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AI, as it happens

1878 published stories
Compute & infrastructure · Software release · 125 storiesClear filters ×
2026-11-14
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01:58

b12362

The latest release of llama.cpp (b12362) includes optimizations for grammar matching through left factoring, improved handling of character class boundaries, prevention of infinite expansion, and enhanced code clarity with ASCII comments and deduplicated logic. It reverts prior changes related to AST refactoring and alternative limits, while introducing shared immutable rules and removing performance benchmarks. The release supports multiple platforms including macOS, Linux, Windows, Android, and iOS, with various backend options such as CUDA, Vulkan, ROCm, OpenVINO, and SYCL.

Inference systems & deploymentRead original
2026-11-13
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18:20

v1.0.0

The llama.cpp project has released version 1.0.0, marking a significant milestone with numerous improvements across multiple platforms including CUDA, Vulkan, Metal, SYCL, OpenCL, and WebGPU. Key updates include enhanced support for quantized models (Q5_0, Q5_1, FP8), optimizations for flash attention and MoE caching, improved server security features like CORS protection and SSRF mitigation, UI enhancements such as sidebar reorganization and live-preview markdown editing, and better cross-platform compatibility. The release also includes bug fixes, CI/CD improvements, and updated dependencies.

Inference systems & deploymentRead original
08:34

b12336

The update requires the position embedding tensor for HunyuanVL to be explicitly provided as F32 during model loading to prevent buffer overflow issues when using F64 tensors, ensuring compatibility and stability on first image requests.

Inference systems & deploymentRead original
2026-11-12
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02:15

b12287

MMVF is recommended for BF16 matrices with few rows and small batch sizes due to its superior performance when K is large, while MMF remains efficient for larger row counts. The decision depends on the condition rows × batch × 256 ≤ nsm × min(K, max_k) and K ≥ 1024, with GPU-specific limits on columns and max_k. Evaluation cases include 128×2560, 512×2560, and 320×10240 at batch sizes 2–8.

Inference systems & deploymentRead original
2026-11-11
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09:26

b12263

The update addresses CUDA out of memory errors by reducing the VMM pool reservation size to 8 GB on GPUs with combined memory, halving the reservation size and retrying on failure only when encountering CUDA_ERROR_OUT_OF_MEMORY, while also logging a warning when the reservation is less than the maximum size.

Inference systems & deploymentRead original
06:11

b12259

The latest release of llama.cpp (b12259) adds support for Kimi K3.1 Vision, includes fixes for preprocessing and projector MLP in both K2.5 and K3.1 models, and features minor renaming changes. The release provides precompiled binaries for multiple platforms including macOS, Linux, Windows, Android, and openEuler, with support for various backends such as CUDA, Vulkan, ROCm, OpenVINO, SYCL, and Snapdragon-specific hardware.

Inference systems & deploymentRead original
00:57

b12249

The update introduces graceful failure handling for unreachable RPC endpoints and failed handshakes in ggml-rpc, replacing fatal aborts with reportable errors. Functions now return appropriate values (e.g., false, nullptr) on connection failures, allowing host applications to handle issues without crashing. A regression test was added for unreachable endpoints, and low-value comments were removed. Mid-stream failures remain unchanged to prevent silent corruption.

Inference systems & deploymentRead original
2026-11-10
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21:09

b12246

On Qualcomm Adreno GPUs, scalar flash attention was limited to 8 rows per workgroup when head size exceeds 128 to prevent register spilling and performance degradation. This change improved performance significantly, reducing execution time from several seconds to milliseconds and enabling completion of the test-backend-ops case.

Inference systems & deploymentRead original
00:41

b12229

A fix was implemented to ensure the OpenAI-compatible chat completions endpoint correctly processes JSON schema when provided at the top level in response_format.schema, rather than requiring it to be nested within response_format.json_schema.schema. The update also improves error messages, documents the correct usage in the README, and adds test cases to verify behavior, resolving issue #10732.

Inference systems & deploymentRead original
2026-11-09
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20:00

b12220

The Metal kernel for ggml_tri has been updated to use a select operation instead of scaling by comparison, aligning its behavior with the CPU kernel. This change ensures that values outside the triangle are set to zero rather than becoming NaN when encountering ±inf. A new test, test_tri_special, validates this fix across all four triangle types, confirming it passes with the update but failed previously.

Inference systems & deploymentRead original
2026-11-08
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10:05

b12198

The llama.cpp release b12198 includes a change to downgrade per-request proxy logs to trace level, along with numerous precompiled binaries for macOS, Linux, Windows, Android, and other platforms, supporting various backends like CUDA, Vulkan, ROCm, OpenVINO, and SYCL. Some builds are disabled or experimental, including openEuler and KleidiAI-enabled macOS versions.

Inference systems & deploymentRead original
09:46

b12197

The update introduces support for automatic loading of libhtp without relying on legacy environment variables, enhances path handling for non-ASCII directories and long paths, adds platform-wide support for FASTRPC_MANAGE_DSP_LIBRARY_PATH and GGML_HEXAGON_LIBHTP_DIR, maintains forward slashes in absolute paths on Android and Linux, ensures absolute path precedence with fallback mechanisms, and includes CDSP1,2,3 in the fallback chain for IQ10 compatibility.

Inference systems & deploymentRead original
09:11

b12195

The llama.cpp release b12195 introduces a change to refuse device buffers that would exceed VRAM, improving memory management. It includes precompiled binaries for multiple platforms including macOS, Linux, Windows, Android, and openEuler, with support for various backends such as CUDA, ROCm, Vulkan, OpenVINO, SYCL, and others. Some builds are marked as disabled or experimental.

Inference systems & deploymentRead original
07:45

b12193

The llama.cpp release b12193 includes a change to move log-mel computation to the cgraph, removes stray files, updates mtmd_audio_track to include source, and adds tests. It provides precompiled binaries for macOS, Linux, Windows, Android, and openEuler across various architectures and backends including CUDA, Vulkan, ROCm, OpenVINO, SYCL, and Snapdragon-specific platforms.

Inference systems & deploymentRead original
05:28

b12192

The latest release of llama.cpp adds support for the MageVL-Cowork model and adjusts how rope frequencies are handled, now stored at the model level within clip_model instead of being copied into each clip_layer. The loader verifies that rope_freqs is a 32-bit floating-point array with dimensions matching d_head, ensuring compatibility with expected 4:6:6 splits in the computational graph.

Inference systems & deploymentRead original
05:11

b12191

The latest update to llama.cpp introduces enhanced MoE (Mixture of Experts) cache functionality, enabling computation of cache misses on the CPU with a configurable CPU share. A new tuning script, moe-cache-tune.py, automatically determines optimal cache size and CPU share settings based on system performance. The update also improves scheduling efficiency, removes redundant sync operations, and refactors code for better maintainability, while maintaining compatibility across multiple platforms including macOS, Linux, Windows, and Android.

Inference systems & deploymentRead original
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