Latest news with #GPTOSS
Yahoo
a day ago
- Business
- Yahoo
Microsoft (MSFT) Gets $650 Target as OpenAI Ties Boost Azure's AI Lead
Microsoft Corporation (NASDAQ:MSFT) is one of the . On August 6, DA Davidson analyst Gil Luria reiterated a Buy rating and $650.00 price target on the stock. The rating affirmation follows OpenAI's release of its latest model, GPT-OSS. The firm emphasized on the significance of Microsoft's relationship with OpenAI, which is helping Microsoft drive significant gains. 'Earlier today, OpenAI released their latest model, GPT-OSS, marking the lab's first open-source release since GPT-2 in February 2019. In conjunction with our initial thoughts on both GPT-OSS models, we remind investors about the outsized benefit Microsoft gains in having a tight commercial relationship with OpenAI.' Azure gains, in particular, were highlighted. 'Microsoft's partnership with OpenAI is driving significant gains throughout the business. Particularly with Azure, Microsoft's agreement terms with OpenAI, which grants them right-of-first-refusal on either training or inference, we believe is a notable driver of the recent outperformance by Azure over its hyperscaler peers.' The firm also talked about material acceleration of Azure OpenAI Services in the fourth fiscal quarter of 2025. This acceleration is elevating other AI services and core hyperscaler services on Azure 'both of which are growing faster than comparable product sets on either GCP or AWS.' Microsoft Corporation (NASDAQ:MSFT) provides AI-powered cloud, productivity, and business solutions, focusing on efficiency, security, and AI advancements. While we acknowledge the potential of MSFT as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock. READ NEXT: and . Disclosure: None. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
The Verge
3 days ago
- Business
- The Verge
OpenAI's new GPT-5 models announced early by GitHub
GitHub has accidentally announced OpenAI's new range of GPT-5 models. A now-deleted GitHub blog post reveals that GPT-5, which will be available in four different versions, offers 'major improvements in reasoning, code quality, and user experience.' Reddit posters noticed a new GitHub blog post announcing that GPT-5 is generally available in GitHub models, before the blog post was quickly removed. An archived version reveals that GPT-5 will have 'enhanced agentic capabilities' and can handle 'complex coding tasks with minimal prompting.' GPT-5 will have four model variants, according to GitHub: I revealed last month that OpenAI was preparing to launch GPT-5 in early August, complete with mini and nano versions that will also be available through its API. Earlier this week multiple OpenAI employees started teasing that it would be a big week, and OpenAI CEO Sam Altman even teased GPT-5 over the weekend. OpenAI has all but confirmed it's launching GPT-5 later today, with the announcement of a 'LIVE5TREAM' for 10AM PT / 1PM ET. OpenAI's GPT-5 launch comes in the same week the company also delivered two GPT-OSS open-weight models, with one small enough to run locally on a PC. Posts from this author will be added to your daily email digest and your homepage feed. See All by Tom Warren Posts from this topic will be added to your daily email digest and your homepage feed. See All AI Posts from this topic will be added to your daily email digest and your homepage feed. See All News Posts from this topic will be added to your daily email digest and your homepage feed. See All OpenAI Posts from this topic will be added to your daily email digest and your homepage feed. See All Tech
Geeky Gadgets
4 days ago
- Business
- Geeky Gadgets
OpenAI's New Open Models Overview : GPT-OSS 120B and 20B
What if the power of innovative AI wasn't locked behind proprietary walls but placed directly in the hands of developers, researchers, and innovators? OpenAI's latest release, GPT-OSS 120B and 20B, represents a bold step toward this vision. With their open-weight design and licensing under Apache 2.0, these models aim to bridge the gap between exclusivity and accessibility, offering developers the freedom to customize and deploy advanced AI systems without sacrificing performance. Whether you're running enterprise-grade cloud applications or experimenting on local hardware, these models promise to redefine what's possible in AI-driven development. Sam Witteveen explains the unique capabilities and trade-offs of the GPT-OSS models, from their scalable architecture to their new integration features. You'll discover how these tools empower developers to balance computational efficiency with task complexity, and why their open-weight framework could signal a paradigm shift in the AI landscape. But are they truly the providing widespread access to force they claim to be, or do their limitations—like restricted multilingual support and slower high-reasoning performance—temper their promise? Let's unpack the potential and challenges of these fantastic models, and what they mean for the future of AI innovation. OpenAI GPT-OSS Models Overview Key Features of GPT-OSS Models The GPT-OSS models are available in two configurations, each tailored to meet specific deployment needs: GPT-OSS 120B: This model is optimized for cloud environments and features 117 billion active parameters. It is well-suited for large-scale, enterprise-level applications that require robust computational power and scalability. This model is optimized for cloud environments and features 117 billion active parameters. It is well-suited for large-scale, enterprise-level applications that require robust computational power and scalability. GPT-OSS 20B: Designed for local deployment, this smaller model contains 3.6 billion active parameters and can operate on systems with as little as 16GB of RAM, making it accessible for developers with limited hardware resources. Both models use advanced training techniques, including reinforcement learning, supervised learning, and instruction tuning. These methods enhance their ability to perform complex reasoning and execute tasks effectively. Additionally, the models offer adjustable reasoning levels—low, medium, and high—allowing you to balance computational latency with task performance. For example, high reasoning levels improve accuracy in complex tasks but may result in slower response times, making them ideal for precision-critical applications. Licensing and Accessibility The GPT-OSS models are released under the Apache 2.0 license, granting you broad rights to use, modify, and redistribute them. However, while the models are labeled as 'open-weight,' they are not fully open source. OpenAI has not provided access to the training code or datasets, which limits the ability to reproduce the models independently. This approach reflects OpenAI's effort to enhance accessibility while safeguarding proprietary research and intellectual property. For developers, this licensing model offers significant flexibility. You can integrate the models into your projects, customize them to suit specific requirements, and even redistribute modified versions, all while adhering to the terms of the Apache 2.0 license. OpenAI GPT-OSS 120B & 20B Explained Watch this video on YouTube. Enhance your knowledge on OpenAI GPT Models by exploring a selection of articles and guides on the subject. Capabilities and Applications The GPT-OSS models are designed to support a wide range of advanced functionalities, making them versatile tools for developers. Key features include: Instruction Following: The models excel at following task-specific instructions, allowing you to build applications tailored to unique requirements. The models excel at following task-specific instructions, allowing you to build applications tailored to unique requirements. Tool and API Integration: Seamless integration with tools and APIs allows for enhanced functionality and streamlined workflows. Seamless integration with tools and APIs allows for enhanced functionality and streamlined workflows. Web Search Capabilities: These models can retrieve and process information from the web, expanding their utility in research and data analysis. These models can retrieve and process information from the web, expanding their utility in research and data analysis. Python Code Execution: The ability to execute Python code makes them valuable for automating tasks and performing complex computations. With a context length of up to 128,000 tokens, the models are particularly effective in tasks requiring extensive input processing. This includes document summarization, multi-turn conversations, and complex data analysis. Their architecture incorporates rotary positional embeddings and a mixture-of-experts framework, enhancing their reasoning and generalization capabilities. However, their current support is limited to English, which may restrict their use in multilingual contexts. Performance Insights Benchmark testing reveals that the GPT-OSS models perform competitively in reasoning and function-calling tasks. While they may not fully match the performance of proprietary OpenAI models in every area, they demonstrate strong capabilities in handling complex reasoning challenges. This makes them particularly valuable for applications in research, education, and enterprise solutions. However, there are trade-offs to consider. Higher reasoning levels improve accuracy but can lead to increased response times, which may not be ideal for real-time applications. For time-sensitive tasks, lower reasoning levels may offer a better balance between speed and performance. Understanding these trade-offs is essential for optimizing the models' use in your specific applications. Deployment Options The GPT-OSS models are designed to accommodate diverse deployment scenarios, offering flexibility for developers with varying needs: Local Deployment: The 20B model is optimized for local use and supports 4-bit quantization, allowing it to run efficiently on systems with limited resources. Tools like Triton can further enhance performance on compatible hardware, making it a practical choice for developers working with constrained computational environments. The 20B model is optimized for local use and supports 4-bit quantization, allowing it to run efficiently on systems with limited resources. Tools like Triton can further enhance performance on compatible hardware, making it a practical choice for developers working with constrained computational environments. Cloud Deployment: The 120B model is built for scalability and high performance, making it ideal for enterprise-level applications that demand robust computational power and seamless integration into cloud-based workflows. Both models integrate seamlessly with OpenAI's Harmony SDK and OpenRouter API, simplifying the process of incorporating them into existing systems. This ease of integration allows you to focus on building innovative applications without being bogged down by complex deployment challenges. Limitations to Consider Despite their strengths, the GPT-OSS models have several limitations that you should be aware of: Knowledge Cutoff: The models' training data only extends to mid-2024, which means they lack awareness of developments and events that have occurred since then. The models' training data only extends to mid-2024, which means they lack awareness of developments and events that have occurred since then. Language Support: Currently, the models support only English, which may limit their applicability in multilingual environments or for users requiring support for other languages. Currently, the models support only English, which may limit their applicability in multilingual environments or for users requiring support for other languages. Latency: Higher reasoning levels can result in slower response times, which may impact their suitability for time-sensitive applications. These limitations underscore the importance of carefully evaluating your specific use case to determine whether the GPT-OSS models align with your requirements. By understanding their capabilities and constraints, you can make informed decisions about how to best use these tools in your projects. Implications for the AI Community The release of GPT-OSS 120B and 20B marks a significant milestone in OpenAI's efforts to balance proprietary advancements with open contributions. By making these models accessible under an open-weight framework, OpenAI fosters innovation and competition within the AI community. For developers like you, this represents an opportunity to use innovative AI technologies while retaining control over deployment and customization. As other organizations consider adopting similar approaches, the release of these models could signal a broader shift toward more accessible AI development. Whether you are building applications for research, business, or personal use, the GPT-OSS models provide a powerful foundation to explore new possibilities in artificial intelligence. Media Credit: Sam Witteveen 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
4 days ago
- Geeky Gadgets
Easily Run OpenAI's New GPT-OSS AI Models Locally
What if you could harness the power of innovative artificial intelligence directly on your own computer—no cloud, no delays, and complete control? With OpenAI's release of GPT-OSS 12B and 20B, this vision is no longer a distant dream but a tangible reality. These open source models are designed to bring advanced reasoning capabilities to your fingertips, offering a level of accessibility and transparency that's rarely seen in the AI world. Whether you're a researcher seeking to push boundaries or a tech enthusiast eager to explore, the ability to run these models locally marks a significant shift in how we interact with AI. Imagine the possibilities: faster performance, enhanced privacy, and the freedom to customize your AI experience—all without relying on external servers. In this guide Skill Leap AI explore how OpenAI's new models are reshaping the landscape of artificial intelligence. You'll discover the unique features of GPT-OSS 12B and 20B, the hardware you'll need to run them, and the tools that make installation seamless for users of all skill levels. From the transparency of reasoning steps to the flexibility of open source customization, these models offer a glimpse into the future of AI innovation. But what makes local installation so compelling, and how does it compare to traditional server-based systems? By the end, you'll not only understand why these models are a fantastic option but also feel inspired to take the leap into this new era of accessible AI. Run GPT-OSS Locally OpenAI GPT-OSS 12B and 20B The GPT-OSS 12B and 20B models reflect OpenAI's mission to provide widespread access to AI technology by providing powerful tools for reasoning tasks. These models are designed to help you analyze complex problems and even observe their reasoning steps, offering a transparent look into their decision-making processes. GPT-OSS 20B: This model is designed for local use on high-end consumer-grade hardware, striking a balance between accessibility and performance. It is ideal for users who want advanced AI capabilities without requiring professional-grade systems. This model is designed for local use on high-end consumer-grade hardware, striking a balance between accessibility and performance. It is ideal for users who want advanced AI capabilities without requiring professional-grade systems. GPT-OSS 12B: A more resource-intensive model, this version is tailored for professional-grade workstations with powerful GPUs. It is particularly suited for researchers and advanced users who need higher computational capabilities. Both models are open source, allowing developers and researchers to customize them for specific applications or delve into their underlying architecture. This flexibility makes them valuable tools for a wide range of tasks, from academic research to practical problem-solving. What You'll Need to Run These Models Locally Running these models locally requires robust hardware to ensure optimal performance. Here's what you'll need to get started: For GPT-OSS 20B: A system with at least 16GB of GPU memory and a modern processor. This configuration is suitable for most high-end consumer desktops or laptops. A system with at least 16GB of GPU memory and a modern processor. This configuration is suitable for most high-end consumer desktops or laptops. For GPT-OSS 12B: A more advanced setup with 24GB or more of GPU memory, typically found in professional-grade workstations or specialized computing environments. If your system meets these requirements, you'll experience faster response times and enhanced functionality compared to web-based interactions. However, users with less powerful hardware may encounter challenges running these models effectively, making hardware upgrades a consideration for those seeking to maximize performance. How to Run GPT-OSS Locally On Your Computer Watch this video on YouTube. Stay informed about the latest in OpenAI open source models by exploring our other resources and articles. How to Install: Tools to Simplify the Process To make the installation process straightforward, OpenAI supports two tools: Olama and LM Studio. These tools cater to users with varying levels of technical expertise, making sure that anyone can get started with minimal hassle. Olama: A user-friendly tool with a graphical interface, compatible with Mac, Windows, and Linux. It is designed for users with limited technical expertise, offering a streamlined setup process that requires minimal configuration. A user-friendly tool with a graphical interface, compatible with Mac, Windows, and Linux. It is designed for users with limited technical expertise, offering a streamlined setup process that requires minimal configuration. LM Studio: A more advanced option for users comfortable with terminal commands. This tool provides greater flexibility, allowing you to customize your installation to meet specific needs or preferences. Both tools enable you to run the models locally, making sure optimal performance and reliability. Whether you prioritize simplicity or advanced customization, these tools make it easier to harness the power of GPT-OSS models on your desktop. Why Choose Local Installation Over Web-Based Interaction? While web-based access to GPT-OSS models is available, local installation offers distinct advantages that make it the preferred choice for many users. By running the models directly on your hardware, you can overcome the limitations of server-based systems and enjoy a more seamless experience. Faster Performance: Local installation eliminates server bottlenecks, making sure quicker response times and smoother interactions. Local installation eliminates server bottlenecks, making sure quicker response times and smoother interactions. Greater Control: You can customize features, such as toggling reasoning step visibility, to suit your specific needs. You can customize features, such as toggling reasoning step visibility, to suit your specific needs. Enhanced Reliability: Running the models locally reduces dependency on external servers, making them more dependable for demanding applications. For users who require consistent performance and greater autonomy, local installation provides a superior alternative to web-based solutions. Key Features and Current Limitations The GPT-OSS models are designed to excel in reasoning tasks, offering unique features that set them apart from other AI systems. However, it's important to consider their current limitations to fully understand their capabilities. Reasoning Transparency: These models allow you to view their reasoning steps, providing valuable insights into their decision-making processes. These models allow you to view their reasoning steps, providing valuable insights into their decision-making processes. Open source Flexibility: Developers can modify and adapt the models for specific applications, making them versatile tools for a variety of use cases. Despite these strengths, there are some limitations to be aware of: No support for file uploads, a feature available in some other AI systems like ChatGPT. Web search functionality, accessible through Olama, is still in its early stages and may not meet all user expectations. These limitations highlight areas for future improvement, but they do not detract from the models' overall utility and potential. Looking Ahead: Future Developments OpenAI is actively working on a larger 120B model, which promises to deliver even greater performance and scalability. This upcoming model could rival state-of-the-art systems like DeepSeek, further expanding the possibilities for AI applications. As hardware technology continues to advance, these models will become increasingly accessible, allowing you to use their full potential for a wide range of tasks, from research to creative problem-solving. The development of these models underscores OpenAI's commitment to pushing the boundaries of AI technology while making sure that it remains accessible to individual users. With ongoing updates and improvements, the future of open source AI looks promising, offering exciting opportunities for innovation and exploration. Media Credit: Skill Leap AI 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
4 days ago
- Business
- Geeky Gadgets
OpenAI's Open Weight GPT-OSS 120B and 20B AI Models Fully Tested
What if the future of AI wasn't locked behind proprietary walls but instead placed in the hands of everyone? OpenAI's bold release of GPT-OSS 120B and GPT-OSS 20B, two new open weight models, has sparked a wave of excitement—and debate—across the AI community. With their unparalleled reasoning capabilities and scalability, these models promise to redefine what open source AI can achieve. Yet, their creative limitations and the shadow of leaked competitors like Horizon Alpha raise tough questions about innovation and accessibility. Are these models a true gift to the open source world, or do they fall short of their potential? In this overview, World of AI explore the unique strengths and trade-offs of OpenAI's GPT-OSS models, from their Apache 2.0 license freedoms to their performance in logic-driven tasks. You'll discover how these models stack up against alternatives, why their scalability makes them accessible to a diverse range of users, and where they might leave you wanting more. Whether you're a developer seeking cost-effective solutions or a researcher pushing the boundaries of AI, this deep dive will help you decide if OpenAI's latest release is the right fit for your needs. Sometimes, the most exciting innovations come with a twist—what will you make of this one? OpenAI's GPT-OSS AI Models Key Features and Specifications The GPT-OSS models are tailored to meet a variety of computational requirements, offering distinct advantages based on their scale and design. Below is a detailed comparison of their core features: GPT-OSS 120B: A large-scale model with 120 billion parameters, optimized for high-performance systems such as data centers and enterprise-level applications. A large-scale model with 120 billion parameters, optimized for high-performance systems such as data centers and enterprise-level applications. GPT-OSS 20B: A medium-scale model with 20 billion parameters, designed for broader accessibility, including use on desktops and laptops. A medium-scale model with 20 billion parameters, designed for broader accessibility, including use on desktops and laptops. Reasoning Capabilities: Both models excel in logical reasoning and mathematical problem-solving, using advanced chain-of-thought techniques. Both models excel in logical reasoning and mathematical problem-solving, using advanced chain-of-thought techniques. Context Length: Support for a 128k context length enables efficient handling of extensive text inputs, making them suitable for complex tasks. Support for a 128k context length enables efficient handling of extensive text inputs, making them suitable for complex tasks. Open source License: Distributed under Apache 2.0, allowing unrestricted experimentation, modification, and commercial use. These features make the GPT-OSS models versatile tools for developers and researchers, catering to a wide range of computational needs. Performance, Scalability, and Deployment The GPT-OSS models are designed to accommodate varying hardware capabilities, making sure scalability for users with different resource constraints. GPT-OSS 120B: This model is optimized for high-end systems, offering unparalleled performance for data-intensive tasks. With its 120 billion parameters, it is best suited for environments requiring significant computational power, such as enterprise-level data centers and advanced research facilities. This model is optimized for high-end systems, offering unparalleled performance for data-intensive tasks. With its 120 billion parameters, it is best suited for environments requiring significant computational power, such as enterprise-level data centers and advanced research facilities. GPT-OSS 20B: A more accessible alternative, this model is designed for everyday devices, including desktops and laptops. It provides a cost-effective solution for developers and researchers with limited hardware resources. Both models demonstrate exceptional reasoning capabilities, particularly in logic-driven tasks such as financial analysis, academic research, and mathematical computations. However, their creative outputs, including design and code generation, are inconsistent and often underperform when compared to other open source models. To enhance accessibility, OpenAI offers multiple deployment options: Local deployment for offline environments, making sure data privacy and security. API access for seamless integration into existing workflows and applications. Availability through platforms like Open Router, providing added flexibility for users. These deployment options, combined with the Apache 2.0 license, make the models adaptable to a wide range of use cases. OpenAI GPT-OSS AI Models 120B & 20B Tested Watch this video on YouTube. Take a look at other insightful guides from our broad collection that might capture your interest in OpenAI AI models. Technical Architecture and Cost Efficiency The GPT-OSS models employ OpenAI's proprietary training techniques and a mixture-of-experts architecture, which dynamically activates subsets of parameters to optimize efficiency. This architecture enables the models to balance performance with resource utilization, making them suitable for various applications. GPT-OSS 120B: Features 117 billion total parameters, with 5.1 billion active parameters per token, making sure high performance for complex tasks. Features 117 billion total parameters, with 5.1 billion active parameters per token, making sure high performance for complex tasks. GPT-OSS 20B: Comprises 21 billion total parameters, with 3.6 billion active parameters per token, offering a more resource-efficient alternative. The pricing structure for these models is designed to accommodate a variety of budgets, making them accessible to a broad audience: GPT-OSS 120B: Input tokens are priced at $0.15 per million, while output tokens cost $0.60 per million. Input tokens are priced at $0.15 per million, while output tokens cost $0.60 per million. GPT-OSS 20B: Input tokens are priced at $0.05 per million, with output tokens costing $0.20 per million. This token-based pricing model allows users to scale their usage based on specific requirements, making sure cost efficiency for both small-scale and large-scale applications. Strengths, Limitations, and Ideal Use Cases The GPT-OSS models are particularly well-suited for logic-intensive applications, offering robust performance in areas such as: Financial planning and analysis, where precise calculations and logical reasoning are critical. Academic research and data interpretation, allowing researchers to process and analyze large datasets efficiently. Offline AI applications, providing functionality in environments where internet access is unavailable. Despite their strengths, the models have notable limitations: Creative Performance: Their outputs in creative tasks, such as design and code generation, are inconsistent and often fall short of expectations when compared to other open source alternatives. Their outputs in creative tasks, such as design and code generation, are inconsistent and often fall short of expectations when compared to other open source alternatives. Content Restrictions: Fine-tuned to block malicious or restricted content, the models may lack adaptability in certain scenarios, limiting their flexibility for unrestricted applications. These limitations may affect their suitability for projects requiring high-quality creative outputs or unrestricted adaptability. However, their strengths in reasoning and logic-driven tasks make them valuable tools for developers and researchers. Impact on the Open source AI Community The release of the GPT-OSS 120B and 20B models represents a significant milestone for the open source AI community. By offering these models under the Apache 2.0 license, OpenAI has empowered developers and researchers to innovate without the constraints of proprietary systems. This move has the potential to foster greater collaboration and innovation within the AI ecosystem. However, the reception has been mixed. While many applaud OpenAI's commitment to open source accessibility, others express disappointment over unmet expectations, particularly when comparing these models to leaked alternatives like Horizon Alpha. This highlights the ongoing debate within the AI community regarding the balance between accessibility, performance, and innovation. The GPT-OSS models mark a pivotal step in the evolution of open weight AI, offering developers and researchers a robust foundation for a wide range of applications. Their strengths in reasoning and mathematical tasks, combined with their open source nature, make them valuable assets for advancing AI research and development. Media Credit: WorldofAI Filed Under: AI, Top News 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.
