b12186
A change has been made to add the 'readonly' attribute to decodeBuf declarations in the Vulkan backend, with potential performance improvements expected in future driver versions.
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A change has been made to add the 'readonly' attribute to decodeBuf declarations in the Vulkan backend, with potential performance improvements expected in future driver versions.
The latest release of llama.cpp (b12187) includes the implementation of a WebGPU group norm kernel, with associated CI workflows, performance tests, and cleanup of related code. The update supports various platforms including macOS, Linux, Windows, Android, and specific hardware backends like Vulkan, CUDA, ROCm, OpenVINO, SYCL, and Snapdragon devices. Some builds remain disabled or experimental.
The server update addresses slot processing order by ensuring parent slots are visited before child slots, preventing delays caused by child tasks landing on lower slot IDs. This change ensures all child tasks join the same batch after being woken by their parent. Additionally, new test tags have been introduced for monitoring: __TEST_TAG_WAIT_PARENT_SLOT__ for child slot waits, __TEST_TAG_PROMPT_CACHE_RESTORE__ for prompt cache restores, and __TEST_TAG_PROMPT_CACHE_FULL_RESTORE__ when a cached prompt is fully contained in a new one based on token counts.
The metal backend's kernel_group_norm_f32 was updated to correctly handle tensors with ne03 > 1 and non-contiguous inputs by dispatching one threadgroup per (group, i03) pair, computing per-batch statistics, clamping group element count for incomplete groups, and supporting strided access via separate strides for source and destination. The fix also restricts GROUP_NORM to f32 inputs and improves performance through specialized pipeline variants.
The Agent adoption chart in the Copilot impact dashboard now includes Copilot Chat and the VS Code Agents window, so you can see engagement across nine Copilot experiences. The adoption… The post Copilot impact dashboard adds chat and VS Code agent usage appeared first on The GitHub Blog.
Greenlight Financial resolves more than 90% of up to 4,000 monthly security and risk cases without human intervention using Luna, a tiered multi-agent system on Amazon Bedrock AgentCore. This post covers the architecture, episodic memory for continuous learning, and the expansion from SOC operations into financial risk triage.
Amazon SageMaker Inference now supports GPU partitioning with NVIDIA Multi-Instance GPU (MIG), which divides one GPU into hardware-isolated partitions so you can run a different model in each or take a right-sized fraction of a newer accelerator. This post shows how two optional API fields place one model per partition, and what we measured against a whole older GPU.
An approach of taking many photos to enhance one final image is a technique called "stacking". Stacking alone doesn't account for everything so this is a take that helps get better alignment of images, allows comparisons, and drop bad images that dont match. Its not super resolution but it feels like when you look at it. it reduces the noise and makes it look more organic shot with less expensive hardware. enjoy! Comments URL: https://news.ycombinator.com/item?id=50401218 Points: 1 # Comments: 0
Article URL: https://github.com/Creepybits/trellis2-blackwell-wsl Comments URL: https://news.ycombinator.com/item?id=50403233 Points: 1 # Comments: 0
Article URL: https://github.com/Kirby54/Select Comments URL: https://news.ycombinator.com/item?id=50400882 Points: 1 # Comments: 0
Article URL: https://hodor.gatebolt.com/blog/secret-leak-test Comments URL: https://news.ycombinator.com/item?id=50401639 Points: 1 # Comments: 0
Article URL: https://github.com/leofritsch/web-potions Comments URL: https://news.ycombinator.com/item?id=50404138 Points: 1 # Comments: 1
Hidehiko ASAHI shares a month of WalShadow private-preview testing, comparing replication latency with ClickPipes and checking data correctness, schema changes, and recovery.
Anthropic is using AI to build more powerful AI. And it appears to be working remarkably well, with the company's models improving roughly twice as quickly as a year ago, with little indication of slowing down. That's according to a new report published Thursday by the AI safety research organization METR, following an 11-week investigation […]
The update removes the u8path() function and replaces it with generic_string() in the server component, as part of a broader effort to streamline and standardize string handling across platforms.
The latest release of llama.cpp, version b12132, includes the removal of u8path() in favor of generic_string(), along with updated binaries for multiple platforms including macOS, Linux, Windows, Android, and openEuler, supporting various backends like CUDA, Vulkan, ROCm, OpenVINO, and SYCL.
The latest release of llama.cpp (b12135) introduces support for MEAN operations in WebGPU, enhancing performance and functionality across multiple platforms including macOS, Linux, Windows, Android, and iOS. The release includes precompiled binaries for various architectures and backends such as CUDA, Vulkan, ROCm, OpenVINO, SYCL, and zDNN, with specific builds for Apple Silicon, Snapdragon devices, and openEuler systems. Some features remain disabled or are under development.
The update addresses an issue in llama.cpp where the number of GPU layers was incorrectly calculated by not accounting for skipped NextN layers, leading to improper layer distribution between CPU and GPU. The fix ensures that skipped NextN layers are included in the n_gpu_layers count and properly assigned during model loading, resolving a bug where layer 0 remained on the CPU despite offloading all other layers.
A recent update to llama.cpp addresses a critical issue by rejecting non-object session updates in the server, preventing malformed realtime session data from causing uncaught exceptions and server termination. The release includes binaries for multiple platforms including macOS, Linux, Windows, Android, and iOS, with support for various backends like CUDA, Vulkan, ROCm, OpenVINO, and SYCL.
The update introduces optimized Metal kernel usage for matrix multiplication with K values between 32 and 64, and adds a new kernel for BF16 x BF16 matrix multiplication, improving performance on Apple Silicon devices.