
NVIDIA has announced significant advancements in AI storage at the Future of Memory and Storage conference. The company is open sourcing its cuFile APIs, allowing GPUs to directly access storage, which reduces data access times to microseconds. This initiative is part of a broader effort, including the Storage-Next initiative, to create a secure and efficient storage ecosystem for AI. NVIDIA's SCADA framework further optimizes data access by enabling GPUs to retrieve only the necessary data, enhancing AI performance. These developments aim to improve the productivity and efficiency of AI infrastructure.
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This release quietly cements llama.cpp as the universal inference runtime by adding default support for ROCm 10.0 and CUDA 13.4 across Linux and Windows. AMD GPU users finally get parity with NVIDIA's latest driver stack without manual configuration, while Apple Silicon KleidiAI builds are temporarily disabled to resolve stability issues. The inclusion of Snapdragon NPU support on Linux signals a serious push into edge AI hardware beyond just x86 and ARM CPUs. It is less about new features and more about ensuring the toolchain keeps pace with the rapidly evolving GPU landscape.
This release quietly closes the hardware gap for local inference by adding default builds for ROCm 10.0 and CUDA 13.4 across Linux and Windows. AMD users finally get parity with NVIDIA in the binary distribution, while CUDA 13 support future-proofs setups on newer drivers. The inclusion of Snapdragon and OpenVINO binaries further broadens the hardware surface area without requiring custom compilation. It is a pragmatic update that makes llama.cpp the most accessible runtime for diverse local AI hardware.
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