
Meta plans to begin production of its latest AI-specific chips in September, as part of its strategy to reduce GPU costs amid a component shortage. The chips, developed under the Meta Training and Inference Accelerator program, are designed with a modular approach to adapt to changing AI needs. Meta is working with Broadcom on the design and TSMC on manufacturing. This initiative is part of Meta's broader effort to secure computing capacity for its AI models, reflecting a trend among tech giants to develop proprietary chips.
Read originalThe latest b9946 release of llama.cpp focuses on optimizing Hexagon operations, particularly unary operations, to improve performance and efficiency. By introducing tiling for wide rows and replacing divisions with fastdiv, the update aims to prevent VTCM overflow and streamline code execution. The release also includes tracing instrumentation and specialized thread functions to enhance code generation. While no new models are introduced, these technical improvements make llama.cpp more robust and efficient for developers working with Hexagon architectures.
The latest b9948 release of llama.cpp focuses on optimizing memory usage in CUDA operations, specifically in the ggml_top_k() and ggml_argsort() functions. By processing data in smaller chunks, the update reduces the need for large temporary buffers, enhancing performance on CUDA-enabled systems. This release also includes minor code improvements like allocating temporary destinations only once and refining the use of ternary operators. While no new model architectures are introduced, these changes make llama.cpp more efficient for developers working with CUDA, particularly in memory-constrained environments.