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How does StarLandAI Enhance Machine Learning Model efficiency?

StarLandAI
StarLandAI
Maintainer

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The advent of Large Language Models (LLMs) has marked a new era in the field of machine learning, bringing with it unprecedented capabilities for natural language processing. However, these models’ size and complexity pose significant challenges in terms of deployment, particularly on devices with limited computational resources. Enter quantization, a technique that has risen to prominence as a means of optimizing LLMs for efficient inference. GGML (Generic GEMM Library), a cutting-edge C library developed by Georgi Gerganov, stands at the forefront of this optimization, offering innovative quantization methods that enhance model performance without compromising on accuracy.

The Necessity for Quantization in LLMs

Quantization operates on the principle of reducing the numerical precision of the model’s weights, thereby minimizing memory consumption and accelerating inference times. This is not merely a matter of efficiency; it’s a necessity for the practical deployment of LLMs, especially on consumer hardware that may lack the high-end GPUs typically used in data centers.

GGML: A Foundation for Optimized Machine Learning

GGML is more than a library; it’s a comprehensive toolkit designed to streamline the deployment of LLMs. It provides the foundational elements for machine learning operations, such as tensors, and extends its capabilities with a unique binary format, GGUF, for distributing and storing LLMs. The GGUF format is extensible and future-proof, ensuring that new features can be added without breaking compatibility with existing models.

Quantization Methods in GGML

GGML supports a variety of quantization methods, each tailored to different trade-offs between model accuracy and computational efficiency:

  • q4_0: A standard 4-bit quantization method that offers a good balance between size and performance.
  • q4_k_m: A mixed-precision approach that applies higher precision to certain layers, such as attention.wv and feed_forward.w2, to maintain accuracy while reducing the overall model size.
  • q5_k_m: This method further increases precision for critical layers, providing higher accuracy at the cost of increased resource usage and potentially slower inference.

Practical Quantization with GGML

The process of quantization with GGML is both sophisticated and practical. It begins with converting the model’s weights into GGML’s FP16 format, followed by the application of the chosen quantization method. This conversion can be executed on platforms like Google Colab, leveraging their free GPU resources to facilitate the process.

Efficient Inference with llama.cpp

The llama.cpp library, also developed by Georgi Gerganov, is a critical component in the deployment of quantized models. Written in C/C++, it is designed for efficient inference of Llama models on both CPUs and GPUs. This dual-compatibility makes it an ideal tool for deploying models across a wide range of devices.

Quantization and CPU Inference

One of the most significant advantages of the GGML and llama.cpp combination is their ability to enable efficient CPU-based inference. By offloading some layers to the GPU where possible, or relying solely on the CPU for inference, these tools make it feasible to run LLMs on devices that may not have the latest GPU technology.

Technical Insights into Quantization

At its core, GGML’s quantization process involves grouping weights into blocks and applying a quantization scheme that reduces their precision. For example, the Q4_K_M method might store most weights at 4-bit precision while reserving higher precision for specific layers. Each block of weights is processed to derive a scale factor, which is then used to quantize the weights efficiently.

Comparative Analysis of Quantization Techniques

When evaluating the performance of GGML’s quantization against other methods like NF4 and GPTQ, it shows a competitive edge in terms of perplexity — a measure of how well a model predicts its own test data. While the differences may be subtle, they can be significant when considering the trade-offs between model size, inference speed, and accuracy.

The Future of Quantization in Machine Learning

Quantization is more than a passing trend; it is a transformative approach that is set to redefine how machine learning models are deployed. As the technology matures, we can expect to see further improvements in mixed-precision quantization and other advanced techniques that will push the boundaries of what is possible with LLMs.

Conclusion

GGML’s quantization techniques are a testament to the potential for optimizing machine learning models for efficiency without sacrificing performance. By enabling the deployment of large models on devices with limited resources, GGML is helping to democratize access to advanced machine learning capabilities. As the field of machine learning continues to evolve, the role of GGML and libraries like it will be pivotal in shaping the future of model deployment, ensuring that the benefits of LLMs can be fully realized across a diverse array of applications and environments.

In summary, GGML and its associated tools like llama.cpp are not just optimizing the present state of machine learning models; they are setting the stage for a future where the deployment of sophisticated LLMs is as accessible and efficient as possible. With continued advancements in quantization techniques, the gap between research and practical application will continue to narrow, bringing us closer to a world where the full potential of machine learning can be harnessed by all.

The Core of StarLandAI’s DePIN:Proof of Computation

StarLandAI
StarLandAI
Maintainer

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What are Verifiable Computing and Proof of Computation?

Verifiable Computing is a computational paradigm that allows computers to delegate the computation of specific functions to other untrusted clients while ensuring that the results obtained can be effectively verified. These clients, upon completing the relevant computations, provide proof that confirms the correctness of the computation process. With the significant advancement of decentralized computing and cloud computing, the scenario of outsourcing computational tasks to untrusted parties has become increasingly common. It also reflects a growing demand for enabling devices with limited computational power to delegate their computational tasks to third-party, more powerful computational service platforms. The concept of verifiable computing was first proposed by Babai et al. [1] and has been explored under various names, such as “checking computations” (Babai et al.), “delegating computations” [2], “certified computation” [3], etc. The term “verifiable computing” was explicitly defined by Rosario Gennaro, Craig Gentry, and Bryan Parno [4].

Proof of Computation (PoC) is a cryptographic protocol that allows a verifier to confirm that a computational task has been correctly executed without having to re-execute the entire computation process. The core idea of this protocol is that the executor of the computation task provides a brief proof, which is compact enough to be efficiently verified while also conclusively demonstrating the correctness of the computation. In PoC, the executor first computes the input data and generates an output result. Then, they create a proof that contains sufficient information to verify the correctness of the output result without revealing the input data or the specifics of the computation. The verifier can use this proof to confirm the correctness of the computation without knowing the specific computation process or the original data. Proof of Computation has applications in multiple fields, such as:

  • Cloud Computing: In cloud services, customers may wish to verify that their data is being processed correctly without disclosing the data itself. PoC allows cloud service providers to provide proof that they have correctly executed the computation task.

  • Distributed Systems: In a distributed computing environment, nodes may need to verify the computational results of other nodes to ensure the consistency and reliability of the entire system.

  • Blockchain: In blockchain technology, PoC can be used to verify the execution results of smart contracts, which is crucial for ensuring the security and transparency of decentralized applications.

  • Privacy Protection: PoC can be used to protect personal privacy as it allows the verification of the correctness of computations without disclosing the original data.

Verifiable Computing is a broad field that encompasses a variety of technologies and applications, and Proof of Computation (PoC) is a key technology within this field, used to achieve the verifiability of computations. PoC is a component of verifiable computing, and together they support a more secure and trustworthy computing environment.

Mainstream Proof of Computation Principles and Technologies

2.1 Proof of Computation (PoC) based on Zero-Knowledge Proofs

(1) Proof of Computation (PoC) based on Zero-Knowledge Proofs is a cryptographic method that allows a prover to demonstrate to a verifier that a computational task has been correctly executed without revealing the specifics of the computation or any sensitive data. The core advantage of this method lies in privacy protection and enhanced security, as the verifier only needs to know whether the result is correct, not how it was achieved. The main technical process is as follows:

  • Define the computational task: First, it is necessary to clarify what the computational task to be verified is. This could be a mathematical function, an algorithm, or any other type of computational process.

  • Generate the proof: The prover performs the computational task and generates a zero-knowledge proof. This proof is a cryptographic structure that contains sufficient information to prove the correctness of the computation without including any sensitive information about the computational inputs or intermediate steps. Zero-knowledge proofs typically rely on complex cryptographic constructs such as elliptic curves, pairings, or zero-knowledge SNARKs (Succinct Non-Interactive Arguments of Knowledge).

  • Verify the proof: Upon receiving the proof, the verifier runs a verification algorithm to check the validity of the proof. If the proof is valid, the verifier can be confident that the computational task has been correctly executed without knowing the specific computational details.

  • Maintain privacy: Throughout the process, the prover does not need to disclose any information about the computational inputs. This is crucial for protecting data privacy and preventing potential data leaks.

(2) There are various technical approaches to implementing PoC based on zero-knowledge proofs, including:

  • zk-SNARKs: This is a special type of zero-knowledge proof that provides properties of succinctness, non-interaction, and knowledge of proof. zk-SNARKs allow the prover to generate a short proof that the verifier can verify offline without interacting with the prover.

  • zk-STARKs: This is a zero-knowledge proof that does not require a trusted setup, offering transparency and scalability. Compared to zk-SNARKs, zk-STARKs do not rely on complex mathematical puzzles, making them easier to implement and verify.

  • Bulletproofs: This is a new type of zero-knowledge proof that provides efficient verification while protecting privacy, particularly suitable for blockchain applications involving transaction amounts.

2.2 Proof of Computation based on Trusted Hardware

(1) In contrast to the purely software implementation of zero-knowledge proofs, we can also implement PoC based on Trusted Hardware, which is a method that utilizes physical security features to ensure the correctness and security of the computation process. The implementation typically involves hardware security modules (such as secure processors, cryptographic cards, or Trusted Execution Environments TEE), designed to provide an isolated and secure execution environment, resistant to external attacks and unauthorized access. The main technical process is as follows:

  • Build secure boot for hardware and applications: Secure boot is a process that ensures only authenticated, unmodified software can be executed on the hardware. This is a fundamental step in ensuring hardware security.

  • Agree on cryptographic anchoring: When using trusted hardware, computational proofs are often combined with cryptographic anchoring. This means that the results or evidence of computation are associated with a cryptographic key, which is protected by trusted hardware.

  • Compute based on Trusted Execution Environment (TEE): TEE is a combination of hardware and software that provides a secure execution environment, protecting the code and data loaded into the TEE from external attacks and tampering. TEE typically includes a secure processor and an isolated memory area.

  • Verify computation through remote attestation: Remote attestation is a mechanism that allows the authenticity and integrity of the TEE to be verified remotely. Through remote attestation, a client can be assured that it is interacting with a genuine, unmodified TEE.

(2) The advantage of PoC based on trusted hardware lies in their provision of a physically secure computing environment, which is theoretically very difficult to breach. They also offer high performance.

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StarLandAI’s proof of computation

3.1 Overall Process

To provide a reliable and scalable computational power infrastructure, StarLandAI has implemented a complete set of hardware authentication and proof of computation mechanisms by combining secure hardware with cryptographic algorithms. The overall process is illustrated in the figure below:

  • During the startup phase of the computational node, a self-check of the device is performed to inspect the status of components such as the GPU and CPU, as well as the versions of their drivers.

  • The computational node daemon verifies the hash of the StarLand runtime image.

  • The computational node daemon launches the StarLandAI runtime. If a trusted execution environment (TEE) is available, it will initiate the runtime based on the TEE.

  • The StarLandAI runtime conducts a consistency check and loads the model.

  • Once launched, the StarLandAI runtime checks its own operating environment, loads the model, identifies the certificate and device information, generates a runtime authentication report, and sends it to the StarLandAI DePIN Master in the form of a heartbeat.

  • The StarLandAI DePIN Master validates the runtime information based on the received report and completes the node access procedure.

  • For a computational power assessment and inference task, the StarLandAI DePIN Master encrypts the task parameters and challenge values using the public key of the runtime and issues them.

  • The runtime decrypts the task information to generate a runtime challenge response and a model-specific call challenge value, then calls the model to obtain the inference result.

  • The runtime verifies the model challenge response value and the inference result. It constructs a single-call computational proof using the runtime challenge response generated in step 8 and returns it to the StarLandAI DePIN Master. Upon receiving the response, the StarLandAI DePIN Master completes the check and results, concluding the entire process.

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3.2 Composition of Computation Proof

StarLandAI’s algorithm for generating computation proofs is an innovative solution designed to optimize the utilization of computational resources. The algorithm not only intelligently evaluates the computational capacity of each computational node to ensure the most suitable computational tasks are matched, but it also takes into account the computational throughput of the nodes to maximize efficiency and performance. Moreover, what sets StarLandAI apart is its in-depth analysis of the model capabilities supported by the nodes, allowing us to accurately schedule complex computational tasks, especially those advanced applications that require specific model support. With this comprehensive consideration, StarLandAI can significantly enhance the execution speed and accuracy of computational tasks while reducing operational costs. Our computation proof generation algorithm is the core that drives efficient, intelligent, and scalable management of computational resources, providing unparalleled support for AI and machine learning workloads. StarLandAI is committed to leading the future of computational resource management through cutting-edge technology, unlocking infinite possibilities. StarLandAI computation proofs are divided into two categories:

  • Runtime Verified Report: A periodic assessment proof for an integrated computational node.
  • Proof of Inference Computation: A workload assessment proof for a specific inference task.

(1) Runtime verified report

The Runtime Verified Report is a periodic assessment proof for an integrated computational node. After the node completes self-inspection and initialization, it will periodically report its heartbeat, which must include the Runtime Verified Report. The specific structure of the Runtime Verified Report includes the following content:

  • Node Identity Address (associated with the identity certificate)
  • Node Computational Power Score (the calculation formula will be provided later)
  • Node Computational Power Equipment Information
  • Node Geographic Distribution Information
  • Node Identity Authentication Signature
  • Hardware Authentication Report of the Runtime

The node identity corresponds to a pair of public and private keys. StarLandAI will receive the node-related registration identity certificate information to support the verification, encryption, and authentication of the subsequent computational process. At the same time, the computational power equipment information, computational power score, and geographic distribution information will support StarLandAI in selecting the optimal computational power for scheduling during subsequent inference tasks. Each heartbeat report requires a node identity authentication signature to prevent impersonation by malicious parties.

(2) Proof of inference computation

Proof of Inference Computation is a proof of computational contribution for a specific inference task, which specifically includes the following content:

  • Computational Node Information
  • Hardware Authentication Report of the Computational Runtime
  • Task Challenge Response Value and Signature
  • Hash of the Model Snapshot Corresponding to the Task
  • Node Computational Power Score Involved in This Task

Appendix

Computation Power Score = S(Computing_Card_Count×Single_Card_Inference_Throughput×Deployed_Model_Scale×Model_Count) Where:

  • ScoreScore: Represents the final score or performance metric.
  • S: Is a function that normalizes the product of several factors into a standardized score.
  • Computing_Card_CountComputing_Card_Count: Indicates the number of computing devices (such as GPUs or TPUs).
  • Single_Card_Inference_ThroughputSingle_Card_Inference_Throughput: Refers to the ability of a single computing device to process model inferences within a unit of time.
  • Deployed_Model_ScaleDeployed_Model_Scale: Represents the measure of the scale or complexity of the deployed model, which correlate with the number of model parameters or computational requirements.
  • Model_CountModel_Count: Denotes the total number of models deployed in the computational environment.

Reference

  1. Babai, László; Fortnow, Lance; Levin, Leonid A.; Szegedy, Mario (1991–01–01). “Checking computations in polylogarithmic time”. Proceedings of the twenty-third annual ACM symposium on Theory of computing — STOC ’91. STOC ’91. New York, NY, US: ACM. pp. 21–32. CiteSeerX. 10.1.1.42.5832. doi: 10.1145/103418.103428. ISBN 978–0897913973. S2CID 16965640.

  2. ^ Goldwasser, Kalai, Yael Tauman; Rothblum, Guy N. (2008–01–01). “Delegating computation”. Proceedings of the fortieth annual ACM symposium on Theory of computing. STOC ’08. New York, NY, US: ACM. pp. doi: 10.1145/1374376.1374396. ISBN 9781605580470. S2CID 47106603.

  3. ^ Jump up to:(a) (b) “Computationally Sound Proofs”. SIAM Journal on Computing. 30 (4): 1253–1298. CiteSeerX 10.1.1.207.8277. doi: 10.1137/S0097539795284959. ISSN

  4. Gennaro, Rosario; Gentry, Craig; Parno, Bryan (31 August 2010). Non-Interactive Verifiable Computing: Outsourcing Computation to Untrusted Workers. CRYPTO 2010. doi: 10.1007/978–3–642–14623–7_25.

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StarLandAI: The First AI MaaS DePIN Network

StarLandAI
StarLandAI
Maintainer

StarLandAI is the first AI MaaS DePIN network that supports all types of large multimodal model applications. It is the first GenAI Model-as-a-Service (MaaS) DePIN network, capable of running large multimodal models using any type of computing device.

Why do we need DePIN? As we all know, deploying, training, fine-tuning and managing multimodal large models, integrating text, images, sound, databases and distributed cloud-native systems, is highly complex. AI computing spans server capabilities like H100, A100, and consumer-grade power such as 4090, 3090, and 3080, integrated graphics, and CPUs, making unified management challenging. On the other hand, running large models efficiently on low-end compute resources such as 3090, integrated graphics, and CPUs is highly challenging, leading to idle resources. The advantage of StarLandAI from the current DePIN network is that the current DePIN networks, despite integrating substantial AI computing power, lack adequate support for developing, deploying, and maintaining generative AI applications, leading to limited real-world usage. StarLandAI’s vision is Harness All Idle Compute Resources into DePIN Layer, so StarlandAI can Innovate GenAI DePIN Layer for Blockchains, through simplifying AI Development with One-Click APIs.

How can StarlandAI become the first AI MaaS DePIN network? StarLandAI supplies lower barriers for AI developers, bypassing concerns about computing power and multimodal model complexity. StarLandAI enables large models on low-end compute such as 3080, 3090 and CPUs, increasing earnings and opportunities for compute providers. So more Web2 AI users can be attracted to blockchains, enhancing its practicality.

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The architecture of StarLandAI can provide multimodal large models through cloud services and APIs, including text, voice, image, and video, etc., allowing for scalability, ease of access, and flexibility. StarLandAI can utilize microservices, containers, immutable infrastructure, and declarative APIs to ensure the rapid and resilient deployment of GenAI services on any type of computing device such as 4090, 3090, 3080, integrated graphics, and CPUs. StarLandAI can also assist developers in creating GenAI applications such as AI avatars, image, voice, music, and video generation with GPT-level quality, compatible with blockchains including Solana, Ethereum, and Bitcoin.

So the first AI Dapp on StarlandAI is AI avatars, which can combine multimodal large models. In StarLandAI, you can easily create your on-chain AI avatar and turn it into a digital asset on blockchain, from which you can obtain steady earnings from digital persona’s ongoing services. For the holder of computing power, we supply running AI avatars on DePIN devices, and you can contribute computing devices such as PCs, mobiles, GPUs, etc., to access the network for more benefits.

So, let’s take a look at how to chat with AI avatars together.

Go and talk to your favourite AI avatars

You can find each AI avatar running on Starland on the “All Avatars” page of Starland. There are cute and clever soft girls, overbearing CEOs, handsome straight-A students, and even AI avatars like Trump. You can chat with them about anything at will, and you will find that you seem to be talking to the real Trump.

Not only can you chat, he may also reply to you with voice.If you are a little luckier, you can also receive his emoji pack. Isn’t it very fun? There are hundreds of characters for you to choose from, and you can enjoy yourself to the fullest. You can let the young girl accompany you to chat, or you can also find Yichan the little monk to solve your worries and doubts.

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Create your own AI Avatars.

On StarLandAI, everyone can create their own AI digital person. You can customize the personality, characteristics, background, appearance, and even the voice of the AI digital person. With such a rich form, creating an AI digital person only requires two steps.

Even if only one step is needed, you can completely let the AI help you generate the background information, appearance, and voice of a character. If you think it is good, just click to confirm, and you can have a customized AI Avatar. You can even use your own voice as the voice of the AI Avatars.

In this process, StarLandAI uses deep learning techniques such as CNN, RNN, and Transformers, integrating data from multiple sources such as text, images, audio, and video, to enhance the understanding and adaptability of AI avatars. So you will get a very realistic AI avatar.

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Let your PC make money for you.

The difficulty of GenAI models, deploying, training, fine-tuning and managing multimodal large models, integrating text, images, sound, databases and distributed cloud-native systems, is highly complex. On StarLandAI, now you can use your PC device to participate in the training and reasoning of AI avatars. Running large models efficiently on low-end compute resources such as 3090. So everyone can participate in it.

On StarLandAI, your DePIN devices support running large-scale models with multiple modules: Enabling various devices, such as PCs, smartphones, IoT (IoT) devices, edge computing nodes, etc., to execute complex models involving multiple modalities, such as text, images, and audio. So you can obtain stable returns by providing computing power.

StarLandAI’s vision is to support harnessing all Idle compute resources. It integrates various unused computing powers, including GPUs such as the 4090, 3090, and 3080, as well as computing from PCs, edge devices, and mobile platforms, transforming them into a versatile resource pool for multimodal large-scale models. StarLandAI takes advantage of Solana’s high efficiency and good ecosystem to create a complete narrative network of its own, and builds a complete network of various roles such as computing power providers, AI avatars creators, and AI users. StarLandAI has innovated AI Layer2 for Blockchains and brings Web 2.0 AI users to blockchain.

Just online for a week, StarlandAI has already been used by more than 10,000 people, causing a big wave in the Solana ecosystem. The website (www.starland.ai), on par with c.ai and GPT in response speed and multimodal capabilities, is live. Welcome everyone to try it out. In the early stage, there are various types of points given away.

StarLandAI, the first AI MaaS DePIN network supports all types of large multimodal model applications

StarLandAI
StarLandAI
Maintainer

StarLandAI is a decentralized AI and blockchain network in the Web3 era, aiming to create a globally accessible ecosystem for AI + Web3 applications and accelerate global innovation in a large multimodal model. It provides AI capabilities such as AI-generated content creation and conversational AI, while supporting Web3 capabilities like wallets, NFT generation, data crowdfunding, and computing power staking. Serving as an AI layer, StarLandAI can cater to multiple public chains and is committed to becoming the world’s first AI MaaS DePIN network application ecosystem.

StarLandAI can supply generative AI models as a service (MaaS), providing multimodal large models through cloud services and APIs, including text, voice, image, and video, etc., allowing for scalability, ease of access, and flexibility. StarlandAI uses cloud-native Enhanced DePIN Networks and utilizes microservices, containers, immutable infrastructure, and declarative APIs to ensure the rapid and resilient deployment of GenAI services on any type of computing device. With generative AI applications Matrix, StarLandAI offers GenAI applications like AI avatars, images, voice, music, and video generation at GPT-level quality, compatible with blockchains including Solana, Ethereum, Bitcoin, and more.

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On the crossroads between AI and crypto, there are always many questions to be solved. For example, the difficulty of GenAI models, including deploying, training, fine-tuning, and managing multimodal large models, integrating text, images, sound, databases, and distributed cloud-native systems, is highly complex. Types of computing power are so varied, AI computing spans server capabilities like H100, A100, and consumer-grade power such as 4090, 3090, and 3080, integrated graphics, and CPUs, making unified management challenging. Low-end computing power is idle. Running large models efficiently on low-end compute resources such as 3090, integrated graphics, and CPUs is highly challenging, leading to idle resources. Furthermore, there is a lack focus on applications. The current DePIN networks, despite integrating substantial AI computing power, lacks adequate support for developing, deploying, and maintaining generative AI applications, leading to limited real-world usage.

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StarLandAI is the first decentralized AI avatar network based on large-scale multimodal models. The StarLandAI team itself is a mature team that has been deeply involved in AI for many years. They have developed several core AI technologies, including:

  • Multimodal large-scale models: Using deep learning techniques such as CNN, RNN, and Transformers, integrating data from multiple sources such as text, images, audio, and video, to enhance the understanding and adaptability of AI avatars;

  • Distributed low-memory training and inference: Distributing the model across multiple GPUs, thereby being able to take advantage of tensor parallelism, data parallelism, pipelined parallelism, gradient accumulation, and memory optimization techniques, this method allows GPU with smaller memory capacities to participate in inference calculations;

  • Cross-chain AI Avatar mining: Supporting Solana, Ethereum, BNB Chain, etc.—through the improved protocols of SPL 22 and ERC721/ERC404, it promotes the separation of NFT ownership and usage rights, and improves liquidity and participation by securitizing the inference revenue rights represented by NFT;

  • DePIN devices that support running large-scale models with multiple modules: Enabling various devices, such as PCs, smartphones, IoT (IoT) devices, edge computing nodes, etc., to execute complex models involving multiple modalities, such as text, images, and audio;

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StarLandAI’s vision is to support harnessing all idle compute resources. StarLandAI integrates various unused computing powers, including GPUs such as the 4090, 3090, and 3080, as well as computing from PCs, edge devices, and mobile platforms, transforming them into a versatile resource pool for multimodal large-scale models. Innovative AI Layer2 for Blockchains, advances Solana and others with AI Layer 2, bringing Web 2.0 AI users to blockchain. StarlandAI aims to build a healthy economic ecosystem among computer providers, developers, AI creators, and public blockchains, fostering mutual growth and innovation. StarlandAI offers One-Click APIs, helping developers with easy-to-use APIs and cloud services, enabling seamless use and further development of all open-source large models without requiring in-depth technical knowledge.

StarLandAI’s GenAI MaaS product has already been running, generating over $500,000 in revenue. The AI Avatar product (www.starland.ai), on par with c.ai and GPT in response speed and multimodal capabilities, is live. StarLandAI also leverages low-end DePIN computing, like 3090 and 3080 GPUs, reducing total costs by 90%. Furthermore, StarlandAI has integrated with Solana blockchain via point smart contracts, NFTs, and model mining.

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For the ecosystem, StarLandAI takes advantage of Solana’s high efficiency and good ecosystem to create a complete narrative network of its own, and builds a complete network of various roles such as computing power providers, AI digital human creators, and AI users. Each role can find a position in the ecosystem and obtain income.

Today’s Solana ecosystem is showing amazing vitality. Compared with the previous bull market, Solana has achieved amazing results in many aspects, such as infrastructure, ecological applications, market popularity, and wealth effects. Solana-based StarLandAI is even more so, which is really anticipated.

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