Category: AI Chips

  • Murakkab: New System Cuts AI Agent Energy Use and Cost by Over 70%

    Murakkab: New System Cuts AI Agent Energy Use and Cost by Over 70%

    Agentic workflows — AI-powered software systems that chain multiple models and tools to complete complex tasks — are becoming the backbone of cloud computing. But their fragmented design often wastes computation, energy, and money. Researchers from MIT and Microsoft have developed a new system called Murakkab that streamlines the design and deployment of these workflows, automatically optimizing them for speed, energy efficiency, and cost.

    With Murakkab, developers describe their application’s goal in plain language, and the system automatically selects the best AI models, tools, hardware configurations, and resource allocations. It adjusts these on the fly based on user priorities, such as minimizing costs or maximizing speed. In tests, Murakkab used only about 35% of the computation, 27% of the energy, and under 25% of the cost compared to traditional methods — without sacrificing performance.

    “Agentic workflows are getting very complicated and quickly becoming the backbone of what cloud providers are doing,” says Gohar Chaudhry, an MIT EECS graduate student and lead author of the paper presented at USENIX OSDI. “Energy usage is a huge concern, so we need to be very careful about how efficient these workflows are.”

    Murakkab also adapts dynamically when new models or hardware become available, eliminating the need for developers to manually reconfigure their systems. The researchers plan to expand the system to more complex workflows and larger computing clusters.

  • Trump’s Beijing Visit Opens Door for Nvidia Chips to Power Chinese AI Giants

    Trump’s Beijing Visit Opens Door for Nvidia Chips to Power Chinese AI Giants

    President Donald Trump’s recent trip to Beijing, accompanied by a delegation including Apple’s Tim Cook, Tesla’s Elon Musk, and Nvidia’s Jensen Huang, signals a potential reset in AI supply chains between the U.S. and China. The visit, focused on high-level negotiations, suggests a shift toward a transactional relationship where American semiconductor technology may support Chinese AI development.

    At the state banquet, President Xi Jinping emphasized the possibility of common cause between China’s rejuvenation goals and America’s “Make America Great Again” agenda. Behind the diplomatic smiles, however, lies a strategic contest: Beijing’s “new productive forces” policy prioritizes AI, advanced manufacturing, and robotics, exemplified by the transformation of Chongqing into a high-tech megacity. Yet China remains dependent on U.S.-controlled high-end accelerators for training frontier AI models.

    Nvidia’s Jensen Huang’s presence is particularly significant. After years of tightening export controls, Washington is now considering case-by-case reviews for advanced AI compute exports. Nvidia is positioned to ship H200 data center GPUs to major Chinese cloud platforms like Alibaba and Tencent. Although not the top-tier Blackwell-class chips, the H200 is roughly six times more powerful than any domestic Chinese alternative, potentially compressing AI training timelines for Chinese firms.

    For Apple and Tesla, the mission focuses on supply chain stability and regulatory clarity. Apple aims to protect its manufacturing resilience and consumer base, where the iPhone 17 has seen success. Tesla views China as crucial for production and full self-driving deployment, seeking clarity on mapping and data policies to compete with domestic rivals.

    This delegation represents an attempt to reverse the 20% decline in U.S. imports from China. By leveraging tech leaders for targeted access—compute in exchange for market openness and IP protections—both nations may enter a more transactional era. The true metrics of success will be the speed of Chinese hyperscalers building H200 clusters and the regulatory wins secured by Apple and Tesla. Ultimately, compute access shapes capability, and this visit suggests a hard-nosed accommodation that keeps the AI flywheel spinning on both sides of the Pacific.

  • IBM’s Sub-1nm Chip Breakthrough: Nanostack Architecture Ushers in New Era for AI Computing

    IBM’s Sub-1nm Chip Breakthrough: Nanostack Architecture Ushers in New Era for AI Computing

    IBM has unveiled the world’s first sub-1nm chip technology, introducing a new three-dimensional Nanostack architecture designed for next-generation AI computing. The breakthrough promises higher performance, lower power consumption, and could reshape the future of semiconductor innovation across cloud computing, electronics, and advanced AI applications.

    Built on a 0.7nm (7 angstroms) process, the prototype vertically stacks transistors rather than relying solely on shrinking them. IBM said the technology is designed to support increasingly demanding workloads in artificial intelligence, cloud computing, and high-performance computing. As transistors approach atomic dimensions, continuing that trend through conventional scaling has become increasingly difficult because of power, heat, and manufacturing constraints.

    “The next frontier of semiconductor innovation isn’t just about making things smaller, it’s about rethinking how chips are built from the ground up,” IBM said in the announcement. The company noted that the technology marks the beginning of semiconductor development where transistor dimensions are measured in angstroms rather than nanometres. One angstrom is one-tenth of a nanometre, making the new technology a 7-angstrom node. To illustrate the scale, IBM noted that a human red blood cell is about 7,000 nanometres wide, roughly 10,000 times larger than the chip’s 0.7nm transistor node.

    IBM said the new chip packs nearly 100 billion transistors into an area roughly the size of a fingernail, almost twice the transistor density of its 2nm technology introduced in 2021. According to the company, the sub-1nm design can deliver up to 50% higher performance at the same power level, or up to 70% lower power consumption while maintaining the same performance, compared with its 2nm technology.

    “The era of simple scaling is over,” IBM said, adding that “future breakthroughs will come from integrating materials, devices, and architectures in entirely new ways.” IBM claimed that the technology can find applications in artificial intelligence, cloud computing, edge computing, cell phones, and other future electronics, where better performance with lower power consumption becomes increasingly crucial.

    IBM has led the world in developing the chips that power computing systems for decades, from early semiconductors in the 1960s to the world’s first 2nm node chip. The company also recently announced a plan to form Anderon, the world’s first pure-play quantum foundry.

    “IBM’s latest chip breakthrough marks a landmark moment in computing, pushing technology beyond the nanometer era to the scale of atoms. With our new nanostack architecture, we’re not just making smaller transistors; we’re reinventing how chips are built to deliver dramatically more power and energy efficiency,” said Jay Gambetta, Director of IBM Research and IBM Fellow. “This industry-first innovation continues IBM’s legacy of leading in next-generation technologies and sets the foundation for the next era of computing,” he added.

    IBM’s sub-1nm achievement underscores how semiconductor innovations continue to set new benchmarks for technology. With increasing demand for AI applications, semiconductor innovations can drive advancements in computing speed, energy efficiency, and other areas across the medical field, robotics, and beyond.

  • Micron Locks in $22 Billion AI Chip Deals with NVIDIA to Break Memory Market Cycles

    Micron Locks in $22 Billion AI Chip Deals with NVIDIA to Break Memory Market Cycles

    Memory chipmakers are rewriting their playbook. Long treated as interchangeable commodities, memory chips forced suppliers into brutal boom-bust cycles. Now, with AI demand surging, Micron Technology has secured $22 billion in multi-year commitments from customers like NVIDIA — a move designed to stabilize cash flow and shield production from market volatility.

    The agreements follow similar long-term deals by rivals SK Hynix and Samsung. Under these “take-or-pay” contracts, clients must either purchase chips or pay up anyway. Micron’s Chief Business Officer Sumit Sadana told Reuters that billions of dollars have been placed on Micron’s balance sheet as a show of confidence in the new business model.

    While the deals provide visibility and validate the AI demand narrative, analysts warn that the strategy remains a gamble. Memory stocks stay vulnerable to sudden downturns, and any cooling of AI demand could send buyers back to the negotiating table. Micron itself posted a $5.3 billion loss in 2023 when consumer electronics spending collapsed.

    Investors are watching whether Micron’s pricing power can last. The company argues that these long-term contracts push financial risk further into the future and turn chipmakers into strategic partners rather than commodity suppliers. New factory expansions will now require collaborative customer funding, keeping supplies tight until at least 2027.