Ollama
One-command local LLM runtime.
OPEN SOURCECPU-CAPABLE
DATASHEET // LLAMA-CPP
llama.cpp
The C++ inference engine powering most local LLMs.
OPEN SOURCECPU-CAPABLERuns locally
Actually FreeNo SignupOpen SourceWatermark-FreeHobbyist-OKAPI
Visit llama.cppUPDATED 2026-05-17 · DIRECT LINK
HARDWARE REQUIREMENTS //
Runs locally · High-end GPU (16–24 GB)
Min VRAM
None
Rec. VRAM
24 GB
Min RAM
8 GB
Rec. RAM
64 GB
Disk
100 GB
GPU class
High-end GPU
CUDAApple Silicon ✓CPU-CapableQuant: Q2_K, Q3_K, Q4_K_M +6
Mixed CPU+GPU offload is its superpower for 70B+ on a single card.
[ EDITORIAL PICK ]
Why we recommend llama.cpp
- Open source
Source is public — you can audit it, fork it, and you'll never lose access to your workflows if llama.cpp the company changes direction.
- CPU-capable
Doesn't require a dedicated GPU — useful on laptops and headless servers.
- Apple Silicon
Native Metal / MPS support — runs on M-series Macs without CUDA gymnastics.
- Top-tier pick
Power-user score 96/100 — consistently rated highly by people who use this every day, not just benchmark chasers.
[ EVIDENCE NOTE ]
Documentation-led datasheet
This page summarizes upstream documentation, release information, and editorially reviewed catalogue fields. It is not presented as a hands-on benchmark. Verify changing requirements at the official project; report stale data through our corrections channel.
AT-A-GLANCE SIGNALS //
DERIVED FROM THIS PAGE'S DATA- Install difficultyEasy
Runs CPU-only — no CUDA / driver gymnastics required.
- Hardware comfortRuns anywhere
No dedicated GPU required.
- EcosystemStrong devkit
Open-source AND ships an API — easy to integrate, possible to host yourself.
- VerificationRecent
Catalogue entry last updated 60 days ago — re-verification due soon.
[ COMMUNITY GUIDES & WORKFLOWS ]
Tutorials & deep-dives for llama.cpp
Hand-picked from YouTube, Reddit, GitHub, and the wider web. Each link goes straight to the source — we don't intercept or rewrite anything.
[ MORE IN THIS NICHE ]
Other local llm runners tools we rate
Three picks across different tradeoffs — so you don't end up with three near-clones of llama.cpp.
What is llama.cpp?
The reference CPU/GPU inference engine for GGUF-quantized LLMs. Ollama, LM Studio, Jan, and KoboldCpp all sit on top of it. Use it directly when you need raw control, multi-GPU split, or exotic quants.
Pros & cons
✓ PROS
- Best quantization support anywhere
- Runs on basically anything
- Multi-GPU tensor split
– CONS
- CLI-first
- You manage models yourself
What's actually free?
Free / OSS.
✓ Actually FreeNo SignupOpen SourceWatermark-Free