b10076

ggml-org/llama.cppb10076Jul 21, 2026by github-actions[bot]

AI Summary

CUDA optimization to vectorize get_rows operation for int4 types.

Key Highlights

  • Vectorize same-type get_rows with int4 copy
  • Performance improvement on Strix Halo (gfx1151) - 27% faster
  • Adds k_get_rows_float_vec for contiguous same-type cases
  • Fixes issue #25929

Full Release Notes

<details open>

CUDA: vectorize same-type get_rows with int4 copy (#25929)

k_get_rows_float did a scalar one-element-per-thread copy and recomputed the
row-invariant work (index load, fast_div_modulo, src/dst row pointers) for
every element. Hoist that out of the per-element loop, and add a vectorized
path (k_get_rows_float_vec) that copies one int4 (16 B) per thread for the
contiguous same-type (no-cast) case.

The vectorized path is gated at compile time (is_same<src0_t, dst_t>) and at
runtime on 16-byte alignment of the base pointers and all row strides and on
ne00 % VEC == 0. Vectorizing divides the block count by VEC, so a small
single-row gather can drop below the device CU count and regress; an
occupancy gate keeps those on the block-rich scalar path.

On Strix Halo (gfx1151) the DeltaNet recurrent-state gather (ne00=524288)
drops 18.6us -> 13.0us (rocprofv3 HW timestamps), faster than the Vulkan
backend, with no regression on the small conv-state gather; total get_rows
-27%. test-backend-ops GET_ROWS passes (47/47).

Assisted-by: Claude Opus 4.8

</details>

**Website:**
- <https://llama.app>

**macOS/iOS:**
- [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-macos-arm64.tar.gz)
- macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780)
- [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-macos-x64.tar.gz)
- [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-xcframework.zip)

**Linux:**
- [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-ubuntu-x64.tar.gz)
- [Ubuntu arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-ubuntu-arm64.tar.gz)
- [Ubuntu s390x (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-ubuntu-s390x.tar.gz)
- [Ubuntu x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-ubuntu-vulkan-x64.tar.gz)
- [Ubuntu arm64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-ubuntu-vulkan-arm64.tar.gz)
- [Ubuntu x64 (ROCm 7.2)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-ubuntu-rocm-7.2-x64.tar.gz)
- [Ubuntu x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-ubuntu-openvino-2026.2.1-x64.tar.gz)
- [Ubuntu x64 (SYCL FP32)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-ubuntu-sycl-fp32-x64.tar.gz)
- [Ubuntu x64 (SYCL FP16)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-ubuntu-sycl-fp16-x64.tar.gz)

**Android:**
- [Android arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-android-arm64.tar.gz)

**Windows:**
- [Windows x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-win-cpu-x64.zip)
- [Windows arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-win-cpu-arm64.zip)
- [Windows arm64 (OpenCL Adreno)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-win-opencl-adreno-arm64.zip)
- [Windows x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-win-cuda-12.4-x64.zip) - [CUDA 12.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/b10076/cudart-llama-bin-win-cuda-12.4-x64.zip)
- [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-win-cuda-13.3-x64.zip) - [CUDA 13.3 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/b10076/cudart-llama-bin-win-cuda-13.3-x64.zip)
- [Windows x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-win-vulkan-x64.zip)
- [Windows x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-win-openvino-2026.2.1-x64.zip)
- [Windows x64 (SYCL)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-win-sycl-x64.zip)
- [Windows x64 (HIP)](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-bin-win-hip-radeon-x64.zip)

**openEuler:**
- [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23705)
- openEuler x86 (310p)
- openEuler x86 (910b, ACL Graph)
- openEuler aarch64 (310p)
- openEuler aarch64 (910b, ACL Graph)

**UI:**
- [UI](https://github.com/ggml-org/llama.cpp/releases/download/b10076/llama-b10076-ui.tar.gz)