b9866
ggml-org/llama.cppb9866Jul 3, 2026by github-actions[bot]
AI Summary
Enables topk-moe fusion for 288 experts in CUDA, previously only supported for power-of-2 counts or 576. This improves decode performance for large MoE models.
Key Highlights
- Enables topk-moe fusion for 288 expert models (e.g., Step-3.7-Flash)
- Adds missing template instantiation for 288 expert counts
- Measured ~2.4% decode speedup at shallow context
- Fusion only affects decode routing, not prompt processing
New Features
- CUDA optimization for 288-expert Mixture of Experts models
Full Release Notes
<details open> cuda: enable topk-moe fusion for 288 experts (#25267) * cuda: enable topk-moe fusion for 288 experts The topk-moe fusion only accepted power-of-2 expert counts (or the special-cased 576), so models with 288 experts (e.g. Step-3.7-Flash) fell back to the unfused per-layer routing chain: softmax/sigmoid, argsort, get_rows, sum_rows, div, clamp, scale. At batch size 1 that is ~330 extra tiny graph nodes per token. 288 is a multiple of the warp size, so the existing kernel already handles it; this adds the missing template instantiation and accepts 288 in the eligibility check. Measured on gfx1151 with Step-3.7-Flash IQ4_XS (llama-bench, -b 4096 -ub 4096 -fa 1 -dio 1 -ctk q8_0 -ctv q8_0; machine idle, before/after paired so pp4096 stays matched as a load control): test | before | after ----------------+----------------+---------------- pp4096 | 460.99 ± 0.45 | 462.47 ± 0.34 (unchanged) tg128 | 19.10 ± 0.04 | 19.56 ± 0.03 (+2.4%) tg128 @ d30000 | 12.68 ± 0.04 | 12.69 ± 0.03 (unchanged) Prompt processing is unaffected (the fusion only touches decode routing). The decode gain is ~+2.4% at shallow context and fades with depth: by 30k tokens each step is attention-bound over the KV cache, so removing the fixed routing overhead is no longer visible. Assisted-By: Claude Fable 5 <noreply@anthropic.com> * Update tests/test-backend-ops.cpp Co-authored-by: Oliver Simons <osimons@nvidia.com> * Add comment for case 288 in topk-moe.cu --------- Co-authored-by: Oliver Simons <osimons@nvidia.com> </details> **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-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/b9866/llama-b9866-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-ubuntu-arm64.tar.gz) - [Ubuntu s390x (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-ubuntu-s390x.tar.gz) - [Ubuntu x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-ubuntu-vulkan-x64.tar.gz) - [Ubuntu arm64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-ubuntu-vulkan-arm64.tar.gz) - [Ubuntu x64 (ROCm 7.2)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-ubuntu-rocm-7.2-x64.tar.gz) - [Ubuntu x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-ubuntu-openvino-2026.2.1-x64.tar.gz) - [Ubuntu x64 (SYCL FP32)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-ubuntu-sycl-fp32-x64.tar.gz) - [Ubuntu x64 (SYCL FP16)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-ubuntu-sycl-fp16-x64.tar.gz) **Android:** - [Android arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-android-arm64.tar.gz) **Windows:** - [Windows x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-win-cpu-x64.zip) - [Windows arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-win-cpu-arm64.zip) - [Windows arm64 (OpenCL Adreno)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-win-opencl-adreno-arm64.zip) - [Windows x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-win-cuda-12.4-x64.zip) - [CUDA 12.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/b9866/cudart-llama-bin-win-cuda-12.4-x64.zip) - [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-win-cuda-13.3-x64.zip) - [CUDA 13.3 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/b9866/cudart-llama-bin-win-cuda-13.3-x64.zip) - [Windows x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-win-vulkan-x64.zip) - [Windows x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-win-openvino-2026.2.1-x64.zip) - [Windows x64 (SYCL)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-bin-win-sycl-x64.zip) - [Windows x64 (HIP)](https://github.com/ggml-org/llama.cpp/releases/download/b9866/llama-b9866-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/b9866/llama-b9866-ui.tar.gz)