vLLM has released version 0.26.0, featuring significant updates including the new Inkling model family with advanced CUDA graph support and speculative decoding. The release also enhances performance across multiple hardware platforms, such as AMD and XPU, with the DeepSeek-V4 performance push. Improvements in attention mechanisms and KV offloading provide greater flexibility for hybrid models. These updates make vLLM a more powerful tool for developers handling large-scale AI models.
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This release quietly expands llama.cpp's hardware support to include Qualcomm's Hexagon NPU on Linux arm64, a significant step for local inference on Snapdragon devices. It also updates CUDA builds to version 13.4 and introduces ROCm 10.0 binaries, keeping the project aligned with the latest NVIDIA and AMD driver ecosystems. KleidiAI on Apple Silicon is temporarily disabled in this build, likely due to stability checks rather than a feature rollback. For developers targeting edge AI or diverse GPU stacks, this update ensures broader compatibility without requiring custom compilation.
© Lev SelectorNVIDIA introduced the NVFP4 4-bit format and SoL-Pi technology, which uses 2x fewer tokens for improved efficiency.
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