As AI systems become more capable, researchers are increasingly asking a practical question: Can AI do more than help write code for computing systems? A new demonstration in AI-driven chip design suggests it can, and on a budget. ✅ Using a team of AI agents, researchers optimized hardware for quantum error correction, at a cost 11.5x cheaper than the baseline design. The results show how low-cost open models can help AI move beyond coding assistance and into hardware design itself. Here's how: https://ibm.co/6041EVO07
IBM Research
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Inventing what's next in science and technology.
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IBM Research is a group of researchers, scientists, technologists, designers, and thinkers inventing what’s next in computing. We’re relentlessly curious about all the ways that computing can change the world. We’re obsessed with advancing the state of the art in AI and hybrid cloud, and quantum computing. We’re discovering the new materials for the next generation of computer chips; we’re building bias-free AI that can take the burden out of business decisions; we’re designing a hybrid-cloud platform that essentially operates as the world’s computer. We’re moving quantum computing from a theoretical concept to machines that will redefine industries. The problems the world is facing today require us to work faster than ever before. We want to catalyze scientific progress by scaling the technologies we’re working on and deploying them with partners across every industry and field of study. Our goal is to be the engine of change for IBM, our partners, and the world at large.
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IBM Research reposted this
Scaling semiconductors has historically meant building across two dimensions. IBM's nanostack scales upward. Named to TIME's Best Inventions of 2026, this 3D chip architecture unlocks nearly twice the transistor density of 2nm node technology. Dive deeper into IBM nanostack and see how it provides a path for future chips that deliver dramatically better performance: https://ibm.co/6043EVE2R
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IBM Research reposted this
What does it take to support new AI hardware within PyTorch? In our latest blog, contributors from IBM share how they extended PyTorch Inductor for the IBM Spyre Accelerator, leveraging PyTorch's compiler to address challenges in data movement, memory management, and work distribution while optimizing for Spyre's unique architecture. Read on for a look at how PyTorch's extensibility helps developers bring support for new hardware to the framework: https://lnkd.in/e2xSBfSb Dave Grove, Olivier Tardieu
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As AI workloads become more complex, efficiently serving models is becoming just as important as building them. For that reason, distributed inference is emerging as a reliable and affordable framework for the agentic AI era. This explainer highlights the ideas behind distributed inference and how llm-d brings together technologies designed to help orchestrate diverse AI workloads across clusters, accelerators, and clouds. Explore the full story: https://ibm.co/6046EV8zi
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IBM 🤝 Siemens EDA (Siemens Digital Industries Software) Nanostack is designed to extend angstrom-era scaling for years to come. Together, IBM and Siemens are advancing the design, simulation, and lithography techniques needed for future generations of the architecture. Explore what's next for nanostack through this collaboration: https://ibm.biz/~hLBeqCGam
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Semiconductor design is preparing for its next major architectural shift. To help the industry explore what comes next, IBM plans to make an early research and development process design kit (PDK) available in 2027 for nanostack technology. The kit will give chip designers, EDA vendors, equipment suppliers, and researchers an early opportunity to evaluate this emerging 3D transistor architecture and help shape its design ecosystem. Learn more: https://ibm.co/6045EphMS
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IBM Research reposted this
Get more out of your AI infrastructure. Carlos Costa, Distinguished Engineer at IBM Research and co-founder of the llm-d project will share some exciting news about llm-d: how the project changed the way the open source AI community scales inference at PyTorch Conference North America. A little over a year after the project was founded, llm-d is reaching a new level of maturity, delivering state-of-the-art performance for generative serving, while also enabling the capabilities needed for day two operations, including multi-tenant quality of service, zero downtime operations, and all of that without sacrificing performance in production. Join us in San Jose, October 20-21: https://hubs.la/Q04v4SL60
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Quantum computing is getting closer to fault tolerance. ⚡ This week on Circuit Breaker, we're looking at two important steps along that path: IBM's progression into the testing phase of DARPA's Quantum Benchmark Initiative and how Qiskit is expanding beyond Python to make quantum development more accessible. Read the latest edition. ↓
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IBM Research reposted this
PyTorch Conference is where the open source Al community gathers to talk about what we are all building together. Priya Nagpurkar, VP of AI Native Systems at IBM Research will give a keynote at #PyTorchCon North America on IBM and Red Hat's contributions to the PyTorch ecosystem, including exciting developments on infrastructure for out-of-tree accelerators, enablement of IBM's Spyre Accelerator, and work with Ilm-d for distributed inference. Come to the talk, meet the community, and hear what's next in open source Al. Join us in San Jose, October 20-21: https://hubs.la/Q04v4SL60
