Models & Labs
NVIDIA Unveils 100% Liquid-Cooled AI Servers
NVIDIA Bloghigh confidence
Why it matters
- →Liquid cooling at higher temperatures reduces energy and water consumption in data centers.
- →The innovation allows for more efficient data center designs, eliminating the need for fans and chillers.
- →It sets a new standard for sustainable AI infrastructure, potentially influencing future data center construction.
NVIDIA has launched the Rubin generation of AI servers, which are the first to achieve 100% liquid cooling. This new infrastructure allows cooling liquids to operate at temperatures up to 45 degrees Celsius, reducing energy and water usage significantly. The design eliminates the need for fans and mechanical chillers, making data centers more efficient and environmentally friendly. This advancement could lead to substantial cost savings and a shift in how data centers are built and operated, particularly in regions with cooler climates.
Read originalMore from NVIDIA Blog
More in Models & Labs
Models & Labsmodels
llama.cpp b10955 release addresses heap corruption
The latest release of llama.cpp, b10955, tackles a critical issue of heap corruption by disabling the ggml-cpu precompiled header and fixing CACHE_LINE_SIZE ambiguity. This update ensures consistent CACHE_LINE_SIZE values across C++ kernels and C work-buffer sizing code, preventing heap-buffer-overflow and subsequent crashes. By restoring the natural include order and removing the std::hardware_destructive_interference_size branch, the update makes the value deterministic and include-order independent. This release is a technical fix that stabilizes the runtime environment for developers using llama.cpp.
llama.cpp Releases
Models & Labsmodels
llama.cpp b10956 release enhances SYCL backend
The latest llama.cpp release, b10956, introduces significant improvements to the SYCL backend, particularly for handling large k values in TOP_K operations. By implementing a radix select method, the update allows for efficient GPU-resident processing, avoiding previous limitations that forced operations to fall back to the CPU. This change enhances performance, especially in scenarios requiring large k values, such as qwen4exp's sparse-attention indexer. The update ensures that operations are more efficient and scalable, providing a notable boost in processing speed without regressing any measured shapes.