llm-d 1.0 hardens open-source AI inference for day 2 production operations
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Peter Hess
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78 results for Explainers
How distributed inference keeps large AI models moving
ExplainerPeter HessQiskit beyond Python: Fortran, C++, and Julia
ExplainerHaimeng Zhang, Jim Garrison, Matthew Treinish, Henry Zou, Aaryav Mishra, Dhinakaran Vinayagamurthy, Shobhit Pandey, Nate Earnest-Noble, and Robert DavisWhy are silicon wafers round?
ExplainerPeter HessA brief history of HRL Laboratories, one of the world’s most storied private research laboratories
ExplainerRyan MandelbaumWhat are spin qubits?
ExplainerIBM QuantumHow to measure the performance of a quantum computer
ExplainerDavid McKay, Scott Crowder, Jerry Chow, Oliver Dial, Robert Davis, and Catherine DundonWhat is a nanostack? Building a chip like a city
ExplainerPeter HessThe fast Fourier transform, how and why it works
ExplainerPeter Hess- Algorithms
- Mathematical Sciences
Why IBM is investing $10 billion into quantum computing
ExplainerIBM QuantumHow to use sample-based quantum diagonalization on IBM hardware
ExplainerPeter HessRIKEN and IBM demonstrate quantum-centric supercomputing at enormous scale
ExplainerRafi LetzterHow IBM Granite became a leader in responsible AI
ExplainerKim MartineauA glimpse at computing’s quantum-centric future
ExplainerRyan Mandelbaum and Jerry ChowCan quantum computers model nature’s most turbulent systems?
ExplainerRyan Mandelbaum- Algorithms
- Mathematical Sciences
- Quantum Algorithms
Scaling beyond our roadmap with networked quantum computers
ExplainerJerry ChowQiskit C API enables new end-to-end quantum + HPC workflows
ExplainerJim Garrison, Hiroshi Horii (堀井 洋), Matthew Treinish, Antonio Córcoles, and Robert Davis
