llama.cpp 為 SpacemiT X60 補上 Q8_0 IME1 核心
llama.cpp 合併 PR #28479，為 SpacemiT X60 新增 Q8_0 的 IME1 矩陣核心與 repack 路徑。此前該平台 IME 加速只覆蓋 Q4_0/Q4_1/Q4_K，且建置時 GGML_CPU_REPACK=OFF，Q8_0 無加速路徑，prefill 比 Q4_0 慢約十倍。
首次讓 SpacemiT X60 上的 Q8_0 獲得 IME1 加速，prefill 吞吐提升近 9 倍，對在 RISC-V 開發板本地跑 llama.cpp 的使用者有直接參考價值。
原標題：b11408
閱讀原文
| 評分 | 68 / 45（平均 56，門檻 60） |
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| 狀態 | 未入選 |
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原文
ggml-cpu : add Q8_0 IME1 matrix kernel for SpacemiT X60 ( #28479 )
ggml-cpu : add Q8_0 IME1 matrix kernel for SpacemiT X60
On the SpacemiT X60, IME matrix acceleration only covered Q4_0/Q4_1/Q4_K.
Q8_0 had no IME1 kernel, and since the SpacemiT build sets
GGML_CPU_REPACK=OFF there was no repack path compiled in either, so Q8_0
had no accelerated path at all and ran roughly ten times slower than
Q4_0 for prefill on the same board.
add make_block_q8_0x16 and the Q8_0 repack entry: interleave the
weights into the 16-column layout the IME1 vmadot sequence expects
add ime1::gemm_kernel_i8i8, an int8 x int8 IME1 kernel with a
single-row and a 4-row A path; the 4-row path loads each B panel once
and reuses it across 4 rows of A
add quantize_a_4row_i8 for the 4-row activation quantization
wire both into forward_mul_mat and the repack factory for Q8_0
docs: mark Q8_0 as supported on X60
Correctness was checked against a quant-exact integer reference for
K = 32 up to 4096, with a max relative error of about 1e-6, and by
checking that generation stays coherent across several prompts.
Tested on Milk-V Jupiter (SpacemiT X60), Bianbu 2.1.1, gcc 14.2, with
Qwen2.5-0.5B-Instruct Q8_0. llama-bench -t 4 under taskset -c 0-3, 5
repetitions on an idle board: pp128 goes from 10.70 to 93.87 t/s. Q4_0
is unchanged at 106.40 -> 107.51 t/s, as expected since this does not
touch that path.
ggml-cpu : move q8_0_16x32 decl to IME1 section
ggml-cpu : align q8_0 IME1 kernel assignments
Website:
https://llama.app
Attestations:
https://github.com/ggml-org/llama.cpp/attestations/52757256
macOS/iOS:
macOS Apple Silicon (arm64)
macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED
macOS Intel (x64)
iOS XCFramework
Linux:
Ubuntu x64 (CPU)
Ubuntu arm64 (CPU)
Ubuntu s390x (CPU)
Ubuntu x64 (Vulkan)
Ubuntu arm64 (Vulkan)
Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries
Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries
Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries
Ubuntu x64 (ROCm 10.0)
Ubuntu x64 (OpenVINO)
Ubuntu x64 (SYCL FP32)
Ubuntu x64 (SYCL FP16)
Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide
Android:
Android arm64 (CPU)
Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide
Windows:
Windows x64 (CPU)
Windows arm64 (CPU)
Windows arm64 (OpenCL Adreno)
Windows x64 (CUDA 12) - CUDA 12.4 DLLs
Windows x64 (CUDA 13) - CUDA 13.4 DLLs
Windows arm64 (CUDA 13) - CUDA 13.4 DLLs
Windows x64 (Vulkan)
Windows arm64 (Vulkan)
Windows x64 (OpenVINO)
Windows x64 (SYCL)
Windows x64 (ROCm 10.0)
openEuler:
DISABLED
openEuler x86 (310p)
openEuler x86 (910b, ACL Graph)
openEuler aarch64 (310p)
openEuler aarch64 (910b, ACL Graph)
UI:
UI
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