Models & Labs
Hugging Face and SkyPilot Enable Zero-Egress AI Workloads
Hugging Face Bloghigh confidence
Why it matters
- →Enables cost-effective AI workload distribution across multiple clouds.
- →Reduces operational complexity by eliminating data migration needs.
- →Leverages efficient data transfer with Xet-backed deduplication.
Hugging Face and SkyPilot have collaborated to enable AI workloads to run across multiple cloud platforms without egress fees. This integration allows users to mount Hugging Face Buckets into SkyPilot tasks, facilitating data access on any cloud's GPUs at no extra cost. The system uses Xet-backed deduplication to efficiently manage data transfer by only moving changed chunks. This development simplifies operations for teams using Hugging Face storage, removing the need for data migration and reducing costs.
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llama.cpp b10955 release addresses heap corruption
The 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.
llama.cpp Releases
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llama.cpp b10956 release enhances SYCL backend
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.