Technology · LLMs → Serving · Open Source · wiki:deep

llama.cpp

Taxonomy

llama.cpp runs LLMs efficiently on consumer and edge hardware via quantized C/C++ inference. Underpins many local model deployments. Enables private/on-prem workers without GPU clouds — trade throughput for data locality. LLM inference in C/C++ ggml / ops / maintainer PRs%20sort%3Aupdated-desc) / dev stats / lib llama API / llama-server REST API - Visit https://llama.app and follow the instructions - Run with Docker - see our Docker documentation - Download pre-built binaries from the releases page - Build from source by cloning this repository.

GitHub · ggml-org/llama.cppDocsStack: llms-serving

Why it matters here

In this catalog it is the Serving option when you need to run model inference at usable latency and cost. Keep llama.cpp on the observe/propose side of the loop: useful signals and drafts, with human or policy authority still required before anything irreversible runs.

How it works

llama.cpp runs quantized C/C++ inference on consumer and edge hardware.

  1. Obtain a GGUF (or convert) quantized model.
  2. Load into llama.cpp / server bindings.
  3. Run completion on CPU/GPU backends available.
  4. Return tokens to local agents or UIs.

Related: ollama · vllm · topics/04-data

Flow

When to reach for it

  • Use when: llama matches the bottleneck and you specifically need to run model inference at usable latency and cost.
  • Skip when: another pack already covers the need with less ops surface, or llama.cpp would become a second place people retype truth.
  • Prefer instead: compare Ollama, vLLM before locking the cell.

Limits

  • Ops: confirm license, hosting, and release cadence for llama.cpp (ggml-org/llama.cpp) before relying on it in a live corridor.
  • Integration: bad credentials, scopes, or schema drift produce empty retrievals or runaway tool/model spend without an obvious UI alarm.
  • Scope: Serving components support agents; they are not the company ledger or the approval authority.

What we checked

Claims below are backed by science sources on disk.

Primary / product role

ggml tensor library · MODERATE

“The main goal of llama.cpp is to enable LLM (and VLM) inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud.”

Docs or contrast

ggml-org/llama.cpp · MODERATE

“Plain C/C++ implementation without any dependencies”

Additional source

Server-scale vs local inference · MODERATE

“Visit https://llama.app and follow the instructions”
Research inventory

9 tags · 10 out · 11 in · 3 artifacts · 1 gaps · 14 corpus docs

Catalog tags

landscape.layer
LLMs
landscape.subcategory
Serving
license_tag
Open Source
maps.dm
absent
maps.features
5
one_liner
LLMs
review.depth
science
slug
llama-cpp
title
llama.cpp

Artifacts

  • dm_map · absent
  • features_map · present · technologies/llama-cpp/features.md
  • readme · present · technologies/llama-cpp/README.md

Out · alternative_to

Out · maps_to

In · alternative_to

In · in_stack

In · maps_to

Corpus tags

category
LLMs → Serving
dedication
open-source
feature
bring-your-own-model
cost-latency-ops
deployment-data-residency
model-flexibility-routing
self-host
kind
map_edge
tech_features
tech_quote
tech_readme
needs_deepen
false
quality
ok
slug
llama-cpp
source_id
ggml-note
llama-cpp-github
vllm-contrast
technology
llama-cpp

Corpus documents (14)

map_edge · 5

  • llama-cpp → bring-your-own-model
  • llama-cpp → cost-latency-ops
  • llama-cpp → deployment-data-residency
  • llama-cpp → model-flexibility-routing
  • llama-cpp → self-host

tech_features · 1

  • llama.cpp · features

tech_quote · 7

  • llama.cpp · ggml-note
  • llama.cpp · ggml-note
  • llama.cpp · ggml-note
  • llama.cpp · llama-cpp-github
  • llama.cpp · llama-cpp-github
  • llama.cpp · vllm-contrast
  • llama.cpp · vllm-contrast

tech_readme · 1

  • llama.cpp