SigTech CEO Ren on How AI & Finance Can Coexist
Bin Ren, Founder & CEO, SigTech shows investors its flagship GenAI product MAGIC. He then discusses where, alongside humans, he sees AI unlocking new levels of performance and efficiency with Bloomberg's Tim Stenovec at Bloomberg Invest. (Source: Bloomberg)
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Associated Press
7 minutes ago
- Associated Press
KAYTUS Unveils Upgraded MotusAI to Accelerate LLM Deployment
SINGAPORE--(BUSINESS WIRE)--Jun 12, 2025-- KAYTUS, a leading provider of end-to-end AI and liquid cooling solutions, today announced the release of the latest version of its MotusAI AI DevOps Platform at ISC High Performance 2025. The upgraded MotusAI platform delivers significant enhancements in large model inference performance and offers broad compatibility with multiple open-source tools covering the full lifecycle of large models. Engineered for unified and dynamic resource scheduling, it dramatically improves resource utilization and operational efficiency in large-scale AI model development and deployment. This latest release of MotusAI is set to further accelerate AI adoption and fuel business innovation across key sectors such as education, finance, energy, automotive, and manufacturing. This press release features multimedia. View the full release here: MotusAI Dashboard As large AI models become increasingly embedded in real-world applications, enterprises are deploying them at scale, to generate tangible value across a wide range of sectors. Yet, many organizations continue to face critical challenges in AI adoption, including prolonged deployment cycles, stringent stability requirements, fragmented open-source tool management, and low compute resource utilization. To address these pain points, KAYTUS has introduced the latest version of its MotusAI AI DevOps Platform, purpose-built to streamline AI deployment, enhance system stability, and optimize AI infrastructure efficiency for large-scale model operations. Enhanced Inference Performance to Ensure Service Quality Deploying AI inference services is a complex undertaking that involves service deployment, management, and continuous health monitoring. These tasks require stringent standards in model and service governance, performance tuning via acceleration frameworks, and long-term service stability, all of which typically demand substantial investments in manpower, time, and technical expertise. The upgraded MotusAI delivers robust large-model deployment capabilities that bring visibility and performance into perfect alignment. By integrating optimized frameworks such as SGLang and vLLM, MotusAI ensures high-performance, distributed inference services that enterprises can deploy quickly and with confidence. Designed to support large-parameter models, MotusAI leverages intelligent resource and network affinity scheduling to accelerate time-to-launch while maximizing hardware utilization. Its built-in monitoring capabilities span the full stack—from hardware and platforms to pods and services—offering automated fault diagnosis and rapid service recovery. MotusAI also supports dynamic scaling of inference workloads based on real-time usage and resource monitoring, delivering enhanced service stability. Comprehensive Tool Support to Accelerate AI Adoption As AI model technologies evolve rapidly, the supporting ecosystem of development tools continues to grow in complexity. Developers require a streamlined, universal platform to efficiently select, deploy, and operate these tools. The upgraded MotusAI provides extensive support for a wide range of leading open-source tools, enabling enterprise users to configure and manage their model development environments on demand. With built-in tools such as LabelStudio, MotusAI accelerates data annotation and synchronization across diverse categories, improving data processing efficiency and expediting model development cycles. MotusAI also offers an integrated toolchain for the entire AI model lifecycle. This includes LabelStudio and OpenRefine for data annotation and governance, LLaMA-Factory for fine-tuning large models, Dify and Confluence for large model application development, and Stable Diffusion for text-to-image generation. Together, these tools empower users to adopt large models quickly and boost development productivity at scale. Hybrid Training-Inference Scheduling on the Same Node to Maximize Resource Efficiency Efficient utilization of computing resources remains a critical priority for AI startups and small to mid-sized enterprises in the early stages of AI adoption. Traditional AI clusters typically allocate compute nodes separately for training and inference tasks, limiting the flexibility and efficiency of resource scheduling across the two types of workloads. The upgraded MotusAI overcomes traditional limitations by enabling hybrid scheduling of training and inference workloads on a single node, allowing for seamless integration and dynamic orchestration of diverse task types. Equipped with advanced GPU scheduling capabilities, MotusAI supports on-demand resource allocation, empowering users to efficiently manage GPU resources based on workload requirements. MotusAI also features multi-dimensional GPU scheduling, including fine-grained partitioning and support for Multi-Instance GPU (MIG), addressing a wide range of use cases across model development, debugging, and inference. MotusAI's enhanced scheduler significantly outperforms community-based versions, delivering a 5× improvement in task throughput and 5× reduction in latency for large-scale POD deployments. It enables rapid startup and environment readiness for hundreds of PODs while supporting dynamic workload scaling and tidal scheduling for both training and inference. These capabilities empower seamless task orchestration across a wide range of real-world AI scenarios. About KAYTUS KAYTUS is a leading provider of end-to-end AI and liquid cooling solutions, delivering a diverse range of innovative, open, and eco-friendly products for cloud, AI, edge computing, and other emerging applications. With a customer-centric approach, KAYTUS is agile and responsive to user needs through its adaptable business model. Discover more at and follow us on LinkedIn and X. View source version on CONTACT: Media Contacts [email protected] KEYWORD: EUROPE SINGAPORE SOUTHEAST ASIA ASIA PACIFIC INDUSTRY KEYWORD: APPS/APPLICATIONS TECHNOLOGY OTHER TECHNOLOGY SOFTWARE NETWORKS INTERNET HARDWARE DATA MANAGEMENT ARTIFICIAL INTELLIGENCE SOURCE: KAYTUS Copyright Business Wire 2025. PUB: 06/12/2025 07:11 AM/DISC: 06/12/2025 07:10 AM
Bloomberg
12 minutes ago
- Bloomberg
Precious Metals Gains Pushing Several African Currencies Higher
A surge in the price of precious metals is helping several African currencies to rebound from a weak start to the year. The Zambian kwacha is up about 8% so far in June, the best performance globally. It's followed by the Tanzanian shilling, up 4% this month, and not far after by the Nigerian naira, up 3.1%, according to data compiled by Bloomberg.
Forbes
21 minutes ago
- Forbes
What Good Is AI On Blockchain If No One Can Use It Easily In Practice
The interaction of Artificial intelligence (AI) and Blockchain are emerging. (Photo Illustration by ... More Budrul Chukrut/SOPA Images/LightRocket via Getty Images) An increasing number of blockchains is actively seeking to integrate AI capabilities, but this 'AI on blockchain' growth is accompanied by significant challenges, most notably the issue of "chain silos", which fragment the sector and can hold back the full realization and utility of Decentralized AI (DeAI) potential. Afterall, if there's no widely available use case scenario, how do we continue the narrative and innovation of the currently already-overhyped DeAI sector? Take blockchain-native autonomous AI agents as an example, while a precise census of AI agents on the blockchain is elusive, the available data strongly suggests a rapidly growing and dynamic landscape. The number is likely in the hundreds to potentially thousands when considering individual deployed agents across various platforms and projects. All these AI agents reside in a scattered landscape of chains. It's like when computers could not communicate with other computers before the World Wide Web was invented, as a result, the full potential of computers could not be unleashed. While centralized AI suffers from data silos controlled by corporations, DeAI risks creating new silos at the blockchain level if interoperability is not prioritized, blocking DeAI's full potential. This fragmentation is not merely about data residing on different ledgers. It extends to the unique protocols, smart contract languages, virtual machine environments, consensus mechanisms, and overall operational logic of each distinct blockchain. For example, a DeAI application built to leverage the specific features of Ethereum and its EVM may not be able to natively interact with or utilize AI models deployed on a non-EVM chain like Solana without resorting to complex and potentially insecure bridging solutions. Similarly, AI agents trained within one chain's environment may find it difficult to operate effectively elsewhere. This leads to scenarios where separate databases or non-communicating tools on different chains effectively become isolated islands of DeAI activity. Fragmentation issues, similar to those seen in decentralized identity systems or healthcare electronic health records due to platform incompatibilities, can limit the scalability and impact of DeAI solutions. The DeAI community's vision extends beyond isolated applications on single blockchains. Building "Super AI Applications" is becoming a key mission for many. Imagine it as an all-encompassing platform or a network of integrated services that accommodates diverse AI functionalities – such as sophisticated data analysis, distributed model training, autonomous agent deployment, and complex decision making – across different, often varied and disparate blockchain environments. Such an application would not be confined to the resources or limitations of a single chain. On one hand, specialized Layer 1 blockchains like Bittensor, and Gensyn are being engineered from the ground up with DeAI specific requirements in mind. These platforms aim to provide optimized environments for tasks like high-volume data processing, intensive computation, or unique AI model incentive mechanisms, based on the premise that general-purpose L1s may not be ideally suited for the distinct demands of DeAI. On the other hand, many prominent DeAI Apps and protocols, such as Ocean Protocol and SingularityNET, initially launched on established, general-purpose L1s like Ethereum and are now pursuing multichain strategies. Then a key debate arose: Commit to a specialized L1 for potentially superior tailored performance but a smaller initial ecosystem, or build on/across established L1s/L2s to tap into broader reach but with possible limitations in AI specific optimizations? Inevitably, successful DeAI platforms will increasingly rely on reliable and functional cross-chain capabilities to access wider markets, liquidity, and data sources, regardless of their foundational architecture, thereby avoiding the very 'silo-zation' they aim to overcome. Realizing the Super AI APP vision is charged with significant challenges though. Despite these challenges, industry players are proactively exploring solutions and standardization for DeAI Super Applications to cross chain, including leaders like BSC and Solana, although this is still at an infant stage. In the meantime, innovations in protocols, platforms, and conceptual frameworks are also taking shape to construct a more interconnected DeAI ecosystem which can potentially become real utility for even novice internet users. This trend is inevitable, driven by the enormous potential underneath AI and blockchain's synergistic benefits. The inherent characteristics of blockchain can address some of AI's most pressing challenges, while AI can unlock new functionalities and efficiencies for decentralized systems, such as network optimization and intelligent resource allocation, or automated security auditing, and more. For the benefits and advantages of AI on blockchain over centralized AI, I've discussed in my previous articles: How To Solve Data Collection Challenges For Your Business's AI Needs DeepSeek's Lesson: The Future Of AI Is Decentralized And Open-Source Top 5 Decentralized Data Collection Providers In 2025 For AI Business
