llama.cpp/docs/backend/snapdragon

Snapdragon-based devices

Setup

Android

The easiest way to build llama.cpp for a Snapdragon-based Android device is using the toolchain Docker image (see github.com/snapdragon-toolchain). This image includes Android NDK, OpenCL SDK, Hexagon SDK, CMake, etc.

This method works on Linux, macOS, and Windows. macOS and Windows users should install Docker Desktop.

~/src/llama.cpp$ docker run -it -u $(id -u):$(id -g) --volume $(pwd):/workspace --platform linux/amd64 ghcr.io/snapdragon-toolchain/arm64-android:v0.7
[d]/> cd /workspace

Note: The rest of the Android build process assumes that you're running inside the toolchain container.

Windows On Snapdragon

Native Windows 11 arm64 builds has the following tools dependencies:

• MS Visual Studio 2026 (Community Edition or Pro)
  • MSVC arm64 standard and runtime libraries
  • UCRT and Driver Kit
• LLVM core libraries and Clang compiler (winget)
• CMake, Git, Python (winget)
• Hexagon SDK Community Edition 6.6 or later (see windows.md)
• OpenCL SDK 2.3 or later (see windows.md)

Note: The rest of the Windows build process assumes that you're running natively in Powershell. Adapt below build commands accordingly.

How to Build

Let's build llama.cpp with CPU, OpenCL, and Hexagon backends via CMake presets:

[d]/workspace> cp docs/backend/snapdragon/CMakeUserPresets.json .

[d]/workspace> cmake --preset arm64-android-snapdragon-release -B build-snapdragon
Preset CMake variables:
  ANDROID_ABI="arm64-v8a"
  ...
  CMAKE_TOOLCHAIN_FILE="/opt/android-ndk-r28b/build/cmake/android.toolchain.cmake"
  GGML_HEXAGON="ON"
  GGML_OPENCL="ON"
  GGML_OPENMP="OFF"
  HEXAGON_SDK_ROOT="/opt/hexagon/6.6.0.0"
...
-- Including OpenCL backend
-- Including Hexagon backend
...
-- Build files have been written to: /workspace/build-snapdragon

[d]/workspace> cmake --build build-snapdragon
...
[144/356] Performing build step for 'htp-v73'
[1/16] Generating htp_iface_skel.c, htp_iface_stub.c, htp_iface.h
[2/16] Building C object CMakeFiles/ggml-htp-v73.dir/hvx-sigmoid.c.obj
[3/16] Building C object CMakeFiles/ggml-htp-v73.dir/htp-dma.c.obj
[4/16] Building C object CMakeFiles/ggml-htp-v73.dir/worker-pool.c.obj
...
-- Installing: /workspace/build-snapdragon/ggml/src/ggml-hexagon/libggml-htp-v73.so
-- Installing: /workspace/build-snapdragon/ggml/src/ggml-hexagon/libggml-htp-v75.so
...

To generate an installable "package" simply use cmake --install:

[d]/workspace> cmake --install build-snapdragon --prefix pkg-snapdragon/llama.cpp
-- Install configuration: "Release"
-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-cpu.so
-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-opencl.so
-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-hexagon.so
-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-htp-v73.so
-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-htp-v75.so
-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-htp-v79.so
-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-htp-v81.so
-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml.so
...
-- Installing: /workspace/pkg-snapdragon/llama.cpp/bin/llama-bench
-- Installing: /workspace/pkg-snapdragon/llama.cpp/bin/llama-cli
...

How to Install

Android

For this step, your device needs to be configured for on-device development. Please see https://developer.android.com/studio/debug/dev-options for details.

Once ADB is enabled, use adb push to install pkg-snapdragon on the device. Note that the toolchain Docker image doesn't have ADB and doesn't set up the ADB bridge. Please use native ADB on the host.

~/src/llama.cpp$ adb push pkg-snapdragon/llama.cpp /data/local/tmp/
pkg-snapdragon/llama.cpp/bin/: 67 files pushed, 0 skipped. 190.2 MB/s (919095042 bytes in 4.607s)
pkg-snapdragon/llama.cpp/include/: 19 files pushed, 0 skipped. 20.5 MB/s (255173 bytes in 0.012s)
pkg-snapdragon/llama.cpp/lib/: 16 files pushed, 0 skipped. 144.4 MB/s (43801382 bytes in 0.289s)
102 files pushed, 0 skipped. 186.9 MB/s (963151597 bytes in 4.914s)

At this point, you should also install some models:

~/src/llama.cpp$ wget https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_0.gguf
...
2025-10-11 12:04:52 (10.7 MB/s) - ‘Llama-3.2-1B-Instruct-Q4_0.gguf’ saved [773025920/773025920]

~/src/llama.cpp$ adb push Llama-3.2-1B-Instruct-Q4_0.gguf /data/local/tmp/gguf
Llama-3.2-1B-Instruct-Q4_0.gguf: 1 file pushed, 0 skipped. 38.3 MB/s (773025920 bytes in 19.250s)

Windows

All artifacts are already installed in the pkg-snapdragon folder. To run, adapt below instructions to use Powershell scripts in scripts/snapdragon/windows.

How to Run

The easiest way to run llama.cpp cli tools is using provided wrapper scripts that properly set up all required environment variables.

llama.cpp supports three backends on Snapdragon-based devices: CPU, Adreno GPU (GPUOpenCL), and Hexagon NPU (HTP0-4). You can select which backend to run the model on using the D= variable, which maps to the --device option.

Hexagon NPU behaves as a "GPU" device when it comes to -ngl and other offload-related options.

Here are some examples of running various llama.cpp tools via ADB.

Simple question for Llama-3.2-1B

~/src/llama.cpp$ M=Llama-3.2-1B-Instruct-Q4_0.gguf D=HTP0 ./scripts/snapdragon/adb/run-completion.sh -p "what is the most popular cookie in the world?"
...
ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev 1
ggml-hex: Hexagon Arch version v79
ggml-hex: allocating new session: HTP0
ggml-hex: new session: HTP0 : session-id 0 domain-id 3 uri file:///libggml-htp-v79.so?htp_iface_skel_handle_invoke&_modver=1.0&_dom=cdsp&_session=0 handle 0xb4000072c7955e50
...
load_tensors: offloading output layer to GPU
load_tensors: offloaded 17/17 layers to GPU
load_tensors:          CPU model buffer size =   225.49 MiB
load_tensors:         HTP0 model buffer size =     0.26 MiB
load_tensors:  HTP0-REPACK model buffer size =   504.00 MiB
...
I hope this helps you understand the world's most popular cookies! [end of text]
...
llama_perf_sampler_print:    sampling time =      30.08 ms /   487 runs   (    0.06 ms per token, 16191.77 tokens per second)
llama_perf_context_print:        load time =     617.94 ms
llama_perf_context_print: prompt eval time =      80.76 ms /    11 tokens (    7.34 ms per token,   136.21 tokens per second)
llama_perf_context_print:        eval time =    9210.59 ms /   475 runs   (   19.39 ms per token,    51.57 tokens per second)
llama_perf_context_print:       total time =    9454.92 ms /   486 tokens
llama_perf_context_print:    graphs reused =        473
llama_memory_breakdown_print: | memory breakdown [MiB] | total   free    self   model   context   compute    unaccounted |
llama_memory_breakdown_print: |   - HTP0 (Hexagon)     |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
llama_memory_breakdown_print: |   - Host               |                  439 =   225 +     136 +      77                |
llama_memory_breakdown_print: |   - HTP0-REPACK        |                  504 =   504 +       0 +       0                |

Summary request for OLMoE-1B-7B. This is a large model that requires two HTP sessions/devices

~/src/llama.cpp$ M=OLMoE-1B-7B-0125-Instruct-Q4_0.gguf NDEV=2 D=HTP0,HTP1 ./scripts/snapdragon/adb/run-completion.sh -f surfing.txt
...
ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev 1
ggml-hex: Hexagon Arch version v81
ggml-hex: allocating new session: HTP0
ggml-hex: allocating new session: HTP1
...
load_tensors: offloading output layer to GPU
load_tensors: offloaded 17/17 layers to GPU
load_tensors:          CPU model buffer size =   143.86 MiB
load_tensors:         HTP1 model buffer size =     0.23 MiB
load_tensors:  HTP1-REPACK model buffer size =  1575.00 MiB
load_tensors:         HTP0 model buffer size =     0.28 MiB
load_tensors:  HTP0-REPACK model buffer size =  2025.00 MiB
...
llama_context:        CPU  output buffer size =     0.19 MiB
llama_kv_cache:       HTP1 KV buffer size =   238.00 MiB
llama_kv_cache:       HTP0 KV buffer size =   306.00 MiB
llama_kv_cache: size =  544.00 MiB (  8192 cells,  16 layers,  1/1 seqs), K (q8_0):  272.00 MiB, V (q8_0):  272.00 MiB
llama_context:       HTP0 compute buffer size =    15.00 MiB
llama_context:       HTP1 compute buffer size =    15.00 MiB
llama_context:        CPU compute buffer size =    24.56 MiB
...
llama_perf_context_print: prompt eval time =    1730.57 ms /   212 tokens (    8.16 ms per token,   122.50 tokens per second)
llama_perf_context_print:        eval time =    5624.75 ms /   257 runs   (   21.89 ms per token,    45.69 tokens per second)
llama_perf_context_print:       total time =    7377.33 ms /   469 tokens
llama_perf_context_print:    graphs reused =        255
llama_memory_breakdown_print: | memory breakdown [MiB] | total   free    self   model   context   compute    unaccounted |
llama_memory_breakdown_print: |   - HTP0 (Hexagon)     |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
llama_memory_breakdown_print: |   - HTP1 (Hexagon)     |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
llama_memory_breakdown_print: |   - Host               |                  742 =   144 +     544 +      54                |
llama_memory_breakdown_print: |   - HTP1-REPACK        |                 1575 =  1575 +       0 +       0                |
llama_memory_breakdown_print: |   - HTP0-REPACK        |                 2025 =  2025 +       0 +       0                |

Op test for MUL_MAT

~/src/llama.cpp$ HB=0 ./scripts/snapdragon/adb/run-tool.sh test-backend-ops -b HTP0 -o MUL_MAT
...
Backend 2/3: HTP0
Device description: Hexagon
Device memory: 2048 MB (2048 MB free)
MUL_MAT(type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
MUL_MAT(type_a=q4_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
MUL_MAT(type_a=q4_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK

~/src/llama.cpp-hexagon$ M=Llama-3.2-1B-Instruct-Q4_0.gguf ./scripts/snapdragon/adb/run-bench.sh -p 128 -n 64
...
ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev 1
ggml-hex: Hexagon Arch version v79
ggml-hex: allocating new session: HTP0
ggml-hex: new session: HTP0 : session-id 0 domain-id 3 uri file:///libggml-htp-v79.so?htp_iface_skel_handle_invoke&_modver=1.0&_dom=cdsp&_session=0 handle 0xb400007d4b231090
| model          |       size | params | backend    | ngl | threads | n_batch | mmap |  test |           t/s |
| ---------------| ---------: | -----: | ---------- | --: | ------: | ------: | ---: | ----: | ------------: |
| llama 1B Q4_0  | 729.75 MiB | 1.24 B | HTP        |  99 |       4 |     128 |    0 | pp128 | 169.42 ± 1.75 |
| llama 1B Q4_0  | 729.75 MiB | 1.24 B | HTP        |  99 |       4 |     128 |    0 |  tg64 |  51.54 ± 1.13 |

build: 6a8cf8914 (6733)

Environment variables

• GGML_HEXAGON_NDEV=1 Controls the number of devices/sessions to allocate. The default is 1. Most quantized models under 4B fit into a single session; an 8B model needs two, and a 20B model needs four.

• GGML_HEXAGON_NHVX=0 Controls the number of HVX hardware threads to use. The default is all (actual number varies depending on the hardware version).

• GGML_HEXAGON_HOSTBUF=1 Controls whether the Hexagon backend allocates host buffers. By default, all buffers except for REPACK are host buffers. This option is required for testing Ops that require REPACK buffers (MUL_MAT and MUL_MAT_ID).

• GGML_HEXAGON_VERBOSE=1 Enables verbose logging of Ops from the backend. Example output:

  ggml-hex: HTP0 graph-compute n_nodes 2
  ggml-hex: HTP0 matmul : blk.27.ffn_up.weight x ffn_norm-27 -> ffn_up-27 : 3072:8192 x 3072:1 -> 8192:1 : q4_0 x f32 -> f32 : HTP0 x HTP0 -> HTP0 : flags 0x1
  ggml-hex: HTP0 matmul : blk.27.ffn_gate.weight x ffn_norm-27 -> ffn_gate-27 : 3072:8192 x 3072:1 -> 8192:1 : q4_0 x f32 -> f32 : HTP0 x HTP0 -> HTP0 : flags 0x3
  ggml-hex: HTP0 graph-compute n_nodes 1
  ggml-hex: HTP0 matmul : blk.27.ffn_down.weight x ffn_gate_par-27 -> ffn_out-27 : 8192:3072 x 8192:1 -> 3072:1 : q4_0 x f32 -> f32 : HTP0 x HTP0 -> HTP0 : flags 0x0
  ggml-hex: HTP0 get-tensor result_output : data 0x7592487000 offset 0 size 513024
  

• GGML_HEXAGON_PROFILE=1 Enables Op profiling:

  • 1 Basic profile with per-op usecs and cycles counters
  • 2 Extended profile with per-op usecs, cycles and default PMU counter data
  • 0x1,...,0x8 Extended profile with per-op usecs, cycles and custom PMU counter data

  The logging output can be either saved into a file for post-processing or it can be piped directly into the post-processing tool to generate the report. Examples:

  `GGML_HEXAGON_PROFILE=1 llama-completion ... |& ./scripts/snapdragon/ggml-hexagon-profile.py -`
  

• GGML_HEXAGON_OPSTAGE=0x0 Allows enabling specific stages of the Op processing pipeline:

  • 0x1 Enable Op Queue (i.e., queuing Ops into NPU)
  • 0x2 Enable Op Compute (MUL_MAT, etc.)

  Examples:

  `GGML_HEXAGON_OPSTAGE=0x1 llama-completion ...` - Ops are enqueued to the NPU but dma & compute are disabled
  `GGML_HEXAGON_OPSTAGE=0x3 llama-completion ...` - Full queuing and processing of Ops (default)
  

• GGML_HEXAGON_OPFILTER=regex Allows filtering (disabling) Ops that match the regex pattern:

  Examples:

  `GGML_HEXAGON_OPFILTER="FLASH_ATTN_EXT" llama-completion ...` - Disable Flash Attention on Hexagon (falls back to CPU or GPU)
  `GGML_HEXAGON_OPFILTER="ADD\|SUB" llama-completion ...` - Disable ADD and SUB on Hexagon (fall back to CPU or GPU)