vLLM has released version 0.29.0, featuring Model Runner V2 as the default for all models, which enhances performance and memory management. This update includes new models such as Hy4-preview and Tencent's MoE, along with improvements in speculative decoding and RL weight synchronization. Performance optimizations for Kimi-K3 and DeepSeek V4 are also part of this release, promising reduced latency. While Model Runner V1 is being deprecated, vLLM plans to address remaining feature gaps in MRV2 shortly, enhancing its utility for developers.
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.