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Ollama vs llama.cpp vs LM Studio: Which Local LLM Runtime?
All three run GGUF files on the same engine (llama.cpp) — the difference is packaging. Pick by who you are.
At a glance
| Ollama | llama.cpp | LM Studio | |
|---|---|---|---|
| Setup | one installer | build or download binary | one installer, GUI |
| Model source | own registry + any HF GGUF (ollama run hf.co/repo:quant) | any GGUF file, any HF repo (-hf repo:quant) | built-in HF browser |
| OpenAI-compatible API | yes (:11434) | yes (llama-server) | yes (local server tab) |
| Control (context, GPU layers, KV quant) | limited (Modelfile) | full | good (sliders) |
| Speed | same engine — within a few % when settings match | ||
Which to choose
- Just want it to work / building an app on top: Ollama.
ollama run hf.co/<repo>:<quant>pulls the exact GGUF from Hugging Face — every quant on this site works. - Want every knob (KV-cache quant, custom samplers, maximum tok/s): llama.cpp's
llama-serverorllama-cli. - Prefer a GUI, chat UI, and browsing models visually: LM Studio.
Memory tips that apply to all three
Set context to what you need (not the max) — KV cache scales linearly. On Macs raise the wired limit (sysctl iogpu.wired_limit_mb) to use more than 75% of unified memory. Check exact per-model numbers on any model page.
Doesn't fit your machine? Rent a GPU by the hour instead — a 24GB RTX 4090 starts around $0.30-0.50/h (Vast.ai, RunPod), or use the model via a hosted API. Buying instead? Which GPU should I buy? →
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