Tool
KoboldCpp
A single-file GGUF/GGML runner on llama.cpp with the KoboldAI Lite UI and APIs — CPU and GPU paths on Windows, Mac, and Linux. Download only from GitHub releases.
What it is
KoboldCpp lives only in LostRuins/koboldcpp. It is a single-file local runner for GGUF/GGML models built on llama.cpp, shipping with the KoboldAI Lite UI plus API endpoints. It supports CPU and GPU backends across Windows, Mac, and Linux. Upstream README warns that koboldcpp.com is not official — treat it as a phishing risk and get binaries only from GitHub releases.
What it replaces
It replaces juggling a separate llama.cpp build plus a chat UI when you want one download that runs GGUF weights with a Kobold-style interface and API. It does not replace reading each model’s license, sizing VRAM/RAM, or a multi-user GPU cluster like vLLM.
License
KoboldCpp + KoboldAI Lite are AGPL-3.0 (verified from upstream licensing notices). Portions from llama.cpp, stable-diffusion.cpp, and GGML are under MIT. Model licenses are separate — the runner license does not rewrite weight terms. AGPL matters if you network-modify or distribute the software; re-check the repo LICENSE before shipping a modified network service.
Where it falls over
- VRAM and RAM still rule — oversized GGUFs thrash or OOM on undersized boxes.
- AGPL-3.0 obligations apply if you modify and offer the software over a network or redistribute.
- Phishing domain: koboldcpp.com is not official — downloads only from GitHub releases.
- Exposed API without auth/firewall hands strangers a model endpoint on your machine.
- Model cards can restrict commercial or high-user-count use even when the runner starts fine.
Hardware that usually matters
The runner is free. The box is not. Example Amazon listings — not rankings, not endorsements; prices and stock change. More detail in VRAM and RAM for local LLMs and storage for local models.
- ASUS Prime GeForce RTX 5060 Ti 16GB — mid-range 16 GB VRAM; enough for many 7B–13B quantized setups.
- GIGABYTE GeForce RTX 3090 24GB — older generation; 24 GB VRAM still matters for larger local models / less painful offload.
- CORSAIR Vengeance DDR5 64GB (2×32GB) — system RAM when layers spill off the GPU or you CPU-offload.
- Samsung 990 PRO 2TB NVMe SSD — GGUF libraries eat disk; fast NVMe keeps downloads and swaps from feeling stuck.
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Who it's for
Local-LLM users who want a single-file GGUF runner with KoboldAI Lite UI/APIs and will download only from GitHub. Wrong tool if you needed a polished proprietary desktop app, ignored AGPL network obligations, or trusted a lookalike domain for binaries.
Related in this cluster
- llama.cpp — inference engine KoboldCpp builds on
- llamafile — single-file Cosmopolitan distribution path
- Ollama — MIT pull/run runner with a library UX
- Jan — desktop ChatGPT-shaped local app
- LM Studio — proprietary desktop GUI + local API
- Open WebUI — self-hosted chat UI often paired with runners
Verdict
Use KoboldCpp when a single-file GGUF runner plus Kobold UI beats assembling llama.cpp and a front-end yourself — and you will size the box, respect AGPL if you network-modify, and download only from GitHub releases. Skip anything claiming to be “official” at koboldcpp.com.