on FEDML Nexus AI Platform

Artifacts:

Artifacts, as managed by FEDML, encapsulate information about items or data generated during task execution, such as files, logs, or models. This feature streamlines the process of uploading any form of data to the FEDML Nexus AI Platform, facilitating efficient management and sharing of job outputs. FEDML facilitates the uploading of artifacts to the FEDML Nexus AI Platform through the following artifact api:

fedml.log_artifact()

log artifacts to the FEDML Nexus AI Platform (fedml.ai), such as file, log, model, etc.

fedml.log_artifact(artifact: Artifact,version=None,run_id=None,edge_id=None) -> None

Arguments

  • artifact (Artifact): An artifact object represents the item to be logged, which could be a file, log, model, or similar.
  • version (str=None): The version of FEDML Nexus AI Platform, options: dev, test, release. Default is release (fedml.ai).
  • run_id (str=None): Run id for the artifact object. Default is None, which will be filled automatically.
  • edge_id (str=None): Edge id for current device. Default is None, which will be filled automatically.

Artifacts logged using fedml.log_artifact() can be viewed under Runs > Run Detail > Artifacts on FEDML Nexus AI Platform.

Train on Your Own GPU cluster

You can also build your own cluster and launch jobs there. The GPU nodes in the cluster can be GPU instances launched under your AWS/GCP/Azure account or your in-house GPU devices. The workflow is as follows.

Step 1. Bind the machines on the Platform

Log into the platform, head to the Compute / My Servers Page and copy the fedml login command:

Step 2. SSH into your on-prem devices and do the following individually for each device:

Install the fedml library if not installed already:

pip install fedml

Run the login command copied from the platform:

fedml login 3b24dd2f****206e8669

It should show something similar as below:

(fedml) alay@a6000:~$ fedml login 3b24dd2f9b3e478084c517bc206e8669 -v devWelcome to FedML.ai!Start to login the current device to the MLOps (https://fedml.ai)...(fedml) alay@a6000:~$ Found existing installation: fedml 0.8.7Uninstalling fedml-0.8.7:Successfully uninstalled fedml-0.8.7Looking in indexes: https://test.pypi.org/simple/, https://pypi.org/simpleCollecting fedml==0.8.8a156Obtaining dependency information for fedml==0.8.8a156 from https://test-files.pythonhosted.org/packages/e8/44/06b4773fe095760c8dd4933c2f75ee7ea9594938038fb8293afa22028906/fedml-0.8.8a156-py2.py3-none-any.whl.metadata Downloading https://test-files.pythonhosted.org/packages/e8/44/06b4773fe095760c8dd4933c2f75ee7ea9594938038fb8293afa22028906/fedml-0.8.8a156-py2.py3-none-any.whl.metadata (4.8 kB)Requirement already satisfied: numpy>=1.21 in ./.pyenv/versions/fedml/lib/python3.10/site-packages (from fedml==0.8.8a156....Congratulations, your device is connected to the FedML MLOps platform successfully!Your FedML Edge ID is 201610, unique device ID is [email protected]

Head back to the Compute / My Servers page on platform and verify that the devices are bounded to the FEDML Nexus AI Platform:

Step 3. Create a cluster of your servers bounded to the FEDML Nexus AI Platform:

Navigate to the Compute / Create Clusters page and create a cluster of your servers:

All your created clusters will be listed on the Compute / My Clusters page:

Step 4. Launch the job on your cluster:

The way to create the job YAML file is the same as “Training as a Cloud Service”. All that is left to do to launch a job to the on-premise cluster is to run following one-line command:

fedml launch job.yaml -c <cluster_name>

For our example, the command and respective output would be as follows:

fedml launch job.yaml -c hello-world

About FEDML, Inc.

FEDML is your generative AI platform at scale to enable developers and enterprises to build and commercialize their own generative AI applications easily, scalably, and economically. Its flagship product, FEDML Nexus AI, provides unique features in enterprise AI platforms, model deployment, model serving, AI agent APIs, launching training/Inference jobs on serverless/decentralized GPU cloud, experimental tracking for distributed training, federated learning, security, and privacy.

Link

community logo
Join the TheDinarian Community
To read more articles like this, sign up and join my community today
0
What else you may like…
Videos
Podcasts
Posts
Articles
"The World Order That We Are Coming Into"

If XRP is the neutral bridge for all sovereign currencies, stablecoins, and tokenized assets, then it’s not just facilitating payments, it’s capturing all that value at every level. From smart contracts to tokenized treasuries and digitized assets, XRP forms the foundation and backbone for everything in between.

With cross-border payments representing a multi-trillion-dollar corridor, that’s where the largest capital will flow and the greatest returns will come from.

At this point, you’re the gatekeeper to the digital economy. Everything else follows or fades away once regulations take effect.

You either see it or you won’t until it’s too late.

~The Black Swan Capitalist

00:01:50
Denelle Dixon (Stellar CEO) On Bloomburg 🚀

'Everyone, including Mastercard and Visa, is looking at how this technology can make finance easier for their consumers and their business. I don't think there is going to be a loser, but I do think there will be shake-ups. And ultimately, the consumer is going to win.' - SDF CEO @DenelleDixon on @BloombergTV

00:05:29
We are minutes away from passing the GENIUS Act.
00:01:19
👉 Coinbase just launched an AI agent for Crypto Trading

Custom AI assistants that print money in your sleep? 🔜

The future of Crypto x AI is about to go crazy.

👉 Here’s what you need to know:

💠 'Based Agent' enables creation of custom AI agents
💠 Users set up personalized agents in < 3 minutes
💠 Equipped w/ crypto wallet and on-chain functions
💠 Capable of completing trades, swaps, and staking
💠 Integrates with Coinbase’s SDK, OpenAI, & Replit

👉 What this means for the future of Crypto:

1. Open Access: Democratized access to advanced trading
2. Automated Txns: Complex trades + streamlined on-chain activity
3. AI Dominance: Est ~80% of crypto 👉txns done by AI agents by 2025

🚨 I personally wouldn't bet against Brian Armstrong and Jesse Pollak.

👉 Coinbase just launched an AI agent for Crypto Trading
📚 How to Liquid Stake XPRT and Add Liquidity to stkXPRT/XPRT Pool on Persistence DEX 📚

Dinarian Note: The tutorial shows you how to turn your XPRT into Liquid staked stkXPRT, which can then on top of being staked earn you extra yield via the pools on the Persistence DEX. Note: I put a list of the current pools available below. Check out the APR% on these 😉 This is what makes Defi so attractive to investors. Putting your money to work 101. Instead of just staking your XPRT for 16%, you can put it in a pool and make upwards of 50% or more. Note: These values constantly fluctuate. Even if you don't want to partake in this, it's good practice and extremely good to know! This will be invaluable once your a multi-millionaire, unless you plan on keeping your funds in a criminal run BANK! 🤣

⚠ If you reside in the USA, you MUST use a VPN. I set it to Singapore and it works just fine! ~ Namasté 🙏 Crypto Michael ⚡ The Dinarian


This tutorial will guide you through the process of adding liquidity to the stkXPRT/XPRT pool on Persistence DEX.

Table of Contents:

🔹 How to ...

Account Abstraction on Sonic 🚀

With EIP-7702 arriving in Sonic's next client upgrade, your regular wallet will gain smart contract powers. ✨

🎁 Gas Subsidies
📊 Dynamic Fees
🤝 Social Recovery
🔑Session Keys

🧵 https://x.com/SonicLabs/status/1936395173275238459

👀 11-Year-Old Happy Demonstrates Advanced Dormant Abilities – Seeing Without the Physical Eyes

Happy an 11 year old demonstrated in week 3 workshop activating dormant abilities that go far beyond ordinary vision. What he demonstrates here is not imagination or guesswork it’s real perception beyond the physical eyes.

By tapping into his soul awareness and expanding his consciousness, Happy is able to see what others can’t, showing us the potential that lives within all of us when the inner senses are awakened.

👉 This is more than a skill. It’s a reminder of who we truly are & the abilities he have with practice.

Thank You Happy, You have inspired me and many others
Darius J Wright.

post photo preview
Dubai regulator VARA classifies RWA issuance as licensed activity
Virtual Asset Regulatory Authority (VARA) leads global regulatory framework - makes RWA issuance licensed activity in Dubai.

Real-world assets (RWAs) issuance is now licensed activity in Dubai.

~ Actual law.
~ Not a legal gray zone.
~ Not a whitepaper fantasy.

RWA issuance and listing on secondary markets is defined under binding crypto regulation.

It’s execution by Dubai.

Irina Heaver explained:

“RWA issuance is no longer theoretical. It’s now a regulatory reality.”

VARA defined:

- RWAs are classified as Asset-Referenced Virtual Assets (ARVAs)

- Secondary market trading is permitted under VARA license

- Issuers need capital, audits, and legal disclosures

- Regulated broker-dealers and exchanges can now onboard and trade them

This closes the gap that killed STOs in 2018.

No more tokenization without venues.
No more assets without liquidity.

UAE is doing what Switzerland, Singapore, and Europe still haven’t:

Creating enforceable frameworks for RWA tokenization that actually work.

Matthew White, CEO of VARA, said it perfectly:

“Tokenization will redefine global finance in 2025.”

He’s not exaggerating.

$500B+ market predicted next year.

And the UAE just gave it legal rails.

~Real estate.
~Private credit.
~Shariah-compliant products.

Everything is in play.

This is how you turn hype into infrastructure.

What Dubai is doing now is 3 years ahead of everyone else.

Founders, investors, ecosystem builders:

You want to build real-world assets onchain.

Don’t waste another year waiting for clarity.

Come to Dubai.

It’s already here.

 

Source

🙏 Donations Accepted 🙏

If you find value in my content, consider showing your support via:

💳 PayPal: 
1) Simply scan the QR code below 📲
2) or visit https://www.paypal.me/thedinarian

🔗 Crypto – Support via Coinbase Wallet to: [email protected]

Or Buy me a coffee: https://buymeacoffee.com/thedinarian

Your generosity keeps this mission alive, for all! Namasté 🙏 Crypto Michael ⚡  The Dinarian

 

Read full Article
post photo preview
🎬Proof the Deep State Planned This War for Years🎬
Nation First outlines how the Israeli attack on Iran was planned by the Deep State and the Military Industrial Complex over 15 years ago.

Prepare to have your mind blown

~Namasté 🙏 Crypto Michael ⚡ The Dinarian

Dear friend,

What just happened in Iran wasn’t a surprise attack. It wasn’t a last-minute decision. It wasn’t even Israel acting alone.

It was a war plan written years ago — by men in suits, sitting in think tanks in Washington and New York. And yesterday, that plan was finally put into action.

Here’s the truth they don’t want you to know: this war was cooked up long before Trump ever became President — and it was designed to happen exactly this way.

Let’s start with what just happened.

Israel launched a massive, unexpected strike on Iran. They hit nuclear facilities. They killed military generals. They struck deep inside Iranian territory — and now the whole region is on edge, ready to explode into full-blown war.

The media is acting shocked. But I’m not. You shouldn’t be either.

Why?

Because we have the documents. They told us this was coming. Years ago.

Exhibit A: The Brookings Institution.

The Brooking Institution is a fancy name for what’s basically a war-planning factory dressed up as a research centre. Back in 2009, Brookings published a report called Which Path to Persia?

It laid out exactly how to get the U.S. into a war with Iran — without looking like the bad guy.

Here’s the sickest part:

“The United States would encourage — and perhaps even assist — the Israelis in conducting the strikes… in the expectation that both international criticism and Iranian retaliation would be deflected away from the United States and onto Israel.”

Let that sink in.

They literally suggested using Israel to start the war, so America could stand back and say, “Wasn’t us!”

They even titled a chapter of this report: “Leave It to Bibi” — naming Netanyahu as the guy to light the match.

Exhibit B: The Council on Foreign Relations (CFR).

The Council on Foreign Relations is an another Deep State operation. Also in 2009, CFR published a “contingency memothat laid out the whole military plan for an Israeli strike on Iran — step by step.

  • What routes the jets would fly (over Jordan and Iraq).

  • What bombs they’d use (the biggest bunker-busters in the U.S. arsenal).

  • Which Iranian sites to hit (Natanz, Arak, Esfahan).

  • And how Iran might respond (missiles, drones, threats to U.S. bases).

It’s like they had a time machine. Because those exact strikes just happened following the routes, likely using the bombs and hitting the sites that the CFR outlined.

Exhibit C: The Plot to Attack Iran by Dan Kovalik.

This one really blows the lid off.

US human rights lawyer and journalist Dan Kovalik, in his book The Plot to Attack Iran: How the CIA and the Deep State Have Conspired to Vilify Iran, shows how the CIA and Israel’s Mossad have been working together for decades — not just watching Iran, but actively sabotaging it. Killing scientists. Running cyberattacks. Feeding lies to the media to make Iran look like it’s always “six months away” from building a nuke.

He even reveals how they discussed false flag attacks — faking an Iranian strike to justify going to war. That’s not a conspiracy theory. That’s documented strategy.

And here’s where President Trump comes in.

Unlike the warmongers who wrote these plans, Trump wasn’t looking to bomb Iran. He wanted to talk. Negotiate. Make a deal — like he did with North Korea.

In fact, peace talks with Iran were just days away.

But someone didn’t want peace. Someone wanted war.

So Israel went in — just like the Brookings script said — and lit the fuse.

Trump didn’t authorise it. He didn’t want it. But they gazumped him. They went around him. And now, the peace he was trying to build has been blown to bits.

This was never about Iran being a threat. It was about keeping the war machine fed.

Think tanks, defence contractors, foreign lobbies — they don’t profit from peace. They thrive on tension. On fear. On war.

And now, thanks to them, the world’s one step closer to the edge.

If you’ve never trusted the mainstream media, you’re right not to.

If you’ve ever suspected there’s a shadowy agenda behind every war, you’re not paranoid.

You’re paying attention.

Because the documents are real. The war was planned. And the bombs are falling — right on schedule.

Pray for Iran’s civilians.

Pray for the Israelis caught in the crossfire.

Pray for a President who still wants peace.

And pray that we wake up before it’s too late.

Because the war has started.

But the truth has just begun to spread.

Until next time, God bless you, your family and nation.

Take care,

George Christensen

Source:

George Christensen is a former Australian politician, a Christian, freedom lover, conservative, blogger, podcaster, journalist and theologian. He has been feted by the Epoch Times as a “champion of human rights” and his writings have been praised by Infowars’ Alex Jones as “excellent and informative”.

George believes Nation First will be an essential part of the ongoing fight for freedom:

The time is now for every proud patriot to step to the fore and fight for our freedom, sovereignty and way of life. Information is a key tool in any battle and the Nation First newsletter will be a valuable tool in the battle for the future of the West.

— George Christensen.

Find more about George at his www.georgechristensen.com.au website.

🙏 Donations Accepted 🙏

If you find value in my content, consider showing your support via:

💳 PayPal: 
1) Simply scan the QR code below 📲
2) or visit https://www.paypal.me/thedinarian

🔗 Crypto – Support via Coinbase Wallet to: [email protected]

Or Buy me a coffee: https://buymeacoffee.com/thedinarian

Your generosity keeps this mission alive, for all! Namasté 🙏 The Dinarian

 

Read full Article
post photo preview
The Possible Impact Of USDC On The XRP Ledger And RLUSD
Key Points
  • It seems likely that USDC on the XRP Ledger (XRPL) boosts liquidity, benefiting XRP, though some see it as competition for RLUSD.
  • Research suggests both stablecoins can coexist, enhancing the XRPL ecosystem.
  • The evidence leans toward increased network activity being good for XRP, despite potential competition.

The recent launch of USDC on the XRP Ledger has sparked discussions about its impact on the ecosystem, particularly in relation to RLUSD, Ripple's own stablecoin. This response explores whether this development is more about competition for RLUSD or if it enhances liquidity on the XRPL, ultimately benefiting XRP.
 

Impact on Liquidity and XRP

The introduction of USDC, a major stablecoin with a $61 billion market cap, likely increases liquidity on the XRPL by attracting more users, developers, and institutions. This boost can enhance DeFi applications and enterprise payments, potentially driving demand for XRP, the native token used for transaction fees. While some may view it as competition for RLUSD, the overall effect seems positive for the XRPL's growth.
 

Competition vs. Coexistence with RLUSD

USDC and RLUSD cater to different needs: USDC appeals to those valuing regulatory compliance, while RLUSD, backed by Ripple, may attract users preferring ecosystem integration. Research suggests both can coexist, increasing options and fostering innovation, rather than purely competing.
 

Detailed Analysis of USDC on XRPL and Its Implications

The integration of USDC on the XRP Ledger (XRPL), announced on June 12, 2025, by Circle, has significant implications for the ecosystem, particularly in relation to RLUSD, Ripple's stablecoin launched in 2024. This section provides a comprehensive analysis, exploring whether this development is more about competition for RLUSD or if it enhances liquidity on the XRPL, ultimately benefiting XRP.
 

Understanding RLUSD and Its Role

RLUSD, Ripple's stablecoin, received approval from the New York Department of Financial Services (NYDFS) in 2024 and is designed to be fully backed by cash and cash equivalents, ensuring stability. It is available on both the Ethereum and XRP Ledger blockchains, aiming to enhance liquidity, reduce volatility, and serve cross-border payments. With a current market cap of $413 million, RLUSD is smaller than USDC's $61 billion but has regulatory credibility, particularly appealing to institutions.
 

Impact of USDC on the XRPL

The launch of USDC on the XRPL is a significant development, given its status as the second-largest stablecoin by market cap.
 
Key impacts include:
  • Liquidity Boost: USDC's integration can attract more users, developers, and institutions, increasing overall liquidity. This is crucial for DeFi applications, as Circle's announcement emphasizes its use in liquidity provisioning for token pairs and FX flows.
  • Increased Utility: USDC enhances the XRPL's utility for enterprise payments, financial infrastructure, and DeFi, potentially making it more attractive for global money movement and transparent settlements.
  • Regulatory and Institutional Appeal: As a regulated stablecoin issued by Circle, USDC can bring institutional users to the XRPL, aligning with Ripple's goals for regulated financial activities.
  • Network Growth: Supporting a widely recognized stablecoin like USDC on 22 blockchains, including the XRPL, increases the network's visibility and adoption, potentially driving more activity.

Competition vs. Complementarity with RLUSD

While USDC's launch could be seen as competition for RLUSD, the evidence suggests a more nuanced relationship:
  • Competition: Both are stablecoins on the XRPL, and USDC's larger market presence ($61 billion vs. RLUSD's $413 million) might attract users and developers away from RLUSD. However, competition can drive innovation, such as lower fees or better services, benefiting the ecosystem
  • Complementarity: Different stablecoins cater to different needs. USDC appeals to users valuing regulatory compliance and widespread adoption across multiple blockchains, while RLUSD, backed by Ripple, may attract those preferring ecosystem integration and regulatory approval from NYDFS. The XRPL can benefit from having multiple options, increasing liquidity and fostering a diverse ecosystem.
  • Coexistence Benefits: Research suggests that having multiple stablecoins enhances liquidity and provides users with more choices, potentially leading to higher network activity. For example, institutions might use USDC for global payments and RLUSD for specific XRPL-integrated applications, creating a symbiotic relationships.

Impact on XRP

The introduction of USDC, alongside RLUSD, is likely beneficial for XRP, the native token of the XRPL, for several reasons:
  • Increased Liquidity and Activity: Higher liquidity on the XRPL, driven by both stablecoins, can increase transaction volumes. XRP is used for transaction fees, with some fees burned, potentially reducing supply over time and increasing demand.
  • DeFi and Enterprise Use Cases: Both USDC and RLUSD enhance DeFi and enterprise applications, such as liquidity pools and cross-border payments, which can drive demand for XRP as a settlement token.
  • Network Growth: A more liquid and active XRPL is more attractive to developers and users, potentially leading to long-term growth for XRP, as increased utility can drive its value.
Expert analyses, such as those from u.today and ledgerinsights.com, suggest the launch is a "massive boost" for liquidity and adoption, with RLUSD also playing a significant role.
 

Comparative Analysis: USDC vs. RLUSD

To further illustrate, consider the following table comparing key attributes:
 
Given the evidence, it is more accurate to view the introduction of USDC on the XRPL as beneficial for liquidity, which is ultimately good for XRP, rather than solely as competition for RLUSD. The XRPL benefits from increased options, with both stablecoins enhancing liquidity, utility, and network growth. While some competition exists, the overall impact is positive, fostering a robust ecosystem that can drive demand for XRP. This conclusion aligns with expert analyses and community discussions, acknowledging the complexity of the stablecoin market within the XRPL.
 

🙏 Donations Accepted 🙏

If you find value in my content, consider showing your support via:

💳 PayPal: 
1) Simply scan the QR code below 📲
2) or visit https://www.paypal.me/thedinarian

🔗 Crypto – Support via Coinbase Wallet to: [email protected]

Or Buy me a coffee: https://buymeacoffee.com/thedinarian

Your generosity keeps this mission alive, for all! Namasté 🙏 The Dinarian

Read full Article
See More
Available on mobile and TV devices
google store google store app store app store
google store google store app tv store app tv store amazon store amazon store roku store roku store
Powered by Locals