China Construction Bank says it launched internal financial model based on DeepSeek R1
March 28 - China Construction Bank (601939.SS), opens new tab said on Friday it had completed the private deployment of a large financial model based on DeepSeek R1 earlier this year.
Private deployment means for internal use only. The comments were made by the bank's CEO Zhang Yi at a press conference following annual results.
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Geeky Gadgets
30-05-2025
- Geeky Gadgets
New DeepSeek R1 Coding Performance Tested : Pros, Cons and Real-World Applications
What if artificial intelligence could not only write code but also think through problems like a seasoned developer? Enter DeepSeek R1, the latest breakthrough in AI-driven coding and creativity. Built on the innovative V3 architecture, this model promises to transform how we approach complex programming tasks, offering unparalleled accuracy and adaptability. Yet, even the most advanced technologies come with trade-offs. While DeepSeek R1 excels in generating intricate web applications and dynamic animations, its tendency to overanalyze simple problems raises questions about its efficiency in high-pressure scenarios. Is this the future of coding, or does its brilliance come at a cost? In this in-depth breakdown, Prompt Engineering explore how DeepSeek R1 is redefining the boundaries of AI in coding and beyond. From its remarkable chain of thought reasoning to its ability to craft visually stunning outputs, this model is a fantastic option for developers and creative professionals alike. However, we'll also uncover its limitations, such as its struggles with logical deduction and occasional inefficiencies. Whether you're curious about its competitive edge against models like Gemini 2.5 or eager to understand its potential for creative problem-solving, this analysis will provide a balanced look at what makes DeepSeek R1 both impressive and imperfect. How does it stack up against the challenges of real-world applications? Let's find out. DeepSeek R1 AI Overview Transforming Coding: DeepSeek R1's Unparalleled Performance DeepSeek R1 sets a new standard in coding, showcasing exceptional performance that distinguishes it from earlier models. Whether you're developing interactive web applications, crafting animations, or designing complex algorithms, the model demonstrates outstanding accuracy and efficiency. Its performance in live coding benchmarks rivals leading competitors like Gemini 2.5 and Claude 3.7, cementing its status as a formidable player in the AI landscape. Generates interactive web applications with minimal input, streamlining development workflows. with minimal input, streamlining development workflows. Excels in creative coding , such as futuristic interface design and dynamic animations. , such as futuristic interface design and dynamic animations. Adapts seamlessly to real-time coding scenarios, enhancing productivity. Despite these strengths, the model occasionally takes excessive processing time for straightforward tasks. This inefficiency could pose challenges in time-sensitive applications, highlighting an area for potential refinement. Enhanced Reasoning: Transparency with Room for Growth One of DeepSeek R1's standout features is its advanced chain of thought reasoning. The model provides detailed, step-by-step explanations of its processes, allowing users to follow its logic with ease. This transparency is particularly valuable for debugging and understanding complex outputs, making it a useful tool for developers and analysts alike. Delivers structured reasoning paths that enhance clarity and comprehension. that enhance clarity and comprehension. Maintains raw chain of thought visibility , making sure transparency in decision-making. , making sure transparency in decision-making. Occasionally overanalyzes simple queries, leading to inefficiencies in certain scenarios. While this capability is a major strength, the model's tendency to overthink can slow performance in situations requiring quick, straightforward solutions. Addressing this issue could further optimize its utility in diverse applications. DeepSeek R1 Coding Performance Evaluation Watch this video on YouTube. Here are additional guides from our expansive article library that you may find useful on Deepseek Ai models. Creative Potential: Unlocking New Possibilities Creativity is another domain where DeepSeek R1 excels. The model is capable of generating visually compelling outputs, ranging from animations to themed designs and interactive constellations. These features make it an invaluable asset for creative professionals seeking innovative solutions to complex challenges. Produces intricate, aesthetically pleasing visual outputs that meet professional standards. that meet professional standards. Demonstrates creativity in designing unique applications, interfaces, and artistic projects. in designing unique applications, interfaces, and artistic projects. Supports imaginative problem-solving, making it a versatile tool across industries. This creative versatility positions DeepSeek R1 as a valuable resource in fields such as entertainment, education, and digital design. However, making sure consistency in its creative outputs remains an area for ongoing development. Logical Deduction: Strengths and Challenges DeepSeek R1 showcases robust reasoning capabilities but occasionally struggles with logical deduction. In some cases, it defaults to patterns derived from its training data rather than applying strict logical constraints to solve problems. This limitation underscores an area for improvement in future iterations. Demonstrates inconsistent performance in tasks requiring rigorous logical reasoning. in tasks requiring rigorous logical reasoning. Relies on training data patterns in certain scenarios, which can limit its adaptability. in certain scenarios, which can limit its adaptability. Opportunities for refinement exist to enhance its logical deduction capabilities. Addressing these challenges will be critical for improving the model's reliability and effectiveness, particularly in applications requiring precise logical reasoning. Processing Efficiency and User Interface Advancements Built on the V3 architecture, DeepSeek R1 introduces significant advancements in processing efficiency and user interface (UI) generation. The model supports both reasoning and non-reasoning modes, allowing users to tailor its behavior to their specific needs. However, its tendency to overthink can sometimes offset these efficiency gains. Improved processing efficiency compared to earlier versions, allowing faster task completion. compared to earlier versions, allowing faster task completion. Enhanced UI generation capabilities for seamless and intuitive user experiences. capabilities for seamless and intuitive user experiences. Customizable modes that cater to diverse applications and user preferences. These improvements make DeepSeek R1 a versatile tool for a wide range of users. However, further optimization is necessary to fully address its overthinking tendencies and maximize its potential. Competitive Edge: Benchmarks and Comparisons In coding benchmarks, DeepSeek R1 consistently delivers strong performance, often surpassing models like Gemini 2.5 in specific tasks. Its capabilities are comparable to Claude 3.7 in many scenarios, solidifying its position as a competitive option in the AI landscape. Excels in coding and creative benchmarks , demonstrating superior performance in targeted tasks. , demonstrating superior performance in targeted tasks. Outperforms some competitors in areas such as real-time coding and creative output generation. in areas such as real-time coding and creative output generation. Comparable to leading models in reasoning and problem-solving capabilities. While official metrics from DeepSeek are still pending, early results suggest that R1 is a formidable player in the field. Its ability to compete with and, in some cases, outperform established models highlights its potential as a leading AI solution. Future Prospects: Evolving the DeepSeek Series The future of the DeepSeek series holds significant promise, with speculation suggesting that the upcoming R2 model may introduce a new architecture. This evolution could build on the strengths of V3 while addressing its current limitations. Anticipated updates and features are expected to further enhance the model's capabilities. Potential for a new architecture that improves reasoning and efficiency. that improves reasoning and efficiency. Focus on addressing current challenges , such as overthinking and logical inconsistencies. , such as overthinking and logical inconsistencies. Opportunities for enhanced customization and user control in future iterations. These developments underscore the ongoing innovation within the DeepSeek series and its commitment to advancing the boundaries of artificial intelligence. As the series evolves, it is poised to become an even more powerful tool for professionals across various industries. Media Credit: Prompt Engineering Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Geeky Gadgets
27-05-2025
- Geeky Gadgets
From 2GB to 1TB: How to Maximize AI on Any Local Desktop Setup
What if your local desktop could rival the power of a supercomputer? As AI continues its meteoric rise, the ability to run complex models locally—on setups ranging from modest 2GB systems to innovative machines with a staggering 1TB of memory—is no longer a distant dream. But here's the catch: not all hardware is created equal, and choosing the wrong configuration could leave you stuck with sluggish performance or wasted potential. From lightweight models like Gemma3 to the resource-hungry Deepseek R1, the gap between what your hardware can handle and what your AI ambitions demand is wider than ever. So, how do you navigate this rapidly evolving landscape and make the most of your setup? This comprehensive comparison by Dave, unpacks the hidden trade-offs of running AI locally, from the surprising efficiency of entry-level systems to the jaw-dropping capabilities of high-end configurations. You'll discover how memory, GPUs, and CPUs shape the performance of AI workloads, and why token generation speed could be the metric that transforms your workflow. Whether you're a curious hobbyist or a professional looking to optimize large-scale deployments, this deep dive will help you decode the hardware puzzle and unlock the full potential of local desktop AI. After all, the future of AI isn't just in the cloud—it's sitting right on your desk. Optimizing AI on Desktops Why Run AI Models Locally? Running AI models on local hardware offers several distinct advantages over cloud-based solutions. It provides greater control over data, making sure privacy and security, while also reducing long-term costs associated with cloud subscriptions. Additionally, local deployment eliminates latency issues, allowing faster processing for time-sensitive tasks. However, the success of local AI deployment depends heavily on aligning your hardware's specifications with the demands of the AI models you intend to use. For instance, lightweight models like Gemma3 can operate effectively on systems with minimal resources, making them ideal for basic applications. In contrast, advanced models such as Deepseek R1 require robust setups equipped with substantial memory and processing power to function efficiently. Understanding these requirements is essential for achieving optimal performance. The Role of Memory in AI Performance Memory capacity plays a pivotal role in determining the performance of AI models. Tests conducted on systems ranging from 2GB to 1TB of memory reveal significant trade-offs between cost, speed, and scalability. Here's how different setups compare: 2GB systems: These are suitable for lightweight tasks such as license plate recognition or basic image classification. However, they struggle with larger, more complex models due to limited memory bandwidth. These are suitable for lightweight tasks such as license plate recognition or basic image classification. However, they struggle with larger, more complex models due to limited memory bandwidth. 8GB systems: Capable of handling mid-sized models, these setups offer moderate performance but experience slower token generation speeds, particularly with larger datasets. Capable of handling mid-sized models, these setups offer moderate performance but experience slower token generation speeds, particularly with larger datasets. 128GB and above: High-memory configurations excel at running advanced models, offering faster processing speeds and greater scalability for demanding workloads. One critical metric to consider is token generation speed, which improves significantly with higher memory configurations. Systems with more memory are better equipped to process large datasets and execute complex models, making them indispensable for tasks such as natural language processing, image generation, and predictive analytics. Local Desktop AI Compared : 2GB to 1024GB Watch this video on YouTube. Dive deeper into AI models with other articles and guides we have written below. Hardware Configurations: Matching Systems to Workloads Different hardware configurations cater to varying AI workloads, and selecting the right setup is crucial for achieving efficient performance. Below is a breakdown of how various configurations perform: Low-end systems: Devices like the Jetson Orin Nano (2GB RAM) are limited to lightweight models and basic applications, such as object detection or simple automation tasks. Devices like the Jetson Orin Nano (2GB RAM) are limited to lightweight models and basic applications, such as object detection or simple automation tasks. Mid-range GPUs: Options such as the Tesla P40 (8GB RAM) and RTX 6000 ADA (48GB RAM) strike a balance between cost and performance. These systems can handle larger models with moderate efficiency, making them suitable for small to medium-scale AI projects. Options such as the Tesla P40 (8GB RAM) and RTX 6000 ADA (48GB RAM) strike a balance between cost and performance. These systems can handle larger models with moderate efficiency, making them suitable for small to medium-scale AI projects. High-end systems: Machines like the Apple M2 Mac Pro (128GB RAM) and 512GB Mac M4 are designed for advanced models like Deepseek R1. These setups provide the memory and processing power needed for large-scale AI workloads, including deep learning and complex simulations. CPU-only setups, while less common, can also support massive models when paired with extensive memory. For example, systems equipped with 1TB of RAM can handle computationally intensive tasks, though they may lack the speed and efficiency of GPU-accelerated configurations. This highlights the importance of matching hardware capabilities to the specific computational demands of your AI tasks. AI Models: Size and Complexity Matter The size and complexity of AI models are key factors influencing their hardware requirements. Smaller models, such as Gemma3 with 1 billion parameters, are well-suited for low-memory setups and can perform tasks like text summarization or basic image recognition. These models are ideal for users with limited hardware resources or those seeking cost-effective solutions. In contrast, larger models like Deepseek R1, which scale up to 671 billion parameters, demand high-memory systems and advanced GPUs or CPUs to function efficiently. These models are designed for tasks requiring significant computational power, such as advanced natural language understanding, generative AI, and large-scale data analysis. The disparity in hardware requirements underscores the importance of tailoring your setup to the specific needs of your AI applications. Key Performance Insights Testing AI models across various hardware configurations has revealed several critical insights that can guide your decision-making: Memory capacity: Higher memory directly correlates with improved processing speed and scalability, making it a crucial factor for running complex models. Higher memory directly correlates with improved processing speed and scalability, making it a crucial factor for running complex models. Unified memory architecture: Found in Apple systems, this feature enhances AI workloads by allowing seamless access to shared memory resources, improving overall efficiency. Found in Apple systems, this feature enhances AI workloads by allowing seamless access to shared memory resources, improving overall efficiency. Consumer-grade hardware: While affordable, these systems often struggle with large-scale models due to limitations in memory and processing power, making them less suitable for demanding applications. These findings emphasize the need to carefully evaluate your hardware options based on the size, complexity, and computational demands of your AI tasks. Optimizing Local AI Deployment To achieve efficient and cost-effective AI performance on local desktop hardware, consider the following strategies: Ensure your hardware configuration matches the size and complexity of the AI models you plan to run. This alignment is critical for avoiding performance bottlenecks. Use tools like Olama to simplify the process of downloading, configuring, and running AI models locally. These tools can streamline deployment and reduce setup time. to simplify the process of downloading, configuring, and running AI models locally. These tools can streamline deployment and reduce setup time. Invest in high-memory systems if your workload involves large-scale models or extensive data processing. While the upfront cost may be higher, the long-term benefits in performance and scalability are significant. By following these recommendations, you can maximize the performance of your local AI deployments while staying within budget and making sure efficient resource utilization. Challenges and Future Developments Despite recent advancements, consumer hardware still faces limitations when supporting the largest AI models. Memory constraints, processing speed, and scalability remain significant challenges, particularly for users with budget-friendly setups. However, ongoing developments in GPUs, CPUs, and memory architectures are expected to address these issues, paving the way for more powerful and accessible AI systems. Emerging technologies, such as quantum computing and next-generation GPUs, hold the potential to transform local AI deployment. These advancements promise to deliver unprecedented processing power and efficiency, allowing broader adoption of AI across industries and applications. Media Credit: Dave's Garage Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Reuters
22-05-2025
- Reuters
Former PBOC deputy governor Liu Guiping to head Bank of China, sources say
BEIJING, May 22 (Reuters) - China will appoint financial veteran Liu Guiping as chairman of Bank of China ( opens new tab, the nation's fourth largest state-owned bank, two sources with knowledge of the matter said. Liu, who currently serves as executive vice mayor of Tianjin city, will replace current chairman Ge Haijiao, who will move to become governor of China's northern Shanxi province, said the sources, who declined to be named as they're not authorised to speak to the media. The appointments will be announced as soon as this week, the sources said. The Tianjin municipal government, Shanxi provincial government, and Bank of China did not immediately reply to Reuters' request for comment. Liu, 59, has held several prominent positions in China's financial sector, including deputy governor of the People's Bank of China from November 2020 to April 2022 and president of state lender China Construction Bank ( opens new tab from March 2019 to November 2020. He was executive vice president at sovereign investor China Investment Corp from 2014 to 2016. Before that, Liu spent his career at major state lender Agricultural Bank of China ( opens new tab for more than two decades. Ge will succeed Jin Xiangjun, who was placed under investigation in April by China's anti-graft organ on suspicion of serious violation of law and discipline, according to the website of the Central Commission for Discipline Inspection. Chinese President Xi Jinping has cracked down on corruption involving party members, saying in January that it is the biggest threat to China's Communist Party.
