Models & Labs
vLLM v0.25.0rc2 Release Fixes Key Issues
vLLM Releaseshigh confidence
Why it matters
- →Fixing embed scaling improves model accuracy and performance.
- →CUDA graphs integration enhances computational efficiency.
- →These updates make vLLM more reliable for AI developers.
vLLM has released version 0.25.0rc2, which includes fixes for embed scaling and the integration of CUDA graphs in its Transformers modeling backend. These updates are crucial for improving the performance and efficiency of AI model training and inference. The release aims to enhance the tool's reliability and effectiveness for developers working with large language models. This update marks a significant improvement in vLLM's capabilities.
Read originalThe story around this
Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
More in Models & Labs
Models & Labsmodels
llama.cpp b12029 adds CUDA 13 and Snapdragon support
This release quietly cements llama.cpp as the universal inference runtime by finally supporting NVIDIA's latest CUDA 13 stack alongside AMD's ROCm 10.0. The inclusion of native builds for Linux arm64 with Snapdragon chips, covering CPU, Adreno GPU, and Hexagon NPU, signals a serious push into mobile edge deployment. While Apple Silicon KleidiAI is disabled in this specific build, the broader hardware coverage means developers no longer need to compile from source for most modern accelerators. It’s less about new features and more about removing friction for anyone running local models on bleeding-edge or diverse hardware.
llama.cpp Releases
© The Verge AIModels & Labsother
