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Home/Models & Labs
Models & Labs

Llama.cpp b10178 Release Adds Trace Logging

llama.cpp Releases·July 30, 2026·high confidence

Why it matters

  • →Trace logging provides developers with deeper insights into system operations.
  • →Enhanced platform support broadens the tool's applicability.
  • →No new models, but improved infrastructure for existing users.

Llama.cpp has released version b10178, which includes new trace logging capabilities for slot similarity checking in server-context.cpp. This feature provides detailed logs on skip reasons and similarity calculations during prompt cache slot selection, offering developers greater insight into the process. The release supports a wide range of platforms, including macOS, Linux, Windows, and openEuler, though no new model architectures are introduced. This update enhances llama.cpp's utility for developers by improving transparency and platform compatibility.

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The story around this

Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.

llama.cpp b9747 release enhances model tracking — llama.cpp Releases1Llama.cpp b10178 Release Adds Trace Loggingllama.cpp b10593 Release Fixes and Enhancements — llama.cpp Releases2Jun 22You are hereAug 24

How we got here

  1. 1
    llama.cpp b9747 release enhances model tracking

    llama.cpp Releases · June 22, 2026 · Same story

What happened next

  1. 2
    llama.cpp b10593 Release Fixes and Enhancements

    llama.cpp Releases · August 24, 2026 · Same story

More from llama.cpp Releases

Coding Toolscoding

llama.cpp b11532 adds ModernBERT and CUDA 13

This release quietly fixes a critical accuracy gap for ModernBERT encoders by implementing exact GELU activation, ensuring semantic embeddings match the original PyTorch models rather than approximations. It also brings native support for CUDA 13.4 across Linux and Windows, closing the driver compatibility lag that has plagued NVIDIA users on newer hardware stacks. While KleidiAI builds are temporarily disabled on Apple Silicon, the broader expansion to ROCm 10.0 and Snapdragon NPU keeps llama.cpp as the most versatile local inference runtime available today.

llama.cpp Releases·Oct 10, 2026
Coding Toolscoding

llama.cpp b11533 fixes Adreno A6X crashes

This release targets a specific but painful stability issue for Android users running llama.cpp on Qualcomm Adreno A6X GPUs. The kernel compiler was crashing due to argument limits in the iot device backend, effectively breaking local inference on those chips. By skipping the problematic kernel and adding explicit detection for the Adreno 623, the team restores functionality where it previously failed hard. It’s a narrow fix, but essential for anyone trying to run models on mid-range Android hardware without hitting compiler errors.

llama.cpp Releases·Oct 10, 2026
Coding Toolscoding

llama.cpp b11534 optimizes CUDA SSM and adds ROCm 10

This release quietly sharpens llama.cpp’s performance on NVIDIA GPUs by fusing state snapshot copies into the recurrent cache during SSM scans. It also removes redundant CUDA copies in specific non-speculative decoding scenarios, shaving off latency where it counts. On the AMD side, ROCm 10.0 support arrives alongside stable builds for CUDA 12.8 and 13.4, keeping the library competitive across hardware vendors. KleidiAI on Apple Silicon is temporarily disabled, a minor setback for Mac users until that integration is stabilized. The net result is faster inference for SSM-based models without changing the user experience.

llama.cpp Releases·Oct 10, 2026

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