
Together AI has announced the public preview of preemptible compute for its GPU clusters, available on Kubernetes clusters across all regions. This new feature allows users to run interruption-tolerant workloads at half the cost of standard nodes, using the same NVIDIA accelerated infrastructure. Preemptible nodes are billed sub-hourly and can be reclaimed when capacity is needed elsewhere, making them ideal for short experiments and batch jobs. This development offers a cost-effective way to manage non-critical workloads, enhancing resource efficiency in cloud computing.
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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.
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Apple is finally unlocking its AI suite for Chinese users with the iOS 27.3 beta, marking a significant regulatory milestone after months of approval delays. The rollout relies on Alibaba’s Qwen model rather than OpenAI, integrating features like notification summaries and visual intelligence directly into the OS. By sidestepping the new Siri in favor of established text and image tools, Apple is prioritizing immediate utility over the full generative experience. This move effectively brings Apple’s AI capabilities to a massive new market, albeit with localized constraints that distinguish it from the global release.