I. Introduction​

Large Language Models (LLMs) have revolutionized the field of natural language processing, offering unprecedented capabilities in understanding and generation. However, the computational intensity of LLMs poses significant challenges when deploying these models on mid-range GPUs, which are common in many practical applications. The primary obstacles are the substantial memory requirements and the need for high throughput to maintain interactive response times. In this article, we delve into the theoretical underpinnings of three main strategies that we have adopted on StarLand to optimize LLM inference on such hardware: Model Parallelism, Pipeline Parallelism, and Tensor Parallelism. Additionally, we explore advanced memory management techniques that leverage concepts from virtual memory management in operating systems on StarLand. Our goal is to provide a depth of technical insight into how these strategies can be effectively employed to enhance LLM inference on mid-range GPUs.

II. Background​

A. LLM Inference and GPU Limitations​

LLMs, such as the Transformer architecture, consist of multiple layers that process input sequences to generate outputs or predictions. The inference process is memory-intensive, as it requires the storage of a complete set of model parameters and intermediate activation states. For mid-range GPUs with limited memory, this poses a significant challenge. The memory capacity of these GPUs restricts the size of the LLM that can be deployed and the batch size that can be processed simultaneously, leading to underutilization of computational resources and increased latency.

B. Parallelization Concepts for LLMs​

To overcome the limitations of mid-range GPUs, StarLand employs parallelization techniques to distribute the computational load and optimize resource usage. We focus on three parallelization strategies:

1. Model Parallelism: This involves partitioning the model's layers across multiple GPUs. Each GPU processes a subset of the layers, and the outputs are aggregated to form the final prediction. The challenge lies in minimizing inter-GPU communication overhead while maintaining load balance.

2. Pipeline Parallelism: In this approach, multiple instances of the model or different stages of the inference pipeline are executed concurrently on the same GPU. This requires careful scheduling to maximize GPU utilization and reduce idle time between stages, a strategy effectively utilized in StarLand.

3. Tensor Parallelism: This strategy focuses on distributing the tensor operations themselves across multiple GPUs. By dividing the tensors into smaller chunks, each GPU processes a portion of the data, leading to a reduction in the memory footprint and potentially faster processing times, as implemented in StarLand.

C. Memory Management Techniques​

Effective memory management is crucial for LLM inference on mid-range GPUs. We adopt techniques inspired by virtual memory management:

1. Dynamic Memory Allocation: By allocating memory for the key-value cache (KV cache) dynamically, we can better match the memory usage to the actual length of the input sequences, thus reducing waste.

2. Paged Memory Management: Similar to paging in operating systems, we divide the KV cache into fixed-size blocks and manage these blocks as pages. This allows for more efficient memory utilization and the ability to share memory between different inference tasks.

3. Copy-on-Write Mechanism: To avoid unnecessary memory duplication, we implement a copy-on-write mechanism that creates a new copy of a memory block only when it is modified, thus conserving memory resources.

The effectiveness of these strategies is underpinned by their ability to reduce memory fragmentation and enable efficient sharing of memory resources. We will explore these concepts in greater detail in the subsequent sections, providing mathematical formulations where appropriate to illustrate the principles and their implications on system performance.

III. Parallelization Techniques for LLM Inference​

A. Model Parallelism​

Model parallelism involves distributing the layers of an LLM across multiple GPUs. Consider an LLM with 'L' layers to be distributed over 'G' GPUs. Each GPU is assigned a subset of layers, with the number of layers per GPU being $\frac{L}{G}$. The challenge is to minimize the communication overhead while maintaining computational balance.

Let $C_i$ represent the computational complexity of layer 'i' and $M_i$ the memory requirement. The goal is to find an allocation $A = \{a_1, a_2, ..., a_G\}$ where $a_g$ is the set of layers assigned to GPU 'g', such that the total communication overhead $O_{comm}$ is minimized and the memory requirements $M_{req}$ are balanced:

$A^* = \arg \min_{A} O_{comm}(A)$ $\text{s.t. } \sum_{i \in a_g} M_i \leq M_{max} \text{ and } \sum_{i \in a_g} C_i \approx \frac{1}{G} \sum_{j=1}^{L} C_j$

Here, $M_{max}$ is the maximum memory available per GPU, and the second constraint ensures that the computational load is evenly distributed.

B. Pipeline Parallelism​

Pipeline parallelism processes multiple instances of the model concurrently. If 'P' instances are processed in parallel, with each instance going through 'S' stages, the throughput $T$ can be increased:

$T = \frac{P \times S}{\text{Total time per instance}}$

The total time per instance is affected by the stage with the maximum latency $\text{Max}(s_1, s_2, ..., s_S)$. To maximize throughput, the system must pipeline stages efficiently and balance the load across stages.

C. Tensor Parallelism​

Tensor parallelism partitions the input tensors across GPUs. Given a tensor $T$ of size $D \times N$ to be split across 'G' GPUs, each GPU receives a sub-tensor $T_g$ of size $\frac{D}{G} \times N$. The key is to choose an optimal splitting ratio $R = \frac{D}{G}$ that minimizes the communication overhead while maximizing computational efficiency.

Assuming $T$ is a tensor representing input data for an LLM, the split tensor $T_g$ can be computed as:

$T_g = T_{((g-1) \times R + 1) : (g \times R), :}$

Where $R$ must be chosen such that the parallel computation of $T_g$ across GPUs minimizes the overall execution time $E$, which includes both computation and communication costs:

$E = \sum_{g=1}^{G} e_g + c(R, G)$

Here, $e_g$ is the computation time for tensor $T_g$ on GPU 'g', and $c(R, G)$ is the communication overhead that depends on the split ratio $R$ and the number of GPUs $G$.

These parallelization techniques, when combined with advanced memory management strategies, can significantly enhance the inference capabilities of LLMs on mid-range GPUs. The mathematical formulations provided offer a glimpse into the complexity of optimizing these systems, taking into account both computational and communication costs to achieve the best performance.

IV. Memory Management Strategies​

Effective memory management is a cornerstone for efficient LLM inference on mid-range GPUs. The strategies outlined below are inspired by principles from operating systems and are tailored to address the unique challenges posed by LLMs.

A. Dynamic Memory Allocation​

Dynamic memory allocation is essential for handling variable-length input sequences common in LLM inference, a challenge effectively addressed in StarLand. Instead of allocating a fixed, maximum-sized block of memory for each sequence, we allocate memory based on the actual sequence length. This approach significantly reduces memory waste due to over-provisioning.

Let $L$ be the length of the input sequence, $M(L)$ the memory required for a sequence of length $L$, and $B$ the maximum memory block size. The memory allocation $A(L)$ for a sequence of length $L$ is given by:

$A(L) = \min(M(L), B)$

This ensures that memory allocation is proportional to the sequence length, preventing unnecessary memory usage.

B. Paged Memory Management​

Paged memory management, analogous to virtual memory in operating systems, involves dividing the memory into fixed-size pages. This approach allows for efficient memory utilization and the ability to share memory between different inference tasks, as achieved in StarLand.

For a KV cache requiring $P$ pages, and each page being of size $S$, the memory manager maintains a page table that maps logical pages to physical pages. The memory manager's efficiency is characterized by its ability to minimize page faults and maximize page reuse.

C. Copy-on-Write Mechanism​

The copy-on-write (COW) mechanism is a memory optimization technique that comes into play during the inference process when multiple sequences share common prefixes. Instead of duplicating the entire memory block when a write operation is required, COW defers the copy until the actual modification occurs.

Given a memory block $B$ shared by $n$ sequences, the COW mechanism ensures that only the modified portion of $B$ is copied. The memory saving $S_{COW}$ can be expressed as:

$S_{COW} = n \times \text{Size}(B) \times (1 - \frac{\text{Modified Portion}}{\text{Size}(B)})$

This formula captures the memory saving achieved by deferring the copy operation until it is necessary.

D. Swapping and Recomputation​

Swapping and recomputation are two strategies to handle memory eviction when the GPU memory is fully utilized.

• Swapping involves moving less frequently accessed data to a slower, auxiliary memory (such as system RAM or SSD). When the data is needed again, it is swapped back into the GPU memory. The swap operation $S_{swap}$ is modeled as:

  $S_{swap} = \text{Size}(B) \times \text{Swap Rate}$

• Recomputation is an alternative to swapping that involves recalculating the evicted data when it is required. This is particularly useful for data that can be recomputed from other available data without loss of information. The recomputation overhead $S_{recompute}$ is given by:

  $S_{recompute} = \text{Computational Cost} \times \text{Recompute Rate}$

The decision to swap or recompute is based on the relative costs and the current memory state.

By integrating these memory management strategies, we can significantly enhance the inference capabilities of LLMs on mid-range GPUs, allowing them to handle larger models and increased throughput with limited memory resources.

V. Theoretical Analysis and Performance​

The theoretical analysis of parallelization and memory management strategies is crucial for understanding their impact on LLM inference performance. This section delves into the mathematical modeling and analysis of the strategies discussed earlier, providing insights into their efficiency and potential benefits.

A. Performance Limits of Parallelized LLM Inference​

The performance of parallelized LLM inference is bounded by the slowest component in the pipeline, often referred to as the "critical path." The critical path is influenced by the parallelization strategy employed. For instance, in model parallelism, the critical path is determined by the maximum latency across all parallelized layers.

Let $T_i$ be the time taken to process layer $i$ in parallel, and $T_{max}$ be the maximum of $T_i$ for all layers. The throughput $\Theta$ of the parallelized system is given by:

$\Theta = \frac{1}{T_{max}}$

In an ideal scenario with no communication overhead, the throughput would be inversely proportional to the latency of the slowest layer. However, in practice, communication overhead $O_{comm}$ must be considered, leading to an effective throughput $\Theta_{eff}$ :

$\Theta_{eff} = \Theta - O_{comm}$

B. Optimal Parallelization Strategies​

Optimizing parallelization strategies involves finding a balance between computational load and communication overhead. The optimal strategy minimizes the total execution time $E_{total}$, which includes both computation $C_{comp}$ and communication $C_{comm}$ times:

$E_{total} = C_{comp} + C_{comm}$

The computation time $C_{comp}$ can be estimated as the sum of the processing times for all layers or operations. The communication time $C_{comm}$ is influenced by the size of the data being communicated and the bandwidth of the interconnect between GPUs.

C. Performance Trade-offs in LLM Deployment​

There are trade-offs to consider when deploying LLMs on mid-range GPUs. For instance, increasing the parallelism level can lead to higher throughput but may also increase communication overhead. The efficiency of memory management techniques also has a trade-off curve with the complexity of the inference task.

The trade-off can be quantified by analyzing the speedup $S$ gained from parallelization, which is the ratio of the serial execution time $T_{serial}$ to the parallel execution time $T_{parallel}$ :

$S = \frac{T_{serial}}{T_{parallel}}$

Ideally, for 'G' GPUs, a linear speedup is expected:

$S_{ideal} = G$

However, due to overheads, the actual speedup $S_{actual}$ is often less than the ideal speedup. The efficiency $E$ of the parallelization can be calculated as:

$E = \frac{S_{actual}}{G}$

D. Performance Evaluation Metrics​

To evaluate the performance of the parallelization and memory management strategies, we consider the following metrics:

1. Throughput: Measured in inferences per second, it quantifies the number of inference tasks processed in a given time frame.

2. Latency: The time taken to complete a single inference task from input to output.

3. Memory Efficiency: The ratio of useful work to total memory usage, reflecting how effectively memory resources are utilized.

4. Speedup: The factor by which the parallel execution is faster than the serial execution.

5. Efficiency: The average speedup per GPU, indicating how well the parallelization strategy utilizes available resources.

By analyzing these metrics, we can draw conclusions on the effectiveness of our parallelization and memory management techniques, providing a theoretical foundation for their practical implementation and optimization on mid-range GPUs.

VI. Conclusion​

In this article, we have explored the theoretical foundations and practical implications of parallelization techniques and memory management strategies for deploying Large Language Models (LLMs) on mid-range GPUs. The goal has been to enhance LLM inference capabilities without requiring high-end, specialized hardware.

A. Summary of Key Findings​

1. Model Parallelism allows us to distribute the layers of an LLM across multiple GPUs, which can potentially increase throughput and reduce latency, provided that the communication overhead is minimized.

2. Pipeline Parallelism enables the concurrent processing of multiple instances or stages of an LLM, which can lead to higher throughput. However, it requires careful scheduling to ensure that no stage becomes a bottleneck.

3. Tensor Parallelism involves partitioning the input tensors across GPUs, which can reduce the memory footprint of each GPU and potentially speed up computation.

4. Dynamic Memory Allocation and Paged Memory Management are strategies that help to optimize memory usage for variable-length input sequences, reducing memory waste and improving efficiency.

5. Copy-on-Write Mechanism and Swapping and Recomputation are techniques that help manage memory evictions efficiently, allowing for better memory utilization and performance.

B. Prospects for LLM Inference on Mid-Range GPUs​

The strategies discussed in this article open up possibilities for LLM deployment on a wider range of hardware, as demonstrated by the successful implementation on StarLand. As LLMs continue to grow in size and complexity, the need for efficient inference on mid-range GPUs becomes increasingly important. The theoretical analysis provided here serves as a roadmap for future research and development in our StarLand.

C. Implications for Mid-Range GPU Deployment​

The findings of this article have implications for developers and organizations looking to deploy LLMs in resource-constrained environments. By understanding the trade-offs and leveraging the strategies outlined, it is possible to achieve high-performance LLM inference on mid-range GPUs,a goal that StarLand aims to accomplish.

D. Future Directions​

Looking ahead, there are several promising directions for future work:

1. Algorithm Optimization: Further optimization of parallelization algorithms to better handle the unique challenges of LLMs.

2. Hardware-Software Co-Design: Designing GPU hardware with features that are tailored to the needs of LLM inference.

3. Adaptive Strategies: Developing adaptive parallelization and memory management techniques that can respond to changing inference workloads in real-time.

4. Energy Efficiency: Exploring methods to reduce the energy consumption of LLM inference on mid-range GPUs, which is important for sustainability.

5. Open-Source Implementations: Encouraging the development of open-source frameworks that implement these strategies to facilitate wider adoption.

By pursuing these directions, we can continue to push the boundaries of what is possible with LLM inference on mid-range GPUs, making advanced natural language processing capabilities more accessible to a broader range of users and applications.

Appendix:

A. Proofs for Parallelization Strategies​

This appendix provides a detailed mathematical analysis of the parallelization strategies discussed in the main text. We will delve into the theoretical underpinnings of Model Parallelism, Pipeline Parallelism, and Tensor Parallelism, providing proofs for their efficacy under certain conditions.

Model Parallelism​

Model parallelism involves executing different parts of a model on separate GPUs. The goal is to balance the computational load and minimize inter-GPU communication.

Proof of Load Balance: Let $L$ be the total number of layers in an LLM, and $G$ be the number of GPUs available. When using model parallelism, the layers are distributed such that each GPU $g$ gets $\frac{L}{G}$ layers. The load balance can be mathematically expressed as:

$\left| \sum_{i \in GPU_g} C_i - \frac{1}{G} \sum_{i=1}^{L} C_i \right| \leq \epsilon$

Where $C_i$ is the computational complexity of layer $i$, and $\epsilon$ is a small constant representing the allowable imbalance.

Pipeline Parallelism​

Pipeline parallelism processes multiple instances of the model simultaneously, with each instance going through different stages of the pipeline.

Proof of Increased Throughput: Consider $P$ parallel instances of an LLM, each with $S$ stages. The throughput $T$ is given by:

$T = \frac{P \times S}{\text{Total time per instance}}$

Assuming that the stages are perfectly balanced, the total time per instance is the time of the longest stage. If we denote the time taken by the longest stage as $s_{max}$, the throughput can be simplified to:

$T = \frac{P \times S}{s_{max}}$

This shows that the throughput is directly proportional to the number of parallel instances and stages.

Tensor Parallelism​

Tensor parallelism involves splitting the input tensors across multiple GPUs, reducing the memory footprint on each GPU.

Proof of Memory Reduction: Let $T$ be a tensor of size $D \times N$ that needs to be processed by an LLM. When split across $G$ GPUs using tensor parallelism, each GPU processes a sub-tensor $T_g$ of size $\frac{D}{G} \times N$. The total memory required before and after splitting is:

$\text{Memory}_{\text{before}} = D \times N$

$\text{Memory}_{\text{after}} = G \times \left( \frac{D}{G} \times N \right) = D \times N$

Despite the total memory remaining the same, the memory footprint on each individual GPU is reduced, which can be critical when dealing with memory constraints.

Analysis of Communication Overhead​

In all parallelization strategies, communication overhead is a critical factor that can affect the overall performance.

Proof of Communication Overhead in Model Parallelism: Let $C_{comm}$ be the communication overhead per layer when using model parallelism. The total communication overhead $O_{comm}$ for a model with $L$ layers is:

$O_{comm} = L \times C_{comm}$

This overhead must be minimized for efficient parallel execution. Techniques such as gradient aggregation, where gradients from different GPUs are combined before communication, can help reduce this overhead.

Conclusion​

The proofs provided in this appendix serve to illustrate the theoretical basis for the parallelization strategies discussed. They highlight the importance of balancing computational load, minimizing communication overhead, and effectively managing memory in the deployment of LLMs on mid-range GPUs. These principles are fundamental in the design of efficient and scalable LLM inference systems.

B. Memory Management Algorithms​

This appendix outlines the algorithms and data structures used for memory management in the context of LLM inference on mid-range GPUs. We focus on the key techniques discussed in the main text: dynamic memory allocation, paged memory management, and the copy-on-write mechanism.

Dynamic Memory Allocation Algorithm​

Dynamic memory allocation is crucial for handling variable-length sequences in LLMs. The algorithm allocates memory based on the actual sequence length rather than a fixed maximum size.

Algorithm: DynamicMemoryAllocation
Input: SequenceLength L, MaximumMemoryBlock B, MemoryAllocator A
Output: AllocatedMemory M

1. M ← A.Allocate(Min(L * MemoryPerToken, B))
2. if M is NULL then
3.     M ← A.Allocate(B) // Attempt to allocate maximum block if minimum fails
4.     if M is NULL then
5.         A.FreeAll() // Free all memory and retry allocation
6.         M ← A.Allocate(Min(L * MemoryPerToken, B))
7. return M

Paged Memory Management​

Paged memory management involves dividing the memory into fixed-size pages and managing these pages to optimize usage.

Algorithm: PagedMemoryManagement
Input: MemoryRequest R, PageTable T, PageSize S
Output: MemoryBlock B

1. B ← T.Lookup(R)
2. if B is NULL then
3.     B ← AllocateNewPage(S)
4.     T.Insert(R, B)
5. return B

Function AllocateNewPage(PageSize S)
1. if NoFreePagesAvailable() then
2.     CoalesceFreePages() // Merge adjacent free pages
3.    if NoFreePagesAvailable() then
4.        return NULL // No free pages available
5. page ← GetFreePage(S)
6. return page

Copy-on-Write Mechanism​

The copy-on-write (COW) mechanism defers the duplication of memory until a write operation occurs.

Algorithm: CopyOnWrite
Input: MemoryBlock B to be modified, ReferenceCount C for B
Output: Modified MemoryBlock B'

1. if C > 1 then // Check if B is shared
2.     B' ← A.Allocate(SameSizeAs(B))
3.     Copy(B, B') // Duplicate the contents of B to B'
4.     C ← C - 1 // Decrement the reference count of the old block
6. return B' // Return the new block B'

Swapping Mechanism​

Swapping involves moving data between the GPU memory and a slower, auxiliary memory to free up space in the GPU memory.

Algorithm: SwappingMechanism
Input: MemoryBlock B to be swapped out, AuxiliaryMemory M
Output: SwappedOut MemoryBlock B

1. if GPUMemoryFull() then
2.     B ← SelectVictimBlock() // Choose a block to swap out
3.     Write(B, M) // Write the contents of B to auxiliary memory M
4.     GPU.Free(B) // Free the GPU memory occupied by B
5. return B

Recomputation Mechanism​

Recomputation is an alternative to swapping where data is recalculated instead of being stored in memory.

Algorithm: RecomputationMechanism
Input: ComputationDependencies D, RecomputationFunction R
Output: Recomputed Data B

1. if GPUMemoryFull() then
2.     foreach Dependency ∈ D do
3.         if NotInGPUMemory(Dependency) then
4.             Dependency ← R(Dependency) // Recompute the dependency
5. return R(B) // Recompute the data B using its dependencies

These algorithms are central to the efficient management of memory resources during LLM inference on mid-range GPUs. They provide a foundation for the development of more sophisticated memory management systems tailored to the needs of LLMs.

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.

Prompt engineering involves exploring methods to enhance the effectiveness and precision of outputs produced by large language models (LLMs). Some techniques, such as chain-of-thought prompting, have empowered prompt engineers to refine the quality of their outputs significantly. In this discussion, we look at an additional technique known as ReAct prompting, which aids in guiding LLMs towards achieving the desired output more effectively and deepens their comprehension of the given prompt instructions.

What Is ReAct Prompting?​

ReAct is a method for prompting and processing responses in large language models (LLMs) that combines reasoning, action planning, and the assimilation of various knowledge sources. This approach encourages LLMs to extend beyond their intrinsic capabilities, utilizing real-world information to inform their predictions. Essentially, ReAct combines the processes of thinking and executing actions.

Why did StarLandAI choose ReAct Prompting?​

On StarLandAI, we empower users to configure and create custom Avatars by engaging in dialogue with our official AI Agent. In this process, the Agent’s ultimate goal is to assist users in completing the creation and configuration of their Avatars. To achieve this goal, a variety of sub-steps are required, such as obtaining the Avatars’ basic descriptions from users, configuring the Avatars’ voices, generating the Avatars’ visual appearance and so on. ReAct’s approach to reasoning and action planning is a natural fit for our needs. Through reasoning, the Agent can contemplate what steps remain to complete the configuration of the Avatars. It then uses action planning to devise a plan for the next step. Upon completion of an action associated with a step, the reasoning process repeats until the configuration of the Avatars is finalized.

How does StarLandAI utilize ReAct?​

StarLandAI implements ReAct prompting for the workflow of the configuration of the Avatars. It contains reasoning, decision making, action planning, and observation.

The prompt of ReAct should contain four key elements:

• Main instruction: Main instruction is important. It’s goals is to initiate the model’s comprehension of our desired outcomes.
• ReAct steps: Outline the steps for reasoning and action planning. We use “thought, action, and observation” as the steps in our prompt.
• Reasoning: A chain-of-thought like “Let’s think about this step by step” is used to enable reasoning. Some examples of how to tie the reason to actions are also added.
• Actions: The set of actions from which the model can choose one after reasoning.

Therefore, our Main instruction is to assist users in completing the configuration of their Avatars, and we have incorporated all the necessary information and steps for configuring Avatars into our prompt. Within these steps, the required actions to be invoked, including: asking users questions, summarizing and extracting Avatar configuration information, automatically optimizing Avatar configurations, acquiring voices, generating Avatar images, etc., are also integrated into our prompt.

ReAct prompting not only organizes the conversation but also maintains a high level of engagement and interactivity with the user. The feedback loop created by ReAct prompting allows the AI Agent to continuously learn from each interaction, refining its approach to better suit the user’s requirements. This interactivity is especially crucial as it helps in creating a more personalized Avatar that truly represents the user’s preferences.

The Future of ReAct Prompting on StarLandAI​

The future of ReAct prompting on StarLandAI looks promising. By consistently applying this technique, StarLandAI will continue to improve the user experience, giving rise to a more intuitive and user-friendly platform for Avatar customization.

Ultimately, the conclusion of our journey in Avatar creation is not merely a technological accomplishment but a testament to the seamless partnership between human imagination and AI assistance. StarLandAI aims to lead this paradigm shift, creating a future where every user can see a reflection of their unique identity in their digital counterpart, thanks to the innovative power of ReAct prompting.

Large language models can produce text; however, they might not always follow directions correctly when a specific output format is required. In the process of creating characters at StarLandAI, it is necessary to extract key character attributes from multiple messages input by the user to generate structured character attribute information. The ability to accurately generate outputs that meet the required format influences the user experience in configuring character attributes through StarLandAI. Different strategies for designing prompts have been developed to enhance the consistency and reliability of the text produced, yet these methods do not always prove adequate. So, how to control the LLM output format?

StarLandAI uses lm-format-enforcer [1] to solve the issues. By restricting the selection of tokens the language model can produce at each step, lm-format-enforcer ensures compliance with the desired output format, while simultaneously reducing constraints on the language model’s capabilities.

1. How does it work?​

lm-format-enforcer works by integrating a character-level parser with a tokenizer prefix tree to create an intelligent token filtration system.

(1) Character Level Parser​

Interpreting a string into any given format could be perceived as a tacit tree structure — during any point of the parsing sequence, there exists a specific selection of permissible subsequent characters, which, upon being chosen, lead to a subsequent range of allowable characters, and this pattern continues.

(2) Tokenizer Prefix Tree​

If we have a tokenizer from a particular language model, we are able to construct a prefix tree with all potential tokens the model might produce by creating all conceivable token sequences and incorporating them into the tree.

(3) Combining the two​

With a character-level parser and a tokenizer prefix tree in hand, we can adeptly and effectively sift through the permissible tokens for the language model to produce in the next step: We navigate only through those characters that are concurrently present in the node of the character-level parser as well as the node of the tokenizer prefix tree. This allows us to find all of the tokens. This process is repeated recursively on both trees, culminating in the retrieval of all permissible tokens. As the language model outputs a token, we update the character-level parser with the newly generated characters, preparing it to refine the options for the upcoming timestep.

2. Achieved effect​

By applying this technique, StarLandAI is able to enforce the generation of specific enumerated values by the LLM. For example, when categorizing user-created characters and generating recommended character tags through the LLM, StarLandAI utilizes regular expressions to describe the list of tags that the LLM can generate. Then, it converts the regular expression into a character-level parser. This parser is applied to process the LLM’s output logits, filtering out any subsequent tokens that violate the regular expression. Through this method, the LLM selects the next token with the highest probability from those that conform to the regular expression.

3. Feature in StarLandAI​

On StarLandAI, configuring a custom character doesn’t require users to fill out extensive and complex forms, nor is there a need to understand the significance behind each field. Users simply need to have a casual chat with the Starland assistant, informing Starland of the character they wish to create. Starland itself handles the extraction and completion of the form attributes.

Yes, Starland utilizes the lm-format-enforcer in the feature that allows users to configure custom roles. Through conversation with users, Starland understands the type of character a user wishes to create. It then leverages LLM to offer inspiring suggestions and assistance for the user’s configured character. Finally, it summarizes the entire conversation history to generate a structured custom role configuration. Just like that, a custom character is configured.

References​

[1] https://github.com/noamgat/lm-format-enforcer

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.

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.

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.

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.

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.

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.

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 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.

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.

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;

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.

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.