Gainward, a globally recognized pioneer in high-performance graphics hardware, today breaks cover with the new GeForce RTX 3060 Python II OC. Merging factory-tuned clock speeds with a 12 GB frame buffer and a dual-fan layout, the Python II OC delivers elite thermal efficiency and unwavering hardware stability for modern mainstream computing.

A Factory-Overclocked 12 GB Weapon
Gainward provides a massive 12 GB GDDR6 VRAM pool to ensure mainstream hardware never limits your potential. This acts as an essential accelerator for students and novice creators to unlock stable, high-resolution content creation and smooth entry-level local AI pipelines by eliminating critical memory bottlenecks.

At GDC, NVIDIA announced a suite of tools designed to streamline local AI video generation for concept development and storyboarding. These updates specifically target rendering workflows running on NVIDIA RTX GPUs and the DGX Spark AI development desktop. ComfyUI, a popular generative AI tool known for its highly customizable but complex interface, is receiving a major accessibility update. To make the software more approachable for artists unfamiliar with visual programming concepts like node graphs, ComfyUI introduced App View. This new, simple interface allows you to generate content simply by entering a text prompt and adjusting basic parameters. The traditional, complex workspace remains fully available as Node View, and you can toggle seamlessly between the two modes depending on your need for granular control.

Generating high-quality 4K Ultra HD video locally entails balancing three constraints, generation speed, video memory limits, and overall control. Artists typically generate smaller, faster previews before upscaling them, a process that traditionally took minutes for just a 10-second clip. To accelerate this pipeline, NVIDIA released RTX Video Super Resolution as a node from directly within ComfyUI, enabling rapid upscaling to 4K. AI developers can access this upscaling technology through a free Python package available via the PyPI repository. The tool takes advantage of Tensor cores on GeForce RTX GPUs.

Ryzen AI Software 1.7 release introduces several updates aimed at improving model coverage, reducing friction in local development workflows, and delivering more predictable performance on AMD Application Processing Units (NPU + iGPU). This release adds new architecture support, expands context length for LLMs, integrates Stable Diffusion into the unified Ryzen AI installer, and improves BF16 inference latency.

Quantum hardware is advancing rapidly, but until machines reach hundreds or thousands of reliable qubits, high-performance simulation is essential for building, testing, and optimizing real applications. State-of-the-art simulators capable of handling large-scale circuits let developers get ahead of the hardware and be ready when the technology arrives. PennyLane's Lightning on AMD GPUs transforms quantum circuit simulation into a high-performance computing pipeline, enabling researchers and engineers to tackle problem sizes previously unfeasible.

Pennylane's Lightning simulator, running on AMD GPU hardware, has been demonstrated at scale on the Frontier Supercomputer—however, expanding access to quantum simulation requires making this software stack available on a broader range of systems. In this post, we provide an avenue for anyone to try PennyLane on AMD GPUs via the AMD Developer Cloud. We will show how to do it using three methods: Docker, Python wheels, and source code installation, each tailored to different user preferences and needs. By the end, you will be able to run your first Hello World program on AMD hardware and have a development environment to explore more complex PennyLane programs.

Oxmiq Labs Inc., the all-new GPU software and IP startup founded by one of the world's top GPU architects and visionaries, Raja Koduri, emerges from stealth after two years of intensive IP development. Raja has assembled a world-class team of GPU and AI architects with over 500 years of combined experience, hundreds of patents, and a collective track record of generating more than $100B in revenue at prior companies.

The Opportunity:
Modern computing has fundamentally shifted toward multimodal experiences where text, audio, video, images, and 3D environments seamlessly interact, establishing GPU architecture as the cornerstone of this transformation. Unlike fixed-function AI accelerators that handle specific tasks, GPUs provide the general-purpose computational flexibility required for these diverse modalities while maintaining deep integration with mainstream operating systems through standardized APIs and unified memory models. This architectural advantage positions GPUs as the essential compute engine for both current applications and the emerging landscape of multimodal AI, where heterogeneous workloads must be processed in harmony.

AI is rapidly reshaping what's possible on a PC—whether for real-time image generation or voice-controlled workflows. As AI capabilities grow, so does their complexity. Tapping into the power of AI can entail navigating a maze of system settings, software and hardware configurations. Enabling users to explore how on-device AI can simplify and enhance the PC experience, Project G-Assist—an AI assistant that helps tune, control and optimize GeForce RTX systems—is now available as an experimental feature in the NVIDIA app. Developers can try out AI-powered voice and text commands for tasks like monitoring performance, adjusting settings and interacting with supporting peripherals. Users can even summon other AIs powered by GeForce RTX AI PCs.

And it doesn't stop there. For those looking to expand Project G-Assist capabilities in creative ways, the AI supports custom plug-ins. With the new ChatGPT-based G-Assist Plug-In Builder, developers and enthusiasts can create and customize G-Assist's functionality, adding new commands, connecting external tools and building AI workflows tailored to specific needs. With the plug-in builder, users can generate properly formatted code with AI, then integrate the code into G-Assist—enabling quick, AI-assisted functionality that responds to text and voice commands.

Scientists and engineers of all kinds are equipped to solve tough problems a lot faster with NVIDIA CUDA-X libraries powered by NVIDIA GB200 and GH200 superchips. Announced today at the NVIDIA GTC global AI conference, developers can now take advantage of tighter automatic integration and coordination between CPU and GPU resources - enabled by CUDA-X working with these latest superchip architectures - resulting in up to 11x speedups for computational engineering tools and 5x larger calculations compared with using traditional accelerated computing architectures.

This greatly accelerates and improves workflows in engineering simulation, design optimization and more, helping scientists and researchers reach groundbreaking results faster. NVIDIA released CUDA in 2006, opening up a world of applications to the power of accelerated computing. Since then, NVIDIA has built more than 900 domain-specific NVIDIA CUDA-X libraries and AI models, making it easier to adopt accelerated computing and driving incredible scientific breakthroughs. Now, CUDA-X brings accelerated computing to a broad new set of engineering disciplines, including astronomy, particle physics, quantum physics, automotive, aerospace and semiconductor design.

AMD has released the new ROCm 6.3 version which introduces several new features and optimizations, including SGLang integration for accelerated AI inferencing, a re-engineered FlashAttention-2 for optimized AI training and inference, the introduction of multi-node Fast Fourier Transform (FFT), new Fortran compiler, and enhanced computer vision libraries like rocDecode, rocJPEG, and rocAL.

According to AMD, the SGLang, a runtime that is now supported by ROCm 6.3, is purpose-built for optimizing inference on models like LLMs and VLMs on AMD Instinct GPUs, and promises 6x higher throughput and much easier usage thanks to Python-integrated and pre-configured ROCm Docker containers. In addition, the AMD ROCm 6.3 also brings further transformer optimizations with FlashAttention-2, which should bring significant improvements in forward and backward pass compared to FlashAttention-1, a whole new AMD Fortran compiler with direct GPU offloading, backward compatibility, and integration with HIP Kernels and ROCm libraries, a whole new multi-node FFT support in rocFFT, which simplifies multi-node scaling and improved scalability, as well as enhanced computer vision libraries, rocDecode, rocJPEG, and rocAL, for AV1 codec support, GPU-accelerated JPEG decoding, and better audio augmentation.

Gainward is thrilled to announce the arrival of the Python III Series, a revolutionary new graphics card series boasting the mighty GeForce RTX 40 SUPER architecture in a remarkably SFF-ready design. The Python III shatters the stereotype that high-end graphics cards require bulky cases. This design packs the power of the GeForce RTX 4080 SUPER, RTX 4070 Ti SUPER, RTX 4070 SUPER into a compact size, measuring just 294 x 116 x 49.5 mm. This makes Python III the perfect choice for gamers and creators who craft powerful Small Form Factor (SFF) PCs.

Building an SFF PC offers multiple benefits. It not only saves valuable space, but also enhances the overall aesthetic of your setup. The portability of SFF PCs allows you to take your gaming and creative endeavors on the go. Most importantly, you can enjoy all these benefits without compromising performance. The Python III series features a triple-fan cooling system specifically designed for optimal performance within the SFF form factor. This ensures your graphics card remains cool and delivers peak performance even during demanding tasks. With its unmatched performance in a remarkably SFF-ready form factor, the Python III empowers users to build the ultimate compact PC without sacrificing power.

This $39.97 e-learning bundle teaches you how to program in Python, and how to use its web frameworks, libraries, and other tools—less than $4 per course. Interested in becoming a self-taught programmer? You might still benefit from some instruction, and these self-paced e-courses assist you with code examples, security considerations, and real-world projects to help you build websites, apps, and games in Python. It contains 14 courses and 113 hours of content that you can access for life for $39.97 (reg. $196).

Learn Python programming with this $39.97 (reg. $196) e-learning bundle with 14 courses and 113 hours of content. That's less than $4 per course. Beginning April 1st Through April 7th.

Today, Lenovo announced a strategic partnership with Anaconda Inc., the leading provider of the world's most popular artificial intelligence (AI), machine learning (ML) and data science platform, to empower Lenovo's high performance data science workstations. The partnership will couple Lenovo's trusted ThinkStation and ThinkPad workstation product portfolio heritage and leadership with Anaconda's enterprise strengths for open-source leadership, security, and reliability.

The rapidly evolving world of artificial intelligence, deep learning and generative AI is opening up new opportunities for businesses and data scientists. Much of the AI innovation taking place today is driven by open-source software and cloud-based solutions, with Python being a leading software language for AI applications. However, the data security risks associated with utilizing open-source software at an enterprise level, privacy concerns and often prohibitive cost of cloud-based AI solutions, is causing many organizations to rethink their approach to investment in AI development. With Intel -powered Lenovo workstations architected with the latest generations of professional NVIDIA GPUs built for large-language model fine-tuning, and the Anaconda Navigator's ability to enable businesses to leverage open-source and AI with enhanced security, scale, and governance mechanisms in place, the partnership allows data scientists to create and deploy AI solutions with first class hardware and enterprise-grade AI software support within a more manageable investment framework.

Microsoft today announced that Python integration is happening, and it is coming to the latest Excel version. Through a new partnership with Anaconda, Excel will natively support executing Python code and accessing popular Python data science libraries like Pandas, Matplotlib, and Statsmodels. This integration will empower Excel users to leverage the full capabilities of Python for data transformation, analysis, and visualization without switching between separate tools. The Python integration will be available starting today in preview form for Microsoft 365 Insider beta channel members. It is being added as part of Excel's Power Query data preparation feature.

To access it, Insiders must install the latest Excel preview build, then look under the Formulas tab for the new "Insert Python" option. This will allow them to write and execute Python scripts directly in Excel worksheets. The limited-time integration preview will revert to reduced functionality once it expires. At that point, users must purchase a license to regain full access. This reflects Microsoft's strategic move to position Python as an alternative to VBA for programming and automation in Excel. With this new level of Python integration, Excel continues to evolve as a versatile hub for data analysis tasks, reducing the need to switch between separate Python and Excel workflows. Anaconda provides documentation and training resources to help users take full advantage of the new capabilities.Update 10:58 UTC: Just to clarify: only the "Office 365" cloud version receives Python integration—no improvements for the classic offline version.