DATS x ParallelAI
At $PAI, we are always on the lookout for projects whose values and missions align with ours, and we’re happy announce a partnership with @DATSProjectOfficial 🤝
About the DATS Project
DATS is the first cybersecurity DePIN project with a focus on Web3 security, allowing users to allocate system resources without any additional hardware investments via their desktop app.
The DATS desktop app allows individuals to monetize their idle computing power, in the first cybersecurity share-to-earn based high power computing marketplace.
The DATS team has over 20 years of experience in cybersecurity, with a history of solving many hack cases.
About this partnership
As the DATS Project provides an avenue for users to contribute their internet upload speed and CPUs, with this partnership we aim to broaden our capabilities by catering to a wider range of use cases and developers. We foresee a demand for non-GPU compute in the future, and we look forward to building with DATS.
✅ https://x.com/parallelaix/status/1849110761194263024?s=46&t=T7jBG_AvrN2ghmmSWDH1Ew
At $PAI, we are always on the lookout for projects whose values and missions align with ours, and we’re happy announce a partnership with @DATSProjectOfficial 🤝
About the DATS Project
DATS is the first cybersecurity DePIN project with a focus on Web3 security, allowing users to allocate system resources without any additional hardware investments via their desktop app.
The DATS desktop app allows individuals to monetize their idle computing power, in the first cybersecurity share-to-earn based high power computing marketplace.
The DATS team has over 20 years of experience in cybersecurity, with a history of solving many hack cases.
About this partnership
As the DATS Project provides an avenue for users to contribute their internet upload speed and CPUs, with this partnership we aim to broaden our capabilities by catering to a wider range of use cases and developers. We foresee a demand for non-GPU compute in the future, and we look forward to building with DATS.
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Discover the Advantages of ParallelAI and NetMind's New Partnership
We’re delighted to announce a new partnership with @NetmindAi 🤝
We'll be integrating NetMind's decentralized GPU capacity into the $PAI GPU middleware layer, giving our clients more options for executing their parallel code on top-tier GPUs like A100 and RTX 4090, which boast usage rates of 98% and 99%.
About NetMind 🧠
NetMind Power is a decentralized platform aimed at democratizing AI computing power. They've rapidly expanded to over 2,000 GPUs, with 4,800 more queued, demonstrating their unmatched scalability and demand for high-end, computation-intensive AI workloads.
About ParallelAI 💻
ParallelAI is unleashing the power of parallel processing to slash computation times for AI developers by up to 20x. Our automatic parallelization tools make it faster and more efficient for AI developers to run complex tasks on GPUs and CPUs.
About the Partnership 🤝
When ParallelAI's AI developer clients use our Optimized Parallel Code Writer to automatically transform sequential code into parallel code, they can then execute the code through our dedicated decentralized GPU middleware layer. @NetmindAi will be supporting this ecosystem by providing access to their highly-utilized decentralized GPUs. This integration perfectly combines ParallelAI’s automated parallelization tech with NetMind’s cutting-edge compute power.
More To Come 🤫
We look forward to sharing more details on this new partnership soon, so stay tuned for more updates
✅ https://x.com/parallelaix/status/1849445245110583735?s=46&t=T7jBG_AvrN2ghmmSWDH1Ew
We’re delighted to announce a new partnership with @NetmindAi 🤝
We'll be integrating NetMind's decentralized GPU capacity into the $PAI GPU middleware layer, giving our clients more options for executing their parallel code on top-tier GPUs like A100 and RTX 4090, which boast usage rates of 98% and 99%.
About NetMind 🧠
NetMind Power is a decentralized platform aimed at democratizing AI computing power. They've rapidly expanded to over 2,000 GPUs, with 4,800 more queued, demonstrating their unmatched scalability and demand for high-end, computation-intensive AI workloads.
About ParallelAI 💻
ParallelAI is unleashing the power of parallel processing to slash computation times for AI developers by up to 20x. Our automatic parallelization tools make it faster and more efficient for AI developers to run complex tasks on GPUs and CPUs.
About the Partnership 🤝
When ParallelAI's AI developer clients use our Optimized Parallel Code Writer to automatically transform sequential code into parallel code, they can then execute the code through our dedicated decentralized GPU middleware layer. @NetmindAi will be supporting this ecosystem by providing access to their highly-utilized decentralized GPUs. This integration perfectly combines ParallelAI’s automated parallelization tech with NetMind’s cutting-edge compute power.
More To Come 🤫
We look forward to sharing more details on this new partnership soon, so stay tuned for more updates
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AILayer x $PAI
We’re thrilled to announce our partnership with @AILayer_xyz, the first Bitcoin Layer 2 project aimed at supporting the mass adoption of AI applications.
About AILayer:
AILayer is a Bitcoin Layer 2 solution designed with a focus on AI-driven modular construction. Its goal is to advance the deep integration of Web3 and AI.
In addition to its success in technology leadership and ecosystem building, AILayer is the third Web3 project to form a strategic partnership with a sovereign state, following Binance and Tether.
Stay tuned for more updates on this partnership!
✅ https://x.com/ParallelAIx/status/1849802125351772476
We’re thrilled to announce our partnership with @AILayer_xyz, the first Bitcoin Layer 2 project aimed at supporting the mass adoption of AI applications.
About AILayer:
AILayer is a Bitcoin Layer 2 solution designed with a focus on AI-driven modular construction. Its goal is to advance the deep integration of Web3 and AI.
In addition to its success in technology leadership and ecosystem building, AILayer is the third Web3 project to form a strategic partnership with a sovereign state, following Binance and Tether.
Stay tuned for more updates on this partnership!
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BLOCX. x $PAI
We are proud to announce a partnership with @BLOCX_TECH, a visionary safety layer of DePIN aiming to lead the future of digital computing.
About BLOCX.
BLOCX. is an all-in-one computer manager and marketplace, which includes comprehensive tools such as GPU & CPU rentals and lending, AI/ML templates, and many more.
About the Partnership
With BLOCX., developers will be able to leverage their resources in order to facilitate parallel code execution. Parallel AI will function as an aggregator for the BLOCX. marketplace, enabling the automatic rental of idle GPUs across various platforms.
We look forward to sharing more updates regarding this partnership, keep an eye out for ParallelAI integration at BLOCX.!
✅ https://x.com/ParallelAIx/status/1850187185598730467
We are proud to announce a partnership with @BLOCX_TECH, a visionary safety layer of DePIN aiming to lead the future of digital computing.
About BLOCX.
BLOCX. is an all-in-one computer manager and marketplace, which includes comprehensive tools such as GPU & CPU rentals and lending, AI/ML templates, and many more.
About the Partnership
With BLOCX., developers will be able to leverage their resources in order to facilitate parallel code execution. Parallel AI will function as an aggregator for the BLOCX. marketplace, enabling the automatic rental of idle GPUs across various platforms.
We look forward to sharing more updates regarding this partnership, keep an eye out for ParallelAI integration at BLOCX.!
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DEVELOPMENT UPDATE: Parilix Benchmark Testing
Introduction: Parilix Performance in Focus
Last week, we shared the first of our Parilix benchmark tests, comparing the execution time of the Mandelbrot set algorithm in sequential C++ vs Parilix for different set sizes. In that report, we demonstrated that for a set size of n = 12, Parilix achieved a 16x faster runtime when executed on equivalent hardware, Nvidia A100 GPUs. Our Parilix development team continues to expand its libraries by incorporating foundational algorithms relevant to AI training and data analysis. This allows us to showcase the significant performance benefits that developers of all backgrounds can gain by adopting the ParallelAI platform, as opposed to manually optimizing code themselves.
New Benchmark: BubbleSort Performance
Today, we’re excited to share our second benchmark report, this time focusing on the Bubble Sort algorithm, a classic in data analysis. While Bubble Sort is not frequently used in AI/LLM training or large-scale data analysis due to its inefficiency with large datasets, it serves as a valuable reference point for evaluating tech stack and hardware performance. Algorithms like QuickSort and MergeSort typically outperform Bubble Sort, and we look forward to releasing benchmark results with these very soon, but using BubbleSort as a benchmark helps demonstrate the raw computational power of Parilix.
The Benefits of Parallel Processing in BubbleSort
BubbleSort benefits from parallel processing by enabling multiple comparisons to be handled simultaneously. In traditional development, efficiently mapping these tasks to GPU threads can be labor-intensive, requiring careful attention to ensure proper execution. However, with Parilix, this process is handled seamlessly within the code itself.
In our tests, when compared to sequential C++, Parilix delivered a remarkable 156x reduction in runtime for an input size of 100,000. This result highlights the massive impact parallel processing can have on algorithm performance, especially when the process is streamlined by Parilix. The ability to provide such utility and power to developers, as ParallelAI does, sets our platform apart in fields like AI, code optimization, GPU computing, and DePIN. For a detailed look at the results across various input sizes, please refer to the table.
Access to Parilix: Get Started Today
We are excited to announce that our Parilix GitHub repository is now open for access. To gain access, simply visit our website https://www.parallelai.tech/, register with your email address, and you'll be granted entry shortly. Additionally, you'll be added to a technical support group with our development team, where you can ask questions or seek help. Over the next few days, we plan to upload all benchmarking source code to a dedicated repository so users can replicate the results and experience Parilix's performance firsthand.
Expanding Parilix with the Developer Community
This promising start has been driven by our dedicated internal team, but we are excited to soon fully onboard the wider developer community. By doing so, we aim to expand the Parilix libraries and support the development of more complex programs and algorithms. These efforts will directly contribute to training PACT and optimizing Parilix for a broad range of programming applications across multiple use cases.
Looking Ahead: PACT and the Power of ParallelAI
For those with less technical experience but who are eager to experience the power of ParallelAI, we are approaching the beta release of PACT. This release will offer seamless code generation and optimization through a single AI, providing an accessible entry point for users looking to harness Parilix's capabilities.
Stay tuned for further updates on this exciting development as we expect to release footage of our first demo very soon.
https://x.com/ParallelAIx/status/1850890111929491905
Introduction: Parilix Performance in Focus
Last week, we shared the first of our Parilix benchmark tests, comparing the execution time of the Mandelbrot set algorithm in sequential C++ vs Parilix for different set sizes. In that report, we demonstrated that for a set size of n = 12, Parilix achieved a 16x faster runtime when executed on equivalent hardware, Nvidia A100 GPUs. Our Parilix development team continues to expand its libraries by incorporating foundational algorithms relevant to AI training and data analysis. This allows us to showcase the significant performance benefits that developers of all backgrounds can gain by adopting the ParallelAI platform, as opposed to manually optimizing code themselves.
New Benchmark: BubbleSort Performance
Today, we’re excited to share our second benchmark report, this time focusing on the Bubble Sort algorithm, a classic in data analysis. While Bubble Sort is not frequently used in AI/LLM training or large-scale data analysis due to its inefficiency with large datasets, it serves as a valuable reference point for evaluating tech stack and hardware performance. Algorithms like QuickSort and MergeSort typically outperform Bubble Sort, and we look forward to releasing benchmark results with these very soon, but using BubbleSort as a benchmark helps demonstrate the raw computational power of Parilix.
The Benefits of Parallel Processing in BubbleSort
BubbleSort benefits from parallel processing by enabling multiple comparisons to be handled simultaneously. In traditional development, efficiently mapping these tasks to GPU threads can be labor-intensive, requiring careful attention to ensure proper execution. However, with Parilix, this process is handled seamlessly within the code itself.
In our tests, when compared to sequential C++, Parilix delivered a remarkable 156x reduction in runtime for an input size of 100,000. This result highlights the massive impact parallel processing can have on algorithm performance, especially when the process is streamlined by Parilix. The ability to provide such utility and power to developers, as ParallelAI does, sets our platform apart in fields like AI, code optimization, GPU computing, and DePIN. For a detailed look at the results across various input sizes, please refer to the table.
Access to Parilix: Get Started Today
We are excited to announce that our Parilix GitHub repository is now open for access. To gain access, simply visit our website https://www.parallelai.tech/, register with your email address, and you'll be granted entry shortly. Additionally, you'll be added to a technical support group with our development team, where you can ask questions or seek help. Over the next few days, we plan to upload all benchmarking source code to a dedicated repository so users can replicate the results and experience Parilix's performance firsthand.
Expanding Parilix with the Developer Community
This promising start has been driven by our dedicated internal team, but we are excited to soon fully onboard the wider developer community. By doing so, we aim to expand the Parilix libraries and support the development of more complex programs and algorithms. These efforts will directly contribute to training PACT and optimizing Parilix for a broad range of programming applications across multiple use cases.
Looking Ahead: PACT and the Power of ParallelAI
For those with less technical experience but who are eager to experience the power of ParallelAI, we are approaching the beta release of PACT. This release will offer seamless code generation and optimization through a single AI, providing an accessible entry point for users looking to harness Parilix's capabilities.
Stay tuned for further updates on this exciting development as we expect to release footage of our first demo very soon.
https://x.com/ParallelAIx/status/1850890111929491905
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DEVELOPMENT UPDATE: Parilix GitHub & PACT Training
Parilix GitHub Repository: Key Additions
Since the launch of Parilix, we’ve made significant progress in setting up our GitHub repository to provide the resources users need to install, experiment, and build with Parilix. Just over a week ago, we launched the main Parilix repository, which includes:
- Overview: An introduction to Parilix and its core capabilities
- Installation: Detailed instructions for setting up Parilix
- Usage Instructions: Guidance on using Parilix effectively
- Basic Examples: Sample code to help users get started
- Additional Resources: Extra materials for further learning and support
Expanding the Parilix Repository with Benchmark Examples
We’re excited to announce that we’re now adding a dedicated Examples Repository. This new repository will enable the community and developers to experience Parilix’s power directly, offering them the ability to replicate our benchmarks and compare results for themselves.
In recent weeks, we’ve shared benchmarking reports showing Parilix’s performance on foundational algorithms against equivalent sequential code written in traditional languages. To make these comparisons accessible, we’re continuously expanding the Parilix libraries and building more reference models for users. Over the past week, we’ve accelerated these efforts, adding source code for additional algorithms, including:
- Mandlebrot Set
- BubbleSort
- QuickSort
- Matrix Multiplication
This additional code is available alongside the sequential examples in our new repository. Now, users with the appropriate hardware can run these benchmarks themselves and directly experience the benefits of parallel code optimization with Parilix.
Easy Access: Links to GitHub on the ParallelAI Website
All ParallelAI GitHub repositories will soon be accessible through the ParallelAI website. Keep an eye out for these links and start exploring Parilix to see its potential firsthand.
Training PACT with the Expanded Codebase
As we expand the Parilix language libraries, our PACT development team has been working tirelessly to train our AI agent on the new codebase. We’re thrilled to report that PACT is now very close to a stable v1 release. The AI is delivering consistent, accurate outputs for basic algorithm code conversion, which brings us closer to an exciting milestone.
Looking Ahead: Upcoming Demonstration
In the coming days, we’ll release a demonstration video showcasing PACT’s potential to convert and optimize code seamlessly. This video will give users a preview of the ease of code conversion they can expect when using PACT.
Stay Tuned
We look forward to keeping our community updated as Parilix and PACT continue to evolve. Thank you for your ongoing support—stay tuned for more updates soon!
✅ https://twitter.com/ParallelAIx/status/1852105937001288014
Parilix GitHub Repository: Key Additions
Since the launch of Parilix, we’ve made significant progress in setting up our GitHub repository to provide the resources users need to install, experiment, and build with Parilix. Just over a week ago, we launched the main Parilix repository, which includes:
- Overview: An introduction to Parilix and its core capabilities
- Installation: Detailed instructions for setting up Parilix
- Usage Instructions: Guidance on using Parilix effectively
- Basic Examples: Sample code to help users get started
- Additional Resources: Extra materials for further learning and support
Expanding the Parilix Repository with Benchmark Examples
We’re excited to announce that we’re now adding a dedicated Examples Repository. This new repository will enable the community and developers to experience Parilix’s power directly, offering them the ability to replicate our benchmarks and compare results for themselves.
In recent weeks, we’ve shared benchmarking reports showing Parilix’s performance on foundational algorithms against equivalent sequential code written in traditional languages. To make these comparisons accessible, we’re continuously expanding the Parilix libraries and building more reference models for users. Over the past week, we’ve accelerated these efforts, adding source code for additional algorithms, including:
- Mandlebrot Set
- BubbleSort
- QuickSort
- Matrix Multiplication
This additional code is available alongside the sequential examples in our new repository. Now, users with the appropriate hardware can run these benchmarks themselves and directly experience the benefits of parallel code optimization with Parilix.
Easy Access: Links to GitHub on the ParallelAI Website
All ParallelAI GitHub repositories will soon be accessible through the ParallelAI website. Keep an eye out for these links and start exploring Parilix to see its potential firsthand.
Training PACT with the Expanded Codebase
As we expand the Parilix language libraries, our PACT development team has been working tirelessly to train our AI agent on the new codebase. We’re thrilled to report that PACT is now very close to a stable v1 release. The AI is delivering consistent, accurate outputs for basic algorithm code conversion, which brings us closer to an exciting milestone.
Looking Ahead: Upcoming Demonstration
In the coming days, we’ll release a demonstration video showcasing PACT’s potential to convert and optimize code seamlessly. This video will give users a preview of the ease of code conversion they can expect when using PACT.
Stay Tuned
We look forward to keeping our community updated as Parilix and PACT continue to evolve. Thank you for your ongoing support—stay tuned for more updates soon!
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DEVELOPMENT UPDATE:
First Video Demonstration of PACT
Introducing Our First PACT Demonstration:
We’re kicking off the week with an exciting milestone: the release of our first video demonstration of the Parilix Automated Code Transformer (PACT) in action. This video showcases PACT’s real-time ability to convert a Quick Sort algorithm from sequential C++ into Parilix, followed by a benchmark test to highlight the substantial performance gains from utilizing full parallelization across all cores.
Bringing Accessible Optimization to All:
This demonstration reveals the transformative power of PACT and Parilix. By automating complex optimizations, PACT eliminates the need for developers to write low-level GPU code or handle direct GPU thread management. This opens a world of possibilities, making advanced code optimization accessible to developers and builders of all experience levels. Through PACT, users can unlock powerful parallelization without the overhead or cost of specialized development.
Preparing for the Beta Launch:
In the coming weeks, our primary focus will be on preparing for PACT’s Beta launch, where users will have the opportunity to experience its capabilities firsthand. During this period, we’ll be continually expanding the Parilix libraries and training PACT on more complex algorithms, enhancing its ability to accurately interpret and convert a broader range of code.
Note on the Demonstration Interface:
This initial demonstration showcases PACT’s functionality via the terminal, and does not reflect PACT’s final user interface. We look forward to unveiling the full UI in an upcoming preview, which will provide a more intuitive and user-friendly experience.
Stay Tuned:
Thank you for your continued support as we bring PACT closer to release. Stay tuned for more updates, and enjoy exploring the power of PACT and Parilix!
✅ https://x.com/ParallelAIx/status/1853484964643275248
First Video Demonstration of PACT
Introducing Our First PACT Demonstration:
We’re kicking off the week with an exciting milestone: the release of our first video demonstration of the Parilix Automated Code Transformer (PACT) in action. This video showcases PACT’s real-time ability to convert a Quick Sort algorithm from sequential C++ into Parilix, followed by a benchmark test to highlight the substantial performance gains from utilizing full parallelization across all cores.
Bringing Accessible Optimization to All:
This demonstration reveals the transformative power of PACT and Parilix. By automating complex optimizations, PACT eliminates the need for developers to write low-level GPU code or handle direct GPU thread management. This opens a world of possibilities, making advanced code optimization accessible to developers and builders of all experience levels. Through PACT, users can unlock powerful parallelization without the overhead or cost of specialized development.
Preparing for the Beta Launch:
In the coming weeks, our primary focus will be on preparing for PACT’s Beta launch, where users will have the opportunity to experience its capabilities firsthand. During this period, we’ll be continually expanding the Parilix libraries and training PACT on more complex algorithms, enhancing its ability to accurately interpret and convert a broader range of code.
Note on the Demonstration Interface:
This initial demonstration showcases PACT’s functionality via the terminal, and does not reflect PACT’s final user interface. We look forward to unveiling the full UI in an upcoming preview, which will provide a more intuitive and user-friendly experience.
Stay Tuned:
Thank you for your continued support as we bring PACT closer to release. Stay tuned for more updates, and enjoy exploring the power of PACT and Parilix!
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DEVELOPMENT UPDATE: PACT UI Reveal
Introducing PACT’s First Video Demonstration:
Earlier this week, we showcased the first video demonstration of PACT, our powerful AI-driven code optimization tool. In this demo, we highlighted PACT’s ability to transform sequential code into Parilix-optimized code in seconds, fully prepared to utilize available GPU power. The real-time demonstration revealed impressive optimization benefits, underscoring PACT’s potential to revolutionize code efficiency.
A First Look at the PACT User Interface:
Today, we’re thrilled to reveal the first look at the PACT platform UI, walking you through the end-to-end user journey. The streamlined process allows users to import sequential code, optimize it in seconds, and run it seamlessly across our expanding network of GPU providers.
Empowering Accessible AI for All:
At ParallelAI, our mission is to make high-performance AI tools accessible to everyone while minimizing costs—an achievement that benefits both developers and the broader AI community. By bridging complex optimization with ease of use, PACT and Parilix open new doors for developers of all backgrounds.
Preparing for Beta Release:
In parallel with our ongoing efforts to expand the Parilix code libraries and train PACT, our team is now focused on building robust backend and frontend integrations to ensure a smooth user experience. These integrations are crucial as we prepare for PACT’s beta release in the coming weeks.
Stay Tuned for More Updates:
We’re incredibly excited about what lies ahead and are grateful for your support as we continue refining and expanding PACT. Keep an eye out for further updates as we move closer to the full platform launch!
https://x.com/ParallelAIx/status/1854544057932169287
Introducing PACT’s First Video Demonstration:
Earlier this week, we showcased the first video demonstration of PACT, our powerful AI-driven code optimization tool. In this demo, we highlighted PACT’s ability to transform sequential code into Parilix-optimized code in seconds, fully prepared to utilize available GPU power. The real-time demonstration revealed impressive optimization benefits, underscoring PACT’s potential to revolutionize code efficiency.
A First Look at the PACT User Interface:
Today, we’re thrilled to reveal the first look at the PACT platform UI, walking you through the end-to-end user journey. The streamlined process allows users to import sequential code, optimize it in seconds, and run it seamlessly across our expanding network of GPU providers.
Empowering Accessible AI for All:
At ParallelAI, our mission is to make high-performance AI tools accessible to everyone while minimizing costs—an achievement that benefits both developers and the broader AI community. By bridging complex optimization with ease of use, PACT and Parilix open new doors for developers of all backgrounds.
Preparing for Beta Release:
In parallel with our ongoing efforts to expand the Parilix code libraries and train PACT, our team is now focused on building robust backend and frontend integrations to ensure a smooth user experience. These integrations are crucial as we prepare for PACT’s beta release in the coming weeks.
Stay Tuned for More Updates:
We’re incredibly excited about what lies ahead and are grateful for your support as we continue refining and expanding PACT. Keep an eye out for further updates as we move closer to the full platform launch!
https://x.com/ParallelAIx/status/1854544057932169287
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PAI x CUDOS
We've just confirmed a partnership with CUDOS that has very exciting potential for the $PAI ecosystem.
The partnership give our Parahub GPU Middleware Layer access to CUDOS' latest Ada Lovelace and Ampere options via GPU passthrough for maximum performance.
In the wake of their recent token merger with the ASI Alliance, alongside Fetch.ai, SingularityNET, and Ocean Protocol, CUDOS also plugs ParallelAI into one of the most ambitious and trusted global decentralized AI ecosystems.
We're excited to get going with the integration as we gear up for the launch of our Parilix Parallelized Programming Language and PACT Automated Code Transformer soon.
✅ https://x.com/ParallelAIx/status/1854930165001801787
We've just confirmed a partnership with CUDOS that has very exciting potential for the $PAI ecosystem.
The partnership give our Parahub GPU Middleware Layer access to CUDOS' latest Ada Lovelace and Ampere options via GPU passthrough for maximum performance.
In the wake of their recent token merger with the ASI Alliance, alongside Fetch.ai, SingularityNET, and Ocean Protocol, CUDOS also plugs ParallelAI into one of the most ambitious and trusted global decentralized AI ecosystems.
We're excited to get going with the integration as we gear up for the launch of our Parilix Parallelized Programming Language and PACT Automated Code Transformer soon.
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LAUNCH ANNOUNCEMENT: PACT Beta Release Date Confirmed!
We are thrilled to announce that the Beta version of PACT will officially go live on November 27th, exactly two weeks from today!
The Journey to Beta
Over recent months, our development team has been working tirelessly to update our code repositories and expand the training of our PACT AI agent. By continuously introducing new foundational algorithms written in Parilix, relevant to the world of AI and data analysis, we have created a robust library to train PACT effectively. This effort has paved the way for a seamless code optimization experience like never before.
What to Expect from the Beta Version
The Beta release of PACT will allow users to effortlessly convert code for the foundational algorithms it is trained in, transforming them into Parilix-optimized versions ready to fully utilize available compute power. Initially, PACT will support input code in C++ and Python, and users won’t need to worry about structuring code for GPU threads. Simply provide a sequential program, and PACT will handle the rest, drastically simplifying the process and reducing complexity.
The Beta Period: A Collaborative Effort
The Beta phase will serve as a critical period for further refining and training PACT before its public launch. While the AI agent already demonstrates impressive results, there may be cases where outputs deviate from initial training data. Users will have the opportunity to provide feedback and engage in guided learning, contributing directly to PACT’s ongoing growth and improvement.
Building the PACT Community
We are eager to build a strong PACT developer and user community, and we’re currently defining an incentive program to encourage contributions. This program will reward users who add code to the Parilix libraries and participate in training PACT, helping us enhance its capabilities into a true game-changer in the realms of AI, compute, and programming.
We look forward to sharing more details as we approach the Beta release. Stay tuned for updates, and get ready to experience the future of code optimization with PACT!
✅ https://x.com/parallelaix/status/1856792254251245943?s=46&t=T7jBG_AvrN2ghmmSWDH1Ew
We are thrilled to announce that the Beta version of PACT will officially go live on November 27th, exactly two weeks from today!
The Journey to Beta
Over recent months, our development team has been working tirelessly to update our code repositories and expand the training of our PACT AI agent. By continuously introducing new foundational algorithms written in Parilix, relevant to the world of AI and data analysis, we have created a robust library to train PACT effectively. This effort has paved the way for a seamless code optimization experience like never before.
What to Expect from the Beta Version
The Beta release of PACT will allow users to effortlessly convert code for the foundational algorithms it is trained in, transforming them into Parilix-optimized versions ready to fully utilize available compute power. Initially, PACT will support input code in C++ and Python, and users won’t need to worry about structuring code for GPU threads. Simply provide a sequential program, and PACT will handle the rest, drastically simplifying the process and reducing complexity.
The Beta Period: A Collaborative Effort
The Beta phase will serve as a critical period for further refining and training PACT before its public launch. While the AI agent already demonstrates impressive results, there may be cases where outputs deviate from initial training data. Users will have the opportunity to provide feedback and engage in guided learning, contributing directly to PACT’s ongoing growth and improvement.
Building the PACT Community
We are eager to build a strong PACT developer and user community, and we’re currently defining an incentive program to encourage contributions. This program will reward users who add code to the Parilix libraries and participate in training PACT, helping us enhance its capabilities into a true game-changer in the realms of AI, compute, and programming.
We look forward to sharing more details as we approach the Beta release. Stay tuned for updates, and get ready to experience the future of code optimization with PACT!
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Community AMA: PACT and Beyond!
Join us on Thursday, November 21st, for our next AMA as we dive into PACT ahead of its upcoming Beta release, what it means for the AI and developer landscape, and the plan moving forward.
Get your questions ready also as we will be running a TG Q&A also.
✅ https://x.com/parallelaix/status/1857193675639443554?s=46&t=T7jBG_AvrN2ghmmSWDH1Ew
Join us on Thursday, November 21st, for our next AMA as we dive into PACT ahead of its upcoming Beta release, what it means for the AI and developer landscape, and the plan moving forward.
Get your questions ready also as we will be running a TG Q&A also.
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Development Update: Expanding Parilix Libraries
Parilix GitHub Progress
The Parilix GitHub repository has now been open to our community for almost three weeks, attracting builders and developers eager to explore the future of high-level parallelized coding. By leveraging Parilix, users can achieve powerful code optimization with ease. For those who have taken the time to experiment with Parilix’s capabilities, the results have been promising. As we gear up for the launch of PACT, all of your contributions will play a vital role in expanding PACT’s abilities as an automated code transformer.
Building a Strong Foundation: Expanding Parilix Libraries
A key focus for us remains the ongoing expansion of the Parilix libraries, with the goal of developing a robust collection of foundational algorithms. This library forms the core training base for PACT, ensuring that it can handle diverse use cases in AI training, data analysis, and beyond. Since our last update, we’ve added several new examples contributed by both our core team and community developers, which have been used to further train PACT ahead of the Beta release.
New Addition: Breadth-First Traversal (BFS) Algorithm
Most recently, we’ve added the Breadth-First Traversal (BFS) Algorithm to the Parilix libraries.
Why BFS Matters:
Breadth-First Search (BFS) is a fundamental graph traversal algorithm that explores nodes level by level. In AI and data analysis, BFS is significant because it systematically explores all possibilities, making it useful for search problems and state space exploration. For instance, BFS can be used in training graph neural networks (GNNs) or for tasks like pathfinding, where exploring all nodes in a structured manner is crucial. The algorithm’s systematic approach also helps in feature extraction in graph-based models and can assist in parallel processing, aligning well with Parilix’s strengths in high-level parallelization.
Laying the Groundwork for PACT and Beyond
The expansion of Parilix’s libraries and the ongoing training of PACT are setting the stage for ParallelAI to become a leader in high-level compute optimization. This foundational work will enable users to build sophisticated programs, conduct deep data analysis, and train complex AI models faster and more cost-effectively.
Looking Ahead: PACT Beta and Upcoming AMA
As a reminder, the PACT Beta release is scheduled for next week. We’re excited to see how the community will leverage PACT’s capabilities. Additionally, we have an upcoming Community AMA with our CEO, Basith, on Thursday of this week. Stay tuned for the official AMA schedule, which will be shared shortly.
Thank you for your continued support as we push forward with these exciting developments. We look forward to sharing more updates soon!
✅ https://x.com/parallelaix/status/1858632286838624453?s=46&t=T7jBG_AvrN2ghmmSWDH1Ew
Parilix GitHub Progress
The Parilix GitHub repository has now been open to our community for almost three weeks, attracting builders and developers eager to explore the future of high-level parallelized coding. By leveraging Parilix, users can achieve powerful code optimization with ease. For those who have taken the time to experiment with Parilix’s capabilities, the results have been promising. As we gear up for the launch of PACT, all of your contributions will play a vital role in expanding PACT’s abilities as an automated code transformer.
Building a Strong Foundation: Expanding Parilix Libraries
A key focus for us remains the ongoing expansion of the Parilix libraries, with the goal of developing a robust collection of foundational algorithms. This library forms the core training base for PACT, ensuring that it can handle diverse use cases in AI training, data analysis, and beyond. Since our last update, we’ve added several new examples contributed by both our core team and community developers, which have been used to further train PACT ahead of the Beta release.
New Addition: Breadth-First Traversal (BFS) Algorithm
Most recently, we’ve added the Breadth-First Traversal (BFS) Algorithm to the Parilix libraries.
Why BFS Matters:
Breadth-First Search (BFS) is a fundamental graph traversal algorithm that explores nodes level by level. In AI and data analysis, BFS is significant because it systematically explores all possibilities, making it useful for search problems and state space exploration. For instance, BFS can be used in training graph neural networks (GNNs) or for tasks like pathfinding, where exploring all nodes in a structured manner is crucial. The algorithm’s systematic approach also helps in feature extraction in graph-based models and can assist in parallel processing, aligning well with Parilix’s strengths in high-level parallelization.
Laying the Groundwork for PACT and Beyond
The expansion of Parilix’s libraries and the ongoing training of PACT are setting the stage for ParallelAI to become a leader in high-level compute optimization. This foundational work will enable users to build sophisticated programs, conduct deep data analysis, and train complex AI models faster and more cost-effectively.
Looking Ahead: PACT Beta and Upcoming AMA
As a reminder, the PACT Beta release is scheduled for next week. We’re excited to see how the community will leverage PACT’s capabilities. Additionally, we have an upcoming Community AMA with our CEO, Basith, on Thursday of this week. Stay tuned for the official AMA schedule, which will be shared shortly.
Thank you for your continued support as we push forward with these exciting developments. We look forward to sharing more updates soon!
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Ready For The Next Level? Parallel AI and ICP Hub SG Join Forces to Expand AI Model Deployment, Interoperability, and High-Performance Storage
We're delighted to announce that ParallelAI have agreed a partnership with ICP Hub SG, a leading branch of the globally renowned Internet Computer Protocol. This will enable to enable AI developers to leverage ICP’s distributed storage solution when using ParallelAI’s suite of parallel processing tools.
The partnership will allow for models to be deployed into ICP canisters, allowing ParallelAI’s users to benefit from the interoperability the ICP provides, for example smart contract calls on Solana, Ethereum and Bitcoin L1s.
In the future, as ICP expands its proposition to include GPU compute, ParallelAI will take advantage of this by integrating this compute power into ParallelAI’s ParaHub GPU Marketplace Aggregator. This will further diversify the options that AI developers have for executing their parallel code.
This partnership represents an exciting step forward for ParallelAI as we look to provide AI developers with the most interoperable and decentralized parallel processing solution on the market.
✅ https://x.com/parallelaix/status/1858955086648799391?s=46&t=T7jBG_AvrN2ghmmSWDH1Ew
We're delighted to announce that ParallelAI have agreed a partnership with ICP Hub SG, a leading branch of the globally renowned Internet Computer Protocol. This will enable to enable AI developers to leverage ICP’s distributed storage solution when using ParallelAI’s suite of parallel processing tools.
The partnership will allow for models to be deployed into ICP canisters, allowing ParallelAI’s users to benefit from the interoperability the ICP provides, for example smart contract calls on Solana, Ethereum and Bitcoin L1s.
In the future, as ICP expands its proposition to include GPU compute, ParallelAI will take advantage of this by integrating this compute power into ParallelAI’s ParaHub GPU Marketplace Aggregator. This will further diversify the options that AI developers have for executing their parallel code.
This partnership represents an exciting step forward for ParallelAI as we look to provide AI developers with the most interoperable and decentralized parallel processing solution on the market.
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Join Us for Our Community AMA 3!
This Thursday, Nov 21st at 9pm UTC, hosted by @MysticCrypto & featuring our CEO @Basith_AI in which we will discuss:
• Product & Development Updates.
• Insights into how we’re driving adoption.
• Q&A session
Don’t miss it! Schedule here:
https://twitter.com/i/spaces/1OwxWNWbpakJQ
✅ https://x.com/parallelaix/status/1859307774628462840?s=46&t=T7jBG_AvrN2ghmmSWDH1Ew
This Thursday, Nov 21st at 9pm UTC, hosted by @MysticCrypto & featuring our CEO @Basith_AI in which we will discuss:
• Product & Development Updates.
• Insights into how we’re driving adoption.
• Q&A session
Don’t miss it! Schedule here:
https://twitter.com/i/spaces/1OwxWNWbpakJQ
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ParallelAI x PinLink
We’re delighted to announce a new partnership with @PinLinkAI 🤝
We’ll be using PinLink’s decentralized GPU capacity as one of the options for executing the parallel code we create for our AI clients.
About ParallelAI 🖥️
ParallelAI is unleashing the power of parallel processing to slash compute times for AI developers by up to 20x. Our automated parallelization technology makes it faster and more efficient for AI developers to run complex tasks on GPUs and CPUs.
About PinLink📍
PinLink is the first RWA-Tokenized DePIN platform, applying models from Real World Asset trading to the DePIN sector. PinLink’s unique model unlocks capital efficiencies that drive the down costs of physical infrastructure including GPU/s CPUs for AI developers.
About the Partnership 🤝
PinLink will be one of ParallelAI’s decentralized GPU partners, providing some of the compute power required to execute the parallel code we create for our AI clients. This helps us deliver end-to-end decentralization for our parallel processing solution.
More Coming Soon
This partnership brings us one step closer to our vision for a fully decentralized parallel processing solution and making $PAI the universal currency for compute power procurement.
Stay tuned for more exciting partnership announcements coming soon.
✅ https://x.com/parallelaix/status/1860006681477873920?s=46&t=T7jBG_AvrN2ghmmSWDH1Ew
We’re delighted to announce a new partnership with @PinLinkAI 🤝
We’ll be using PinLink’s decentralized GPU capacity as one of the options for executing the parallel code we create for our AI clients.
About ParallelAI 🖥️
ParallelAI is unleashing the power of parallel processing to slash compute times for AI developers by up to 20x. Our automated parallelization technology makes it faster and more efficient for AI developers to run complex tasks on GPUs and CPUs.
About PinLink📍
PinLink is the first RWA-Tokenized DePIN platform, applying models from Real World Asset trading to the DePIN sector. PinLink’s unique model unlocks capital efficiencies that drive the down costs of physical infrastructure including GPU/s CPUs for AI developers.
About the Partnership 🤝
PinLink will be one of ParallelAI’s decentralized GPU partners, providing some of the compute power required to execute the parallel code we create for our AI clients. This helps us deliver end-to-end decentralization for our parallel processing solution.
More Coming Soon
This partnership brings us one step closer to our vision for a fully decentralized parallel processing solution and making $PAI the universal currency for compute power procurement.
Stay tuned for more exciting partnership announcements coming soon.
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The demand for compute power is skyrocketing with AI's rapid growth. ParaHub is here to simplify access, offering an AI-powered GPU marketplace to help you find the best compute at the best price—all with seamless performance, verified privacy, and zero downtime.
Discover more in our latest video.
✅ https://x.com/parallelaix/status/1860325547361853523?s=46&t=T7jBG_AvrN2ghmmSWDH1Ew
Discover more in our latest video.
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Development Update: ParaHub Partnership Integrations
Introducing the ParaHub Vision
At the end of last week, we shared our first dedicated video explaining the value proposition behind ParaHub in simple terms. In essence, ParaHub is the first AI-powered GPU Marketplace Aggregator, designed to help users effortlessly find the best compute options at the lowest possible price in a matter of seconds.
Building Strategic Partnerships
To bring this vision to life, our focus has been on fostering relationships with key partners who will provide the compute infrastructure that ParaHub will rely on. Since its inception, ParallelAI has secured a number of long-standing partnerships. Initially, these partnerships were focused on ensuring access to adequate compute resources. However, in recent months, they have become more nuanced and deliberate, shaping up to significantly enhance ParaHub’s product offering.
Key Features: Confidential Compute & Verification
As highlighted in yesterday’s AMA with our CEO, Basith, two critical features—Confidential Compute and Compute Verification—are being developed through our partnerships with Marlin Protocol and other key players (with more partnerships to be announced soon).
- Confidential Compute ensures that user data remains private and secure while utilizing third-party compute resources.
- Compute Verification provides assurance that users receive the compute power they paid for, preventing resource misuse or fraudulent practices.
These features will be game-changers in the GPU provisioning space, setting ParallelAI and ParaHub apart as pioneers in building trustworthy and efficient compute marketplaces.
ParaHub's Role in the ParallelAI Ecosystem
ParaHub is poised to become a cornerstone of the wider ParallelAI ecosystem. While it stands as a significant product on its own, it also plays a crucial role in driving user adoption for Parilix and PACT.
As GPU resource demand skyrockets in an AI-driven future, ParaHub will serve as a vital bridge for users seeking compute solutions. This demand is expected to grow even before Parilix achieves widespread adoption as a language and before PACT caters to a broader range of programs, applications, and use cases. ParaHub is perfectly positioned to fill this gap in an emerging market, acting as both a standalone solution and a driver of adoption across the entire ParallelAI ecosystem.
What’s Next for ParaHub?
Following the PACT Beta launch this week, ParaHub will take center stage as a primary focus for our development team. We’re excited to share more details soon, including what users can expect from the initial version of ParaHub and its upcoming release in the weeks ahead.
✅ https://x.com/parallelaix/status/1861095921892892885?s=46&t=T7jBG_AvrN2ghmmSWDH1Ew
Introducing the ParaHub Vision
At the end of last week, we shared our first dedicated video explaining the value proposition behind ParaHub in simple terms. In essence, ParaHub is the first AI-powered GPU Marketplace Aggregator, designed to help users effortlessly find the best compute options at the lowest possible price in a matter of seconds.
Building Strategic Partnerships
To bring this vision to life, our focus has been on fostering relationships with key partners who will provide the compute infrastructure that ParaHub will rely on. Since its inception, ParallelAI has secured a number of long-standing partnerships. Initially, these partnerships were focused on ensuring access to adequate compute resources. However, in recent months, they have become more nuanced and deliberate, shaping up to significantly enhance ParaHub’s product offering.
Key Features: Confidential Compute & Verification
As highlighted in yesterday’s AMA with our CEO, Basith, two critical features—Confidential Compute and Compute Verification—are being developed through our partnerships with Marlin Protocol and other key players (with more partnerships to be announced soon).
- Confidential Compute ensures that user data remains private and secure while utilizing third-party compute resources.
- Compute Verification provides assurance that users receive the compute power they paid for, preventing resource misuse or fraudulent practices.
These features will be game-changers in the GPU provisioning space, setting ParallelAI and ParaHub apart as pioneers in building trustworthy and efficient compute marketplaces.
ParaHub's Role in the ParallelAI Ecosystem
ParaHub is poised to become a cornerstone of the wider ParallelAI ecosystem. While it stands as a significant product on its own, it also plays a crucial role in driving user adoption for Parilix and PACT.
As GPU resource demand skyrockets in an AI-driven future, ParaHub will serve as a vital bridge for users seeking compute solutions. This demand is expected to grow even before Parilix achieves widespread adoption as a language and before PACT caters to a broader range of programs, applications, and use cases. ParaHub is perfectly positioned to fill this gap in an emerging market, acting as both a standalone solution and a driver of adoption across the entire ParallelAI ecosystem.
What’s Next for ParaHub?
Following the PACT Beta launch this week, ParaHub will take center stage as a primary focus for our development team. We’re excited to share more details soon, including what users can expect from the initial version of ParaHub and its upcoming release in the weeks ahead.
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PACT (Parilix Automated Code Transformer) Beta is Now Live!
We’re thrilled to announce the launch of the PACT Beta, a major milestone in revolutionizing GPU compute access for developers of all backgrounds. Visit the ParallelAI website to access PACT and experience seamless code optimization today.
Please note:
- The Beta version is not a final product; enhancements and further AI training are ongoing.
- Currently, PACT supports code conversions for foundational algorithms listed in the Parilix GitHub repository. Conversion for unsupported code may not succeed at this stage, but we’re actively expanding the codebase.
Join us in shaping the future of compute.
Try PACT now!
https://www.parallelai.tech/
https://x.com/ParallelAIx/status/1861862794037244265
We’re thrilled to announce the launch of the PACT Beta, a major milestone in revolutionizing GPU compute access for developers of all backgrounds. Visit the ParallelAI website to access PACT and experience seamless code optimization today.
Please note:
- The Beta version is not a final product; enhancements and further AI training are ongoing.
- Currently, PACT supports code conversions for foundational algorithms listed in the Parilix GitHub repository. Conversion for unsupported code may not succeed at this stage, but we’re actively expanding the codebase.
Join us in shaping the future of compute.
Try PACT now!
https://www.parallelai.tech/
https://x.com/ParallelAIx/status/1861862794037244265
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DEVELOPMENT UPDATE: Milestones and the Path Forward
A Major Milestone: Launching PACT
This week marks a significant milestone for ParallelAI with the launch of our first user-centric product, PACT—an automated code transformer powered by AI. Utilizing our ever-growing Parilix code repository, PACT allows developers and builders of all backgrounds to seamlessly optimize their code, maximizing the potential of their GPU hardware.
Building the Foundation: Parilix GitHub
Since opening the Parilix GitHub repository to the public on October 20th, our development team has been tirelessly working to expand the repository. We’ve added numerous code examples for users to reference and to aid in training the PACT AI. This has been a collaborative effort, with contributions from both our core team and early adopters in the community.
PACT Beta Goes Live
On November 27th, we launched the PACT Beta, marking a new era in code optimization. PACT has reached a level where it can accurately convert several algorithm types from traditional programming languages into Parilix. This is especially valuable for users who may not yet have the capacity to work with Parilix directly at a lower level.
The Beta period is critical for refining PACT. We are actively addressing bugs, enhancing the front end, and ensuring all platform components function smoothly. Additionally, every job processed through PACT during the Beta is logged for supervised learning, accelerating the AI’s ability to handle a broader range of code.
The Next Step: ParaHub
With PACT in Beta, our focus now shifts to enabling seamless access to GPU compute power directly through the ParallelAI platform. This effort is centered around the upcoming release of ParaHub, our GPU Marketplace Aggregator.
Through strategic partnerships established in recent weeks with providers such as IO.Net, Aethir, Flux, and others, we’ve secured robust GPU resources. These integrations will allow users to:
1. Check available compute power.
2. Compare pricing across third-party providers.
3. Select the option best suited to their requirements.
This will enable users to run both Parilix-optimized code and their own code with ease, completing the end-to-end workflow for the initial phase of the ParallelAI platform.
Looking Ahead: ParaHub and Phase 2
The official release date for ParaHub will be announced soon. Its launch will mark the completion of the initial phase of the ParallelAI ecosystem, providing users with a seamless solution from code optimization to execution on GPU hardware.
We’re excited to share more details about Phase 2 in the near future, which will build on this foundation and expand the capabilities of the platform even further.
Thank You for Your Support
Thank you to our community and partners for your ongoing support. Stay tuned for further updates as we continue to drive innovation and reshape the future of GPU computing.
✅ https://x.com/parallelaix/status/1862594354340405433?s=46&t=T7jBG_AvrN2ghmmSWDH1Ew
A Major Milestone: Launching PACT
This week marks a significant milestone for ParallelAI with the launch of our first user-centric product, PACT—an automated code transformer powered by AI. Utilizing our ever-growing Parilix code repository, PACT allows developers and builders of all backgrounds to seamlessly optimize their code, maximizing the potential of their GPU hardware.
Building the Foundation: Parilix GitHub
Since opening the Parilix GitHub repository to the public on October 20th, our development team has been tirelessly working to expand the repository. We’ve added numerous code examples for users to reference and to aid in training the PACT AI. This has been a collaborative effort, with contributions from both our core team and early adopters in the community.
PACT Beta Goes Live
On November 27th, we launched the PACT Beta, marking a new era in code optimization. PACT has reached a level where it can accurately convert several algorithm types from traditional programming languages into Parilix. This is especially valuable for users who may not yet have the capacity to work with Parilix directly at a lower level.
The Beta period is critical for refining PACT. We are actively addressing bugs, enhancing the front end, and ensuring all platform components function smoothly. Additionally, every job processed through PACT during the Beta is logged for supervised learning, accelerating the AI’s ability to handle a broader range of code.
The Next Step: ParaHub
With PACT in Beta, our focus now shifts to enabling seamless access to GPU compute power directly through the ParallelAI platform. This effort is centered around the upcoming release of ParaHub, our GPU Marketplace Aggregator.
Through strategic partnerships established in recent weeks with providers such as IO.Net, Aethir, Flux, and others, we’ve secured robust GPU resources. These integrations will allow users to:
1. Check available compute power.
2. Compare pricing across third-party providers.
3. Select the option best suited to their requirements.
This will enable users to run both Parilix-optimized code and their own code with ease, completing the end-to-end workflow for the initial phase of the ParallelAI platform.
Looking Ahead: ParaHub and Phase 2
The official release date for ParaHub will be announced soon. Its launch will mark the completion of the initial phase of the ParallelAI ecosystem, providing users with a seamless solution from code optimization to execution on GPU hardware.
We’re excited to share more details about Phase 2 in the near future, which will build on this foundation and expand the capabilities of the platform even further.
Thank You for Your Support
Thank you to our community and partners for your ongoing support. Stay tuned for further updates as we continue to drive innovation and reshape the future of GPU computing.
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ZkAGI x ParallelAI
We’re thrilled to announce our partnership with @Zk_AGI, the first privacy AI DePIN project, integrating advanced technologies within the blockchain with AI.
About ZkAGI
ZkAGI is a privacy-centric system that enables users and businesses to leverage powerful AI capabilities securely, ensuring that their personal data are protected.
About the Partnership
ZkAGI clients will have access to $PAI’s parallel code as a premier option, made available for ZkAGI’s AI developer clients.
Some ZkAGI products include GPU clustering, Zynapse API for accessing computing power, and the ZkSurfer digital assistant for blockchain needs.
Stay tuned for more updates regarding this partnership!
✅ https://x.com/parallelaix/status/1862887463548543405?s=46&t=T7jBG_AvrN2ghmmSWDH1Ew
We’re thrilled to announce our partnership with @Zk_AGI, the first privacy AI DePIN project, integrating advanced technologies within the blockchain with AI.
About ZkAGI
ZkAGI is a privacy-centric system that enables users and businesses to leverage powerful AI capabilities securely, ensuring that their personal data are protected.
About the Partnership
ZkAGI clients will have access to $PAI’s parallel code as a premier option, made available for ZkAGI’s AI developer clients.
Some ZkAGI products include GPU clustering, Zynapse API for accessing computing power, and the ZkSurfer digital assistant for blockchain needs.
Stay tuned for more updates regarding this partnership!
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