
NVIDIA has announced a collaboration with Ineffable Intelligence, an AI lab founded by AlphaGo architect David Silver, to develop infrastructure for large-scale reinforcement learning. The partnership focuses on creating systems that learn continuously from experience, a step beyond traditional AI models. Utilizing NVIDIA's Grace Blackwell and the upcoming Vera Rubin platform, the project aims to build a pipeline that supports the unique demands of reinforcement learning. This effort could enable AI systems to autonomously discover new knowledge, potentially leading to significant advancements in AI capabilities.
Read originalThe 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.
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.