Install this version:
emerge -a =sci-misc/kvmem-llama-cpp-0.19.1
If this version is masked, you can unmask it using the autounmask tool or standard emerge options:
autounmask =sci-misc/kvmem-llama-cpp-0.19.1
Or alternatively:
emerge --autounmask-write -a =sci-misc/kvmem-llama-cpp-0.19.1
| Version | EAPI | Keywords | Slot |
|---|---|---|---|
| 0.19.1 | 8 | ~amd64 | 0 |
# Copyright 2026 Gentoo Authors
# Distributed under the terms of the GNU General Public License v2
EAPI=8
inherit cmake cuda toolchain-funcs
# llama.cpp commit pinned by upstream in backends/versions.json, with the ordered
# patch series that file also records (backends/patches/llamacpp-*.patch).
LLAMA_COMMIT="620d2225cc436ab0b001e3200e590e3b286ef9ab"
# The series has to be applied in this order: each patch declares the ones it
# requires, and upstream's prepare-backends.py rejects a different sequence.
LLAMA_PATCHES=(
llamacpp-kvmem
llamacpp-pq2
llamacpp-ptq1
llamacpp-ptq1-performance
llamacpp-checkpoint-policy
)
DESCRIPTION="CUDA llama.cpp runtime with tiered KV memory for long contexts"
HOMEPAGE="https://github.com/kvmem/kvmem"
SRC_URI="
https://github.com/kvmem/kvmem/archive/refs/tags/v${PV}.tar.gz -> ${P}.tar.gz
https://github.com/ggml-org/llama.cpp/archive/${LLAMA_COMMIT}.tar.gz -> llama.cpp-${LLAMA_COMMIT}.tar.gz
"
S="${WORKDIR}/kvmem-${PV}"
LICENSE="Apache-2.0 MIT"
SLOT="0"
KEYWORDS="~amd64"
IUSE="cpu_flags_x86_avx cpu_flags_x86_avx2 cpu_flags_x86_bmi2
cpu_flags_x86_f16c cpu_flags_x86_fma3 cpu_flags_x86_sse4_2 +openmp test"
RESTRICT="!test? ( test )"
# Upstream requires nvcc >= 13.2.86 (CUDA 13.2 Update 2) for IQ3 correctness.
RDEPEND="
>=dev-util/nvidia-cuda-toolkit-13.2.2:=
openmp? ( llvm-runtimes/openmp:= )
"
DEPEND="${RDEPEND}"
RDEPEND+=" x11-drivers/nvidia-drivers"
BDEPEND=">=dev-util/nvidia-cuda-toolkit-13.2.2"
PATCHES=(
"${FILESDIR}/${P}-cxxflags.patch"
"${FILESDIR}/${P}-nvml-deprecated.patch"
)
pkg_pretend() {
use openmp && tc-check-openmp
}
pkg_setup() {
use openmp && tc-check-openmp
}
src_prepare() {
# The llama.cpp backend lives in backends/llamacpp, an empty submodule
# directory in the release tarball. Upstream's scripts/prepare-backends.py
# needs the git metadata of that submodule to verify its patch series, which
# a tarball build does not have, so the pinned tree is unpacked here and the
# same series, in the same order, is applied with eapply.
rmdir backends/llamacpp || die
mv "${WORKDIR}/llama.cpp-${LLAMA_COMMIT}" backends/llamacpp || die
pushd backends/llamacpp >/dev/null || die
local patch
for patch in "${LLAMA_PATCHES[@]}"; do
eapply "${S}/backends/patches/${patch}.patch"
done
popd >/dev/null || die
cuda_src_prepare
cmake_src_prepare
}
src_configure() {
local mycmakeargs=(
-DBUILD_SHARED_LIBS=OFF
-DKVMEM_BUILD_LLAMA=ON
-DGGML_CUDA=ON
-DGGML_NATIVE=OFF
-DGGML_AVX=$(usex cpu_flags_x86_avx)
-DGGML_AVX2=$(usex cpu_flags_x86_avx2)
-DGGML_BMI2=$(usex cpu_flags_x86_bmi2)
-DGGML_F16C=$(usex cpu_flags_x86_f16c)
-DGGML_FMA=$(usex cpu_flags_x86_fma3)
-DGGML_SSE42=$(usex cpu_flags_x86_sse4_2)
-DGGML_CCACHE=OFF
-DGGML_OPENMP=$(usex openmp)
-DGGML_VULKAN=OFF
-DLLAMA_OPENSSL=OFF
# The server links mtmd, which llama.cpp defines under tools/.
-DLLAMA_BUILD_TOOLS=ON
)
# llama.cpp's own server target stays off: upstream forces it off in its
# directory, and the two tools installed below are the only entry points
# this package offers.
if [[ -n ${CMAKE_CUDA_ARCHITECTURES} ]]; then
mycmakeargs+=( -DCMAKE_CUDA_ARCHITECTURES="${CMAKE_CUDA_ARCHITECTURES}" )
fi
local -x CUDAHOSTCXX="$(cuda_gccdir)"
cuda_add_sandbox
addpredict /dev/char/
cmake_src_configure
}
src_compile() {
# Build only the runtime tools; upstream also defines model-dependent tests.
cmake_src_compile llama-kvmem-cli llama-kvmem-server
}
src_test() {
# Model-free host-memory tests; GPU/MTP tests need separately supplied GGUFs.
# The NVMe store has been retired upstream (docs/legacy-ssd-retirement), so
# its test target is gone; the shared RAM/SSD tiers are covered by
# host_kv_storage_test and tiered_kv_storage_test.
local targets=(
kvmem_store_test pinned_kv_tier_test host_kv_storage_test
tiered_kv_storage_test kvmem_runtime_test raw_kv_store_test
)
cmake_src_compile "${targets[@]}"
local pattern
pattern=$(IFS='|'; echo "${targets[*]}")
ctest --test-dir "${BUILD_DIR}" --output-on-failure -R "^(${pattern})$" || die
}
src_install() {
# Keep patched llama/ggml private and avoid installing their SDK or tools.
dobin "${BUILD_DIR}/bin/llama-kvmem-cli" "${BUILD_DIR}/bin/llama-kvmem-server"
dodoc README.md VERSION
newdoc backends/llamacpp/LICENSE LICENSE.llama.cpp
dodoc "${FILESDIR}/README.gentoo"
}
Manage flags for this package:
euse -i <flag> -p sci-misc/kvmem-llama-cpp |
euse -E <flag> -p sci-misc/kvmem-llama-cpp |
euse -D <flag> -p sci-misc/kvmem-llama-cpp
>=dev-util/nvidia-cuda-toolkit-13.2.2:= openmp? ( llvm-runtimes/openmp:= )
>=dev-util/nvidia-cuda-toolkit-13.2.2:= openmp? ( llvm-runtimes/openmp:= ) x11-drivers/nvidia-drivers
>=dev-util/nvidia-cuda-toolkit-13.2.2
| Type | File | Size | Source URLs |
|---|---|---|---|
| DIST | kvmem-llama-cpp-0.19.1.tar.gz | 6349568 bytes | https://github.com/kvmem/kvmem/archive/refs/tags/v0.19.1.tar.gz |
| DIST | llama.cpp-620d2225cc436ab0b001e3200e590e3b286ef9ab.tar.gz | 38195486 bytes | https://github.com/ggml-org/llama.cpp/archive/620d2225cc436ab0b001e3200e590e3b286ef9ab.tar.gz |