Install this version:
emerge -a =sci-ml/ollama-0.30.10
If this version is masked, you can unmask it using the autounmask tool or standard emerge options:
autounmask =sci-ml/ollama-0.30.10
Or alternatively:
emerge --autounmask-write -a =sci-ml/ollama-0.30.10
| Version | EAPI | Keywords | Slot |
|---|---|---|---|
| 0.30.10 | 8 | ~amd64 | 0 |
# Copyright 1999-2026 Gentoo Authors
# Distributed under the terms of the GNU General Public License v2
EAPI=8
# supports ROCM/HIP >=5.5, but we define 7.0 to match the rest of the overlay
ROCM_VERSION="7.0"
inherit cuda rocm cmake flag-o-matic go-module linux-info systemd
# Upstream's CMake superbuild (cmake/local.cmake) builds the GGML/llama.cpp
# inference backends by fetching a pinned llama.cpp via ExternalProject /
# FetchContent. The pinned commit lives in the LLAMA_CPP_VERSION file in the
# ollama source tree; we prestage that exact tree as a distfile and point the
# build at it through FETCHCONTENT_SOURCE_DIR_LLAMA_CPP so the build never
# touches the network. Re-check on every bump: `cat LLAMA_CPP_VERSION` in the
# matching ollama tag. verified 2026-06-18 (ollama 0.30.10 -> llama.cpp b9672).
LLAMACPP_COMMIT="b9672"
DESCRIPTION="Get up and running with Llama 3, Mistral, Gemma, and other language models"
HOMEPAGE="https://ollama.com"
MY_PV="${PV/_rc/-rc}"
MY_P="${PN}-${MY_PV}"
SRC_URI="
https://github.com/ollama/${PN}/archive/refs/tags/v${MY_PV}.tar.gz -> ${MY_P}.gh.tar.gz
https://github.com/gentoo-golang-dist/${PN}/releases/download/v${MY_PV}/${MY_P}-deps.tar.xz
https://github.com/ggml-org/llama.cpp/archive/refs/tags/${LLAMACPP_COMMIT}.tar.gz
-> ${PN}-llama.cpp-${LLAMACPP_COMMIT}.tar.gz
"
S="${WORKDIR}/${PN}-${MY_PV}"
LLAMACPP_S="${WORKDIR}/llama.cpp-${LLAMACPP_COMMIT}"
LICENSE="MIT"
SLOT="0"
KEYWORDS="~amd64"
# cuda -> cuda_v13 llama-server backend (this overlay tracks CUDA 13.x;
# cuda_v12 is intentionally not wired).
# rocm -> rocm_v7_2 (Linux) llama-server backend.
# vulkan-> vulkan llama-server backend.
# The CPU backend (all microarch variants) is always built and the right one
# is dlopen'd at runtime, so no cpu_flags_x86 USE flags are needed.
IUSE="cuda rocm vulkan"
# Upstream tests pull models from the network; the dependency tarball is a
# GitHub release asset that must not be mirrored.
RESTRICT="mirror test"
CDEPEND="
cuda? ( dev-util/nvidia-cuda-toolkit:= )
rocm? (
>=dev-util/hip-${ROCM_VERSION}:=
>=sci-libs/hipBLAS-${ROCM_VERSION}:=
>=sci-libs/rocBLAS-${ROCM_VERSION}:=
)
"
DEPEND="${CDEPEND}"
BDEPEND="
>=dev-lang/go-1.26.0
vulkan? (
dev-util/vulkan-headers
media-libs/shaderc
)
"
RDEPEND="
${CDEPEND}
acct-group/${PN}
>=acct-user/${PN}-3[cuda?]
vulkan? ( media-libs/vulkan-loader )
"
PATCHES=(
"${FILESDIR}/${P}-unbundle-gpu-runtime-libs.patch"
"${FILESDIR}/${P}-rocm-no-parallel-jobs.patch"
)
pkg_setup() {
if use rocm; then
linux-info_pkg_setup
if linux-info_get_any_version && linux_config_exists; then
if ! linux_chkconfig_present HSA_AMD_SVM; then
ewarn "To use ROCm/HIP, you need to have HSA_AMD_SVM option enabled in your kernel."
fi
fi
fi
}
src_unpack() {
if use rocm; then
# ROCm/HIP rejects some LTO flags; filter before the Go env captures
# them into CGO_*. 963401
strip-unsupported-flags
export CXXFLAGS="$(test-flags-HIPCXX "${CXXFLAGS}")"
fi
# Unpacks the ollama source, the prestaged llama.cpp tree, and the Go
# dependency tarball (the latter into GOMODCACHE).
go-module_src_unpack
}
src_prepare() {
cmake_src_prepare
# The compat layer (llama/compat) links Ollama-owned sources into the
# llama.cpp targets AND patches call-sites into llama.cpp itself. Upstream
# applies that patch set only when it fetches llama.cpp; because we prestage
# the source and pass FETCHCONTENT_SOURCE_DIR_LLAMA_CPP (which forces
# OLLAMA_LLAMA_CPP_SKIP_COMPAT_PATCH=ON in the sub-build), we must apply it
# ourselves. Mirror apply-patch.cmake: every llama/compat/**/*.patch, in
# basename-numeric order (001-hooks, 002-ui, models/003-laguna, ...). The
# models/*.patch add not-yet-upstreamed architectures whose .cpp are linked
# into the llama target, so the whole set is required — a non-recursive glob
# silently drops them and the linked sources then fail to compile.
local _compat_patch
pushd "${LLAMACPP_S}" >/dev/null || die
while IFS= read -r _compat_patch; do
eapply "${_compat_patch}"
done < <(find "${S}"/llama/compat -name '*.patch' -printf '%f\t%p\n' | sort | cut -f2-)
popd >/dev/null || die
# The Go binary resolves its runtime payload at exeDir/../lib/ollama
# (ml/path.go). We install to $(get_libdir)/ollama (lib64 on multilib), so
# teach the binary to look there. No-op on lib (32-bit) layouts.
sed -i -e "s/\"lib\", \"ollama\"/\"$(get_libdir)\", \"ollama\"/g" \
ml/path.go || die "libdir sed failed"
}
src_configure() {
local backends=()
use cuda && backends+=( cuda_v13 )
use rocm && backends+=( rocm_v7_2 )
use vulkan && backends+=( vulkan )
local mycmakeargs=(
-DOLLAMA_VERSION="${PV}"
-DOLLAMA_LIB_DIR="$(get_libdir)/ollama"
# Prestaged llama.cpp source; no network FetchContent.
-DFETCHCONTENT_SOURCE_DIR_LLAMA_CPP="${LLAMACPP_S}"
-DGGML_CCACHE=OFF
-DOLLAMA_LLAMA_BACKENDS="$(IFS=';'; echo "${backends[*]}")"
)
if use rocm; then
# Forward the configured GPU arch(s); the superbuild then selects the
# rocm_v7_2_user_arch preset and passes AMDGPU_TARGETS down to ggml-hip.
mycmakeargs+=( -DAMDGPU_TARGETS="$(get_amdgpu_flags)" )
fi
cmake_src_configure
}
src_compile() {
if use cuda; then
# nvcc rejects gcc newer than CUDA supports; cuda_gccdir picks a
# compatible slot. The CUDA backend is built by a nested CMake project
# (ExternalProject) during this phase, so the host-compiler choice and
# the device-node sandbox allowances must be in effect here, not in
# src_configure. CMake reads CUDAHOSTCXX into CMAKE_CUDA_HOST_COMPILER.
local -x CUDAHOSTCXX
CUDAHOSTCXX="$(cuda_gccdir)"
cuda_add_sandbox -w
addpredict "/dev/char/"
fi
if use rocm; then
# ggml-hip is built by a nested CMake project that uses
# enable_language(HIP); CMake needs the ROCm clang as the HIP compiler
# (the hipcc wrapper isn't recognized). Point HIPCXX at the clang ROCm
# itself uses, leaving CC/CXX as gcc for the CPU backend and Go cgo.
# Pre-seed the device list so the HIP compiler's GPU enumeration doesn't
# reach /dev/kfd inside the sandbox (no GPU present at build time).
local hipclangpath
hipclangpath="$(hipconfig --hipclangpath 2>/dev/null)" || die "hipconfig failed"
[[ -x ${hipclangpath}/clang++ ]] || die "ROCm clang not found at ${hipclangpath}"
local -x HIPCXX="${hipclangpath}/clang++"
local -x HIP_PATH="${ESYSROOT}/usr"
local -x ROCM_TARGET_LST="${T}/rocm_targets.lst"
printf '%s\n' "${AMDGPU_TARGETS[@]}" > "${ROCM_TARGET_LST}" || die
addpredict /dev/kfd
addpredict /dev/dri
fi
cmake_src_compile
}
src_install() {
# NB: not cmake_src_install. That runs `cmake --build --target install`,
# which re-enters the BUILD_ALWAYS llama-server ExternalProjects; since each
# carries an absolute CMAKE_INSTALL_PREFIX (the superbuild staging dir), the
# re-run drops a duplicate payload at ${D}/<buildpath> once DESTDIR is set.
# Running the top-level install script directly installs the Go binary and
# the already-staged lib/ollama payload without re-entering the sub-builds.
DESTDIR="${D}" cmake --install "${BUILD_DIR}" || die
newinitd "${FILESDIR}"/ollama.init "${PN}"
newconfd "${FILESDIR}"/ollama.confd "${PN}"
systemd_dounit "${FILESDIR}"/ollama.service
}
pkg_preinst() {
keepdir /var/log/ollama
fperms 750 /var/log/ollama
fowners "${PN}:${PN}" /var/log/ollama
}
pkg_postinst() {
if [[ -z ${REPLACING_VERSIONS} ]]; then
einfo "Quick guide:"
einfo " ollama serve"
einfo " ollama run llama3"
einfo
einfo "See available models at https://ollama.com/library"
fi
einfo
einfo "Ollama binds 127.0.0.1 port 11434 by default."
einfo "Change the bind address with the OLLAMA_HOST environment variable."
einfo "See https://docs.ollama.com/faq for more info"
einfo
if use cuda; then
einfo "USE=cuda builds the GPU backend for the GPU present at build time"
einfo "(CMAKE_CUDA_ARCHITECTURES defaults to 'native'). Set CUDAARCHS to"
einfo "override. The ${PN} user must be in the video group to see devices;"
einfo "acct-user/${PN}[cuda] arranges this."
fi
}
Manage flags for this package:
euse -i <flag> -p sci-ml/ollama |
euse -E <flag> -p sci-ml/ollama |
euse -D <flag> -p sci-ml/ollama
cuda? ( dev-util/nvidia-cuda-toolkit:= ) rocm? ( >=dev-util/hip-7.0:= >=sci-libs/hipBLAS-7.0:= >=sci-libs/rocBLAS-7.0:= )
cuda? ( dev-util/nvidia-cuda-toolkit:= ) rocm? ( >=dev-util/hip-7.0:= >=sci-libs/hipBLAS-7.0:= >=sci-libs/rocBLAS-7.0:= ) acct-group/ollama >=acct-user/ollama-3[cuda?] vulkan? ( media-libs/vulkan-loader )
>=dev-lang/go-1.26.0 vulkan? ( dev-util/vulkan-headers media-libs/shaderc )
| Type | File | Size | Source URLs |
|---|---|---|---|
| DIST | ollama-0.30.10-deps.tar.xz | 84642244 bytes | https://github.com/gentoo-golang-dist/ollama/releases/download/v0.30.10/ollama-0.30.10-deps.tar.xz |
| DIST | ollama-0.30.10.gh.tar.gz | 28877828 bytes | https://github.com/ollama/ollama/archive/refs/tags/v0.30.10.tar.gz |
| DIST | ollama-llama.cpp-b9672.tar.gz | 34886141 bytes | https://github.com/ggml-org/llama.cpp/archive/refs/tags/b9672.tar.gz |