ggml-org--llama.cpp/docs/backend/ET.md

llama.cpp for ET

• Background
• Limitations
• Build
• Develop
• Roadmap

Background

ET is a llama.cpp backend targeting the fully open source manycore RISC-V accelerator platform ET-SOC.

Limitations

The ET backend runs several of the major OSS models with some limitations:

• Only limited set of operations is supported (check ../ops.md and ../ops/ET.csv).
• Only q8_0, q4_0 (and partially fp16, q4_K) quantization is supported.
• Only one llama.cpp instance can use device at the same time (current firmware limitation).
• Limited (but working) MoE model support

As a result of the above, only select models can run fully on ET-SOC (you can actually run any model llama.cpp supports, but some/most operations will likely fallback to CPU backend).

Fully supported models:

• Qwen3 models (without MoE), e.g. ggml-org/Qwen3-0.6B-GGUF:q8_0 or ggml-org/Qwen3-14B-GGUF:q8_0.
• Llama3.2 (1B/3B), e.g. lmstudio-community/Llama-3.2-1B-Instruct-GGUF:q8_0.
• SmolLM2, e.g. unsloth/SmolLM2-135M-Instruct-GGUF:q8_0
• Llama 3.1 model family.
• RWKV v7 model family.
• TinyLLaMA

Build

I. Prerequisites

1. Install custom RISC-V toolchain - Follow instructions at: https://github.com/aifoundry-org/riscv-gnu-toolchain/tree/et/aifoundry

2. Install ET platform - Follow instructions at: https://github.com/aifoundry-org/et-platform

Both should be installed to /opt/et (or set ET_TOOLCHAIN and ET_PLATFORM environment variables accordingly).

# Set toolchain and ET platform path (/opt/et is default)
export ET_TOOLCHAIN=/opt/et
export ET_PLATFORM=/opt/et

II. Build llama.cpp

Check out llama.cpp with ET backend (this should checkout et branch):

git clone https://github.com/aifoundry-org/llama.cpp
cd llama.cpp

Build:

cmake -B build -DGGML_ET=ON
cmake --build build --config Release
# Optionally:
# cmake --install build

Build targeting sysemu backend instead of physical hardware:

cmake -B build -DGGML_ET=ON -DGGML_ET_SYSEMU=ON
cmake --build build --config Release

III. Run

Run llama.cpp binaries as usual. (Of course, please make sure you have the ET-SOC device installed and kernel driver loaded).

llama-cli -m mymodel.gguf
# or
llama-server -hf ggml-org/Qwen3-8B-GGUF:q8_0

If you want to run llama.cpp binaries (e.g. llama-cli) inside docker container, you should let it access device files:

docker run \
    --device=/dev/et0_mgmt:/dev/et0_mgmt \
    --device=/dev/et0_ops:/dev/et0_ops \
    ...

Develop

Compute kernels are developed within ggml/src/ggml-et/et-kernels folder. Build is performed using custom RISC-V GNU toolchain and is managed by cmake. At the moment kernels are build as baremetal elf files, without standard lib or any other dependencies. All the yummy parts are written in inline assembler.

Most kernels are very naive with lots of low hanging fruits left:

Important

Several assembly instructions emmited by the compiler are not implemented in hardware and software emulation in firmware is not ready yet. Eventually firmware will transparently trap unimplemented instructions and will emulate them inside exception handler. Until then, kernel build process includes step that checks compiled kernels and fails if any unimplemented instructions are found. Problematic ones follow: FDIV.PI, FDIVU.PI, FREMU.PI, FREM.PI, FDIV.S, FDIV.PS, FSQRT.S, FSQRT.PS, FRSQ.PS, FSIN.PS and (long cast) FCVT.S.L, FCVT.S.LU, FCVT.L.S, FCVT.LU.S What this means, is that for now you should avoid doing any division involving floats, any trigonometry or casting longs into floats. Some workarounds are implemented in math_fp.h (et_fdiv, et_powf etc) and long casting (presuming longs are small enough to fit into 32bits) can be done via int like a = (float)(int)(b).

Tip

There are some slightly higher level helpers (abstracting more complex instructions like tensor extension or synchronization primitives) inside et_platform, directory et-common-libs/include/etsoc/isa/. It was originally developed for firmware needs and is not included into compute kernel build process. Feel free to take ideas/code from there or try linking it in.

Before commiting any changes to operations and/or kernels, don't forget to update supported ops reports (instructions at docs/ops.md).

When logging is enabled (e.g. by setting --log-file cli param), each compute kernel run outputs a line with pipe-delimited key-value pairs containing kernel level performance infomation. Line is prefixed with ET_PERF:

ET_PERF|op=MUL_MAT|kernel=mul_mat_f32_Q8_0xf32|duration_us=3112|tensor=Qcur-0|shape=[4096,2,1,1]|start_us=48437862009|end_us=48437865121|flops=67100672
ET_PERF|op=ROPE|kernel=rope_f32|duration_us=9266|tensor=Qcur-0|shape=[128,32,2,1]|start_us=48437865128|end_us=48437874394|mode=0x0|n_dims=128|freq_base=500000.00|freq_scale=1.00

Keys depend on the operation, but some are always present. flops in this case counts effective floating point operations and not floating point operations per second.

You can enable ET-SOC runtime level ET-SOC profiling by setting environment variable GGML_ET_PROFILE to a path. Profiling/tracing results will be written to GGML_ET_PROFILE/et_runtime_trace.json and GGML_ET_PROFILE/kernel_map on exit.

Uberkernel

The in-knernel implementaiton of device dispatch/kernel fusion. The ET SDK has a non-trivial op-to-op gap. Uberkernel (name taken from the original Esperanto AI's compiler) dispatches multiple already existing kernel implementations with device side synchronization. Due to the processor's design, there is no natural memory visibility horizon between sub-kernel invocations. This makes uberkernel much more difficult to develop and debug. Currently Uberkerel is hidden begind the GGML_ET_UBERKERNEL environment variable and is disabled by default. Setting it to 1 enables it and provides significant performance improvements but is only validated for the LLaMA 3.2 model family and Qwen 3.5.

Roadmap

As of writing the documentation the ET backend is capable of running most models and smaller ones at usable speed given the low power profile of the processor. We'd address the following capabilities in the future:

• Enable Uberkernel for all models
• More oprtator support
• Better TTS model support
• Enable more quantization format support