Llama.cpp

Beschreibung

llama.cpp ist eine C/C++ Implementierung für Inference von Large Language Models (LLMs). Es unterstützt verschiedene Backends (CPU, Vulkan, ROCm, CUDA) und ermöglicht das Ausführen von quantisierten Modellen im GGUF-Format.

Download

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

Installation

Vulkan Build

Vulkan SDK herunterladen entpacken, ins Verzeichnis wechseln und source setup-env.sh ausführen. Dann ins Verzeichnis wechseln wo llama.cpp installiert werden soll. Dort dann

cmake -B build-vulkan -DGGML_VULKAN=1
cmake --build build-vulkan --config Release -- -j $(nproc)

ROCm Build (getestet mit ROCm 6.4.3 und 7.1.0

gfx906 = Mi 50 Support
gfx1100 = 7900 XTX Support

Copy & paste all the commands from the quick install https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/quick-start.html
Before rebooting to complete the install, download the 6.4 rocblas from the AUR: https://archlinux.org/packages/extra/x86_64/rocblas/
Extract it 
Copy all tensor files that contain gfx906 in rocblas-6.4.3-3-x86_64.pkg/opt/rocm/lib/rocblas/library to /opt/rocm/lib/rocblas/library
Reboot
Check if it worked by running sudo update-alternatives --display rocm

1. ROCm Umgebung einrichten (optional falls Fehler auftreten)

echo 'export LD_LIBRARY_PATH=/opt/rocm/lib:$LD_LIBRARY_PATH' >> ~/.bashrc
echo 'export HSA_OVERRIDE_GFX_VERSION=9.0.6' >> ~/.bashrc  # Für MI50/MI60
source ~/.bashrc

1. llama.cpp kompilieren

HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
    cmake -B build-rocm -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx906 -DGGML_HIP_ROCWMMA_FATTN=ON \
    -DCMAKE_BUILD_TYPE=Release
cmake --build build-rocm --config Release -- -j 8

Für mehrere GPU-Architekturen gleichzeitig:

# Für MI50 (gfx906) und RX 7900 XTX (gfx1100)
HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
    cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS="gfx906;gfx1100" \
    -DCMAKE_BUILD_TYPE=Release && \
    cmake --build build --config Release -- -j 8

Konfiguration

llama-server als systemd Service einrichten

Service-Datei erstellen:

sudo nano /etc/systemd/system/llama-server.service

Service-Datei Inhalt (Multi-GPU mit ROCm):

[Unit]
Description=Llama.cpp ROCm Multi-GPU Server
After=network.target

[Service]
Type=simple
User=username
Group=username
WorkingDirectory=/home/username/llama.cpp/build/bin

# Multi-GPU Konfiguration
Environment="HIP_VISIBLE_DEVICES=0,1"
Environment="HSA_OVERRIDE_GFX_VERSION=9.0.6"
Environment="PATH=/opt/rocm/bin:/usr/local/bin:/usr/bin:/bin"
Environment="LD_LIBRARY_PATH=/opt/rocm/lib"

# Server mit optimalen Multi-GPU Einstellungen
ExecStart=/home/username/llama.cpp/build/bin/llama-server \
  -m /home/username/models/model.gguf \
  --split-mode row \
  --tensor-split 0.5,0.5 \
  -ngl 99 \
  -fa 1 \
  --host 0.0.0.0 \
  --port 8080 \
  -c 32768 \
  -b 2048 \
  -ub 2048 \
  --threads 8 \
  --parallel 1 \
  --jinja

Restart=always
RestartSec=10
LimitNOFILE=65535
LimitMEMLOCK=infinity
StandardOutput=journal
StandardError=journal
SyslogIdentifier=llama-server

[Install]
WantedBy=multi-user.target

Service aktivieren und starten:

# Service neu laden
sudo systemctl daemon-reload

# Service aktivieren (Auto-Start beim Boot)
sudo systemctl enable llama-server

# Service starten
sudo systemctl start llama-server

# Status prüfen
sudo systemctl status llama-server

# Logs anzeigen
sudo journalctl -u llama-server -f

Service Management:

# Service stoppen
sudo systemctl stop llama-server

# Service neustarten
sudo systemctl restart llama-server

# Service deaktivieren
sudo systemctl disable llama-server

==== Manuelle Server-Starts ====
'''Multi-GPU (ROCm) - Optimal:'''
<syntaxhighlight lang="bash" line="1">
HIP_VISIBLE_DEVICES=0,1 ./llama-server \
  -m ~/models/model.gguf \
  --split-mode row \
  --tensor-split 0.5,0.5 \
  -ngl 99 \
  -fa 1 \
  --host 0.0.0.0 \
  --port 8080 \
  -c 32768 \
  -b 2048 \
  -ub 2048 \
  --threads 8 \
  --parallel 1 \
  --jinja

Update

cd ~/llama.cpp
git pull
cmake --build build --config Release -- -j $(nproc)

# Service neu starten falls aktiv
sudo systemctl restart llama-server

Befehlszeilenargumente

llama-server

-h,    --help, --usage                  print usage and exit
--version                               show version and build info
-cl,   --cache-list                     show list of models in cache
--completion-bash                       print source-able bash completion script for llama.cpp
--verbose-prompt                        print a verbose prompt before generation (default: false)
-t,    --threads N                      number of CPU threads to use during generation (default: -1)
                                        (env: LLAMA_ARG_THREADS)
-tb,   --threads-batch N                number of threads to use during batch and prompt processing (default:
                                        same as --threads)
-C,    --cpu-mask M                     CPU affinity mask: arbitrarily long hex. Complements cpu-range
                                        (default: "")
-Cr,   --cpu-range lo-hi                range of CPUs for affinity. Complements --cpu-mask
--cpu-strict <0|1>                      use strict CPU placement (default: 0)
--prio N                                set process/thread priority : low(-1), normal(0), medium(1), high(2),
                                        realtime(3) (default: 0)
--poll <0...100>                        use polling level to wait for work (0 - no polling, default: 50)
-Cb,   --cpu-mask-batch M               CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch
                                        (default: same as --cpu-mask)
-Crb,  --cpu-range-batch lo-hi          ranges of CPUs for affinity. Complements --cpu-mask-batch
--cpu-strict-batch <0|1>                use strict CPU placement (default: same as --cpu-strict)
--prio-batch N                          set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime
                                        (default: 0)
--poll-batch <0|1>                      use polling to wait for work (default: same as --poll)
-c,    --ctx-size N                     size of the prompt context (default: 4096, 0 = loaded from model)
                                        (env: LLAMA_ARG_CTX_SIZE)
-n,    --predict, --n-predict N         number of tokens to predict (default: -1, -1 = infinity)
                                        (env: LLAMA_ARG_N_PREDICT)
-b,    --batch-size N                   logical maximum batch size (default: 2048)
                                        (env: LLAMA_ARG_BATCH)
-ub,   --ubatch-size N                  physical maximum batch size (default: 512)
                                        (env: LLAMA_ARG_UBATCH)
--keep N                                number of tokens to keep from the initial prompt (default: 0, -1 =
                                        all)
--swa-full                              use full-size SWA cache (default: false)
                                        [(more
                                        info)](https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
                                        (env: LLAMA_ARG_SWA_FULL)
--kv-unified, -kvu                      use single unified KV buffer for the KV cache of all sequences
                                        (default: false)
                                        [(more info)](https://github.com/ggml-org/llama.cpp/pull/14363)
                                        (env: LLAMA_ARG_KV_SPLIT)
-fa,   --flash-attn [on|off|auto]       set Flash Attention use ('on', 'off', or 'auto', default: 'auto')
                                        (env: LLAMA_ARG_FLASH_ATTN)
--no-perf                               disable internal libllama performance timings (default: false)
                                        (env: LLAMA_ARG_NO_PERF)
-e,    --escape                         process escapes sequences (\n, \r, \t, \', \", \\) (default: true)
--no-escape                             do not process escape sequences
--rope-scaling {none,linear,yarn}       RoPE frequency scaling method, defaults to linear unless specified by
                                        the model
                                        (env: LLAMA_ARG_ROPE_SCALING_TYPE)
--rope-scale N                          RoPE context scaling factor, expands context by a factor of N
                                        (env: LLAMA_ARG_ROPE_SCALE)
--rope-freq-base N                      RoPE base frequency, used by NTK-aware scaling (default: loaded from
                                        model)
                                        (env: LLAMA_ARG_ROPE_FREQ_BASE)
--rope-freq-scale N                     RoPE frequency scaling factor, expands context by a factor of 1/N
                                        (env: LLAMA_ARG_ROPE_FREQ_SCALE)
--yarn-orig-ctx N                       YaRN: original context size of model (default: 0 = model training
                                        context size)
                                        (env: LLAMA_ARG_YARN_ORIG_CTX)
--yarn-ext-factor N                     YaRN: extrapolation mix factor (default: -1.0, 0.0 = full
                                        interpolation)
                                        (env: LLAMA_ARG_YARN_EXT_FACTOR)
--yarn-attn-factor N                    YaRN: scale sqrt(t) or attention magnitude (default: -1.0)
                                        (env: LLAMA_ARG_YARN_ATTN_FACTOR)
--yarn-beta-slow N                      YaRN: high correction dim or alpha (default: -1.0)
                                        (env: LLAMA_ARG_YARN_BETA_SLOW)
--yarn-beta-fast N                      YaRN: low correction dim or beta (default: -1.0)
                                        (env: LLAMA_ARG_YARN_BETA_FAST)
-nkvo, --no-kv-offload                  disable KV offload
                                        (env: LLAMA_ARG_NO_KV_OFFLOAD)
-nr,   --no-repack                      disable weight repacking
                                        (env: LLAMA_ARG_NO_REPACK)
--no-host                               bypass host buffer allowing extra buffers to be used
                                        (env: LLAMA_ARG_NO_HOST)
-ctk,  --cache-type-k TYPE              KV cache data type for K
                                        allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
                                        (default: f16)
                                        (env: LLAMA_ARG_CACHE_TYPE_K)
-ctv,  --cache-type-v TYPE              KV cache data type for V
                                        allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
                                        (default: f16)
                                        (env: LLAMA_ARG_CACHE_TYPE_V)
-dt,   --defrag-thold N                 KV cache defragmentation threshold (DEPRECATED)
                                        (env: LLAMA_ARG_DEFRAG_THOLD)
-np,   --parallel N                     number of parallel sequences to decode (default: 1)
                                        (env: LLAMA_ARG_N_PARALLEL)
--mlock                                 force system to keep model in RAM rather than swapping or compressing
                                        (env: LLAMA_ARG_MLOCK)
--no-mmap                               do not memory-map model (slower load but may reduce pageouts if not
                                        using mlock)
                                        (env: LLAMA_ARG_NO_MMAP)
--numa TYPE                             attempt optimizations that help on some NUMA systems
                                        - distribute: spread execution evenly over all nodes
                                        - isolate: only spawn threads on CPUs on the node that execution
                                        started on
                                        - numactl: use the CPU map provided by numactl
                                        if run without this previously, it is recommended to drop the system
                                        page cache before using this
                                        see https://github.com/ggml-org/llama.cpp/issues/1437
                                        (env: LLAMA_ARG_NUMA)
-dev,  --device <dev1,dev2,..>          comma-separated list of devices to use for offloading (none = don't
                                        offload)
                                        use --list-devices to see a list of available devices
                                        (env: LLAMA_ARG_DEVICE)
--list-devices                          print list of available devices and exit
--override-tensor, -ot <tensor name pattern>=<buffer type>,...
                                        override tensor buffer type
--cpu-moe, -cmoe                        keep all Mixture of Experts (MoE) weights in the CPU
                                        (env: LLAMA_ARG_CPU_MOE)
--n-cpu-moe, -ncmoe N                   keep the Mixture of Experts (MoE) weights of the first N layers in the
                                        CPU
                                        (env: LLAMA_ARG_N_CPU_MOE)
-ngl,  --gpu-layers, --n-gpu-layers N   max. number of layers to store in VRAM (default: -1)
                                        (env: LLAMA_ARG_N_GPU_LAYERS)
-sm,   --split-mode {none,layer,row}    how to split the model across multiple GPUs, one of:
                                        - none: use one GPU only
                                        - layer (default): split layers and KV across GPUs
                                        - row: split rows across GPUs
                                        (env: LLAMA_ARG_SPLIT_MODE)
-ts,   --tensor-split N0,N1,N2,...      fraction of the model to offload to each GPU, comma-separated list of
                                        proportions, e.g. 3,1
                                        (env: LLAMA_ARG_TENSOR_SPLIT)
-mg,   --main-gpu INDEX                 the GPU to use for the model (with split-mode = none), or for
                                        intermediate results and KV (with split-mode = row) (default: 0)
                                        (env: LLAMA_ARG_MAIN_GPU)
--check-tensors                         check model tensor data for invalid values (default: false)
--override-kv KEY=TYPE:VALUE            advanced option to override model metadata by key. may be specified
                                        multiple times.
                                        types: int, float, bool, str. example: --override-kv
                                        tokenizer.ggml.add_bos_token=bool:false
--no-op-offload                         disable offloading host tensor operations to device (default: false)
--lora FNAME                            path to LoRA adapter (can be repeated to use multiple adapters)
--lora-scaled FNAME SCALE               path to LoRA adapter with user defined scaling (can be repeated to use
                                        multiple adapters)
--control-vector FNAME                  add a control vector
                                        note: this argument can be repeated to add multiple control vectors
--control-vector-scaled FNAME SCALE     add a control vector with user defined scaling SCALE
                                        note: this argument can be repeated to add multiple scaled control
                                        vectors
--control-vector-layer-range START END
                                        layer range to apply the control vector(s) to, start and end inclusive
-m,    --model FNAME                    model path (default: `models/$filename` with filename from `--hf-file`
                                        or `--model-url` if set, otherwise models/7B/ggml-model-f16.gguf)
                                        (env: LLAMA_ARG_MODEL)
-mu,   --model-url MODEL_URL            model download url (default: unused)
                                        (env: LLAMA_ARG_MODEL_URL)
-dr,   --docker-repo [<repo>/]<model>[:quant]
                                        Docker Hub model repository. repo is optional, default to ai/. quant
                                        is optional, default to :latest.
                                        example: gemma3
                                        (default: unused)
                                        (env: LLAMA_ARG_DOCKER_REPO)
-hf,   -hfr, --hf-repo <user>/<model>[:quant]
                                        Hugging Face model repository; quant is optional, case-insensitive,
                                        default to Q4_K_M, or falls back to the first file in the repo if
                                        Q4_K_M doesn't exist.
                                        mmproj is also downloaded automatically if available. to disable, add
                                        --no-mmproj
                                        example: unsloth/phi-4-GGUF:q4_k_m
                                        (default: unused)
                                        (env: LLAMA_ARG_HF_REPO)
-hfd,  -hfrd, --hf-repo-draft <user>/<model>[:quant]
                                        Same as --hf-repo, but for the draft model (default: unused)
                                        (env: LLAMA_ARG_HFD_REPO)
-hff,  --hf-file FILE                   Hugging Face model file. If specified, it will override the quant in
                                        --hf-repo (default: unused)
                                        (env: LLAMA_ARG_HF_FILE)
-hfv,  -hfrv, --hf-repo-v <user>/<model>[:quant]
                                        Hugging Face model repository for the vocoder model (default: unused)
                                        (env: LLAMA_ARG_HF_REPO_V)
-hffv, --hf-file-v FILE                 Hugging Face model file for the vocoder model (default: unused)
                                        (env: LLAMA_ARG_HF_FILE_V)
-hft,  --hf-token TOKEN                 Hugging Face access token (default: value from HF_TOKEN environment
                                        variable)
                                        (env: HF_TOKEN)
--log-disable                           Log disable
--log-file FNAME                        Log to file
--log-colors [on|off|auto]              Set colored logging ('on', 'off', or 'auto', default: 'auto')
                                        'auto' enables colors when output is to a terminal
                                        (env: LLAMA_LOG_COLORS)
-v,    --verbose, --log-verbose         Set verbosity level to infinity (i.e. log all messages, useful for
                                        debugging)
--offline                               Offline mode: forces use of cache, prevents network access
                                        (env: LLAMA_OFFLINE)
-lv,   --verbosity, --log-verbosity N   Set the verbosity threshold. Messages with a higher verbosity will be
                                        ignored.
                                        (env: LLAMA_LOG_VERBOSITY)
--log-prefix                            Enable prefix in log messages
                                        (env: LLAMA_LOG_PREFIX)
--log-timestamps                        Enable timestamps in log messages
                                        (env: LLAMA_LOG_TIMESTAMPS)
-ctkd, --cache-type-k-draft TYPE        KV cache data type for K for the draft model
                                        allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
                                        (default: f16)
                                        (env: LLAMA_ARG_CACHE_TYPE_K_DRAFT)
-ctvd, --cache-type-v-draft TYPE        KV cache data type for V for the draft model
                                        allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
                                        (default: f16)
                                        (env: LLAMA_ARG_CACHE_TYPE_V_DRAFT)


----- sampling params -----

--samplers SAMPLERS                     samplers that will be used for generation in the order, separated by
                                        ';'
                                        (default:
                                        penalties;dry;top_n_sigma;top_k;typ_p;top_p;min_p;xtc;temperature)
-s,    --seed SEED                      RNG seed (default: -1, use random seed for -1)
--sampling-seq, --sampler-seq SEQUENCE
                                        simplified sequence for samplers that will be used (default:
                                        edskypmxt)
--ignore-eos                            ignore end of stream token and continue generating (implies
                                        --logit-bias EOS-inf)
--temp N                                temperature (default: 0.8)
--top-k N                               top-k sampling (default: 40, 0 = disabled)
--top-p N                               top-p sampling (default: 0.9, 1.0 = disabled)
--min-p N                               min-p sampling (default: 0.1, 0.0 = disabled)
--top-nsigma N                          top-n-sigma sampling (default: -1.0, -1.0 = disabled)
--xtc-probability N                     xtc probability (default: 0.0, 0.0 = disabled)
--xtc-threshold N                       xtc threshold (default: 0.1, 1.0 = disabled)
--typical N                             locally typical sampling, parameter p (default: 1.0, 1.0 = disabled)
--repeat-last-n N                       last n tokens to consider for penalize (default: 64, 0 = disabled, -1
                                        = ctx_size)
--repeat-penalty N                      penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled)
--presence-penalty N                    repeat alpha presence penalty (default: 0.0, 0.0 = disabled)
--frequency-penalty N                   repeat alpha frequency penalty (default: 0.0, 0.0 = disabled)
--dry-multiplier N                      set DRY sampling multiplier (default: 0.0, 0.0 = disabled)
--dry-base N                            set DRY sampling base value (default: 1.75)
--dry-allowed-length N                  set allowed length for DRY sampling (default: 2)
--dry-penalty-last-n N                  set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 =
                                        context size)
--dry-sequence-breaker STRING           add sequence breaker for DRY sampling, clearing out default breakers
                                        ('\n', ':', '"', '*') in the process; use "none" to not use any
                                        sequence breakers
--dynatemp-range N                      dynamic temperature range (default: 0.0, 0.0 = disabled)
--dynatemp-exp N                        dynamic temperature exponent (default: 1.0)
--mirostat N                            use Mirostat sampling.
                                        Top K, Nucleus and Locally Typical samplers are ignored if used.
                                        (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)
--mirostat-lr N                         Mirostat learning rate, parameter eta (default: 0.1)
--mirostat-ent N                        Mirostat target entropy, parameter tau (default: 5.0)
-l,    --logit-bias TOKEN_ID(+/-)BIAS   modifies the likelihood of token appearing in the completion,
                                        i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',
                                        or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'
--grammar GRAMMAR                       BNF-like grammar to constrain generations (see samples in grammars/
                                        dir) (default: '')
--grammar-file FNAME                    file to read grammar from
-j,    --json-schema SCHEMA             JSON schema to constrain generations (https://json-schema.org/), e.g.
                                        `{}` for any JSON object
                                        For schemas w/ external $refs, use --grammar +
                                        example/json_schema_to_grammar.py instead
-jf,   --json-schema-file FILE          File containing a JSON schema to constrain generations
                                        (https://json-schema.org/), e.g. `{}` for any JSON object
                                        For schemas w/ external $refs, use --grammar +
                                        example/json_schema_to_grammar.py instead


----- example-specific params -----

--ctx-checkpoints, --swa-checkpoints N
                                        max number of context checkpoints to create per slot (default: 8)
                                        [(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)
                                        (env: LLAMA_ARG_CTX_CHECKPOINTS)
--cache-ram, -cram N                    set the maximum cache size in MiB (default: 8192, -1 - no limit, 0 -
                                        disable)
                                        [(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)
                                        (env: LLAMA_ARG_CACHE_RAM)
--no-context-shift                      disables context shift on infinite text generation (default: enabled)
                                        (env: LLAMA_ARG_NO_CONTEXT_SHIFT)
--context-shift                         enables context shift on infinite text generation (default: disabled)
                                        (env: LLAMA_ARG_CONTEXT_SHIFT)
-r,    --reverse-prompt PROMPT          halt generation at PROMPT, return control in interactive mode
-sp,   --special                        special tokens output enabled (default: false)
--no-warmup                             skip warming up the model with an empty run
--spm-infill                            use Suffix/Prefix/Middle pattern for infill (instead of
                                        Prefix/Suffix/Middle) as some models prefer this. (default: disabled)
--pooling {none,mean,cls,last,rank}     pooling type for embeddings, use model default if unspecified
                                        (env: LLAMA_ARG_POOLING)
-cb,   --cont-batching                  enable continuous batching (a.k.a dynamic batching) (default: enabled)
                                        (env: LLAMA_ARG_CONT_BATCHING)
-nocb, --no-cont-batching               disable continuous batching
                                        (env: LLAMA_ARG_NO_CONT_BATCHING)
--mmproj FILE                           path to a multimodal projector file. see tools/mtmd/README.md
                                        note: if -hf is used, this argument can be omitted
                                        (env: LLAMA_ARG_MMPROJ)
--mmproj-url URL                        URL to a multimodal projector file. see tools/mtmd/README.md
                                        (env: LLAMA_ARG_MMPROJ_URL)
--no-mmproj                             explicitly disable multimodal projector, useful when using -hf
                                        (env: LLAMA_ARG_NO_MMPROJ)
--no-mmproj-offload                     do not offload multimodal projector to GPU
                                        (env: LLAMA_ARG_NO_MMPROJ_OFFLOAD)
--image-min-tokens N                    minimum number of tokens each image can take, only used by vision
                                        models with dynamic resolution (default: read from model)
                                        (env: LLAMA_ARG_IMAGE_MIN_TOKENS)
--image-max-tokens N                    maximum number of tokens each image can take, only used by vision
                                        models with dynamic resolution (default: read from model)
                                        (env: LLAMA_ARG_IMAGE_MAX_TOKENS)
--override-tensor-draft, -otd <tensor name pattern>=<buffer type>,...
                                        override tensor buffer type for draft model
--cpu-moe-draft, -cmoed                 keep all Mixture of Experts (MoE) weights in the CPU for the draft
                                        model
                                        (env: LLAMA_ARG_CPU_MOE_DRAFT)
--n-cpu-moe-draft, -ncmoed N            keep the Mixture of Experts (MoE) weights of the first N layers in the
                                        CPU for the draft model
                                        (env: LLAMA_ARG_N_CPU_MOE_DRAFT)
-a,    --alias STRING                   set alias for model name (to be used by REST API)
                                        (env: LLAMA_ARG_ALIAS)
--host HOST                             ip address to listen, or bind to an UNIX socket if the address ends
                                        with .sock (default: 127.0.0.1)
                                        (env: LLAMA_ARG_HOST)
--port PORT                             port to listen (default: 8080)
                                        (env: LLAMA_ARG_PORT)
--path PATH                             path to serve static files from (default: )
                                        (env: LLAMA_ARG_STATIC_PATH)
--api-prefix PREFIX                     prefix path the server serves from, without the trailing slash
                                        (default: )
                                        (env: LLAMA_ARG_API_PREFIX)
--no-webui                              Disable the Web UI (default: enabled)
                                        (env: LLAMA_ARG_NO_WEBUI)
--embedding, --embeddings               restrict to only support embedding use case; use only with dedicated
                                        embedding models (default: disabled)
                                        (env: LLAMA_ARG_EMBEDDINGS)
--reranking, --rerank                   enable reranking endpoint on server (default: disabled)
                                        (env: LLAMA_ARG_RERANKING)
--api-key KEY                           API key to use for authentication (default: none)
                                        (env: LLAMA_API_KEY)
--api-key-file FNAME                    path to file containing API keys (default: none)
--ssl-key-file FNAME                    path to file a PEM-encoded SSL private key
                                        (env: LLAMA_ARG_SSL_KEY_FILE)
--ssl-cert-file FNAME                   path to file a PEM-encoded SSL certificate
                                        (env: LLAMA_ARG_SSL_CERT_FILE)
--chat-template-kwargs STRING           sets additional params for the json template parser
                                        (env: LLAMA_CHAT_TEMPLATE_KWARGS)
-to,   --timeout N                      server read/write timeout in seconds (default: 600)
                                        (env: LLAMA_ARG_TIMEOUT)
--threads-http N                        number of threads used to process HTTP requests (default: -1)
                                        (env: LLAMA_ARG_THREADS_HTTP)
--cache-reuse N                         min chunk size to attempt reusing from the cache via KV shifting
                                        (default: 0)
                                        [(card)](https://ggml.ai/f0.png)
                                        (env: LLAMA_ARG_CACHE_REUSE)
--metrics                               enable prometheus compatible metrics endpoint (default: disabled)
                                        (env: LLAMA_ARG_ENDPOINT_METRICS)
--props                                 enable changing global properties via POST /props (default: disabled)
                                        (env: LLAMA_ARG_ENDPOINT_PROPS)
--slots                                 enable slots monitoring endpoint (default: enabled)
                                        (env: LLAMA_ARG_ENDPOINT_SLOTS)
--no-slots                              disables slots monitoring endpoint
                                        (env: LLAMA_ARG_NO_ENDPOINT_SLOTS)
--slot-save-path PATH                   path to save slot kv cache (default: disabled)
--jinja                                 use jinja template for chat (default: disabled)
                                        (env: LLAMA_ARG_JINJA)
--reasoning-format FORMAT               controls whether thought tags are allowed and/or extracted from the
                                        response, and in which format they're returned; one of:
                                        - none: leaves thoughts unparsed in `message.content`
                                        - deepseek: puts thoughts in `message.reasoning_content`
                                        - deepseek-legacy: keeps `<think>` tags in `message.content` while
                                        also populating `message.reasoning_content`
                                        (default: auto)
                                        (env: LLAMA_ARG_THINK)
--reasoning-budget N                    controls the amount of thinking allowed; currently only one of: -1 for
                                        unrestricted thinking budget, or 0 to disable thinking (default: -1)
                                        (env: LLAMA_ARG_THINK_BUDGET)
--chat-template JINJA_TEMPLATE          set custom jinja chat template (default: template taken from model's
                                        metadata)
                                        if suffix/prefix are specified, template will be disabled
                                        only commonly used templates are accepted (unless --jinja is set
                                        before this flag):
                                        list of built-in templates:
                                        bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml,
                                        command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3,
                                        gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense,
                                        hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos,
                                        llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1,
                                        mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch,
                                        openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss,
                                        smolvlm, vicuna, vicuna-orca, yandex, zephyr
                                        (env: LLAMA_ARG_CHAT_TEMPLATE)
--chat-template-file JINJA_TEMPLATE_FILE
                                        set custom jinja chat template file (default: template taken from
                                        model's metadata)
                                        if suffix/prefix are specified, template will be disabled
                                        only commonly used templates are accepted (unless --jinja is set
                                        before this flag):
                                        list of built-in templates:
                                        bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml,
                                        command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3,
                                        gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense,
                                        hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos,
                                        llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1,
                                        mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch,
                                        openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss,
                                        smolvlm, vicuna, vicuna-orca, yandex, zephyr
                                        (env: LLAMA_ARG_CHAT_TEMPLATE_FILE)
--no-prefill-assistant                  whether to prefill the assistant's response if the last message is an
                                        assistant message (default: prefill enabled)
                                        when this flag is set, if the last message is an assistant message
                                        then it will be treated as a full message and not prefilled
                                        
                                        (env: LLAMA_ARG_NO_PREFILL_ASSISTANT)
-sps,  --slot-prompt-similarity SIMILARITY
                                        how much the prompt of a request must match the prompt of a slot in
                                        order to use that slot (default: 0.10, 0.0 = disabled)
--lora-init-without-apply               load LoRA adapters without applying them (apply later via POST
                                        /lora-adapters) (default: disabled)
-td,   --threads-draft N                number of threads to use during generation (default: same as
                                        --threads)
-tbd,  --threads-batch-draft N          number of threads to use during batch and prompt processing (default:
                                        same as --threads-draft)
--draft-max, --draft, --draft-n N       number of tokens to draft for speculative decoding (default: 16)
                                        (env: LLAMA_ARG_DRAFT_MAX)
--draft-min, --draft-n-min N            minimum number of draft tokens to use for speculative decoding
                                        (default: 0)
                                        (env: LLAMA_ARG_DRAFT_MIN)
--draft-p-min P                         minimum speculative decoding probability (greedy) (default: 0.8)
                                        (env: LLAMA_ARG_DRAFT_P_MIN)
-cd,   --ctx-size-draft N               size of the prompt context for the draft model (default: 0, 0 = loaded
                                        from model)
                                        (env: LLAMA_ARG_CTX_SIZE_DRAFT)
-devd, --device-draft <dev1,dev2,..>    comma-separated list of devices to use for offloading the draft model
                                        (none = don't offload)
                                        use --list-devices to see a list of available devices
-ngld, --gpu-layers-draft, --n-gpu-layers-draft N
                                        number of layers to store in VRAM for the draft model
                                        (env: LLAMA_ARG_N_GPU_LAYERS_DRAFT)
-md,   --model-draft FNAME              draft model for speculative decoding (default: unused)
                                        (env: LLAMA_ARG_MODEL_DRAFT)
--spec-replace TARGET DRAFT             translate the string in TARGET into DRAFT if the draft model and main
                                        model are not compatible
-mv,   --model-vocoder FNAME            vocoder model for audio generation (default: unused)
--tts-use-guide-tokens                  Use guide tokens to improve TTS word recall
--embd-gemma-default                    use default EmbeddingGemma model (note: can download weights from the
                                        internet)
--fim-qwen-1.5b-default                 use default Qwen 2.5 Coder 1.5B (note: can download weights from the
                                        internet)
--fim-qwen-3b-default                   use default Qwen 2.5 Coder 3B (note: can download weights from the
                                        internet)
--fim-qwen-7b-default                   use default Qwen 2.5 Coder 7B (note: can download weights from the
                                        internet)
--fim-qwen-7b-spec                      use Qwen 2.5 Coder 7B + 0.5B draft for speculative decoding (note: can
                                        download weights from the internet)
--fim-qwen-14b-spec                     use Qwen 2.5 Coder 14B + 0.5B draft for speculative decoding (note:
                                        can download weights from the internet)
--fim-qwen-30b-default                  use default Qwen 3 Coder 30B A3B Instruct (note: can download weights
                                        from the internet)
--gpt-oss-20b-default                   use gpt-oss-20b (note: can download weights from the internet)
--gpt-oss-120b-default                  use gpt-oss-120b (note: can download weights from the internet)
--vision-gemma-4b-default               use Gemma 3 4B QAT (note: can download weights from the internet)
--vision-gemma-12b-default              use Gemma 3 12B QAT (note: can download weights from the internet)

Bekannte Probleme

Error 1 [ 34%] Linking HIP shared library ../../../bin/libggml-hip.so [ 34%] Built target ggml-hip gmake[1]: *** [CMakeFiles/Makefile2:1775: ggml/src/CMakeFiles/ggml-cpu.dir/all] Error 2 gmake: *** [Makefile:146: all] Error 2

Lösung:

GCC 13.3.0 konnte damit nicht umgehen, aber GCC 14.2.0 hat bessere C++-Standard-Compliance und kann diesen Header-Konflikt korrekt auflösen – deshalb funktioniert die Kompilierung jetzt mit der neueren Compiler-Version.
sudo update-alternatives --remove gcc /usr/bin/gcc-13
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-14 100
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-13 50
sudo update-alternatives --config gcc
# Wähle gcc-14

sudo update-alternatives --remove g++ /usr/bin/g++-13
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-14 100
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-13 50
sudo update-alternatives --config g++
# Wähle g++-14

Nützliche Links

• Offizielles llama.cpp Repository
• llama-server Dokumentation
• llama.cpp Discussions
• GGUF Modelle auf Hugging Face
• ROCm Dokumentation
• Vulkan SDK Dokumentation