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llama.cpp vs LM Studio

Bottom line

llama.cpp is open source and LM Studio is not.

Which one should you try?

llama.cpp

Free

Best for Developers who want direct control over how a local model runs

Visit llama.cpp See full review

LM Studio

Free

Best for People who want a desktop app for downloading and chatting with local models

Visit LM Studio See full review

Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.

llama.cpp

llama.cpp

llama.cpp is the MIT-licensed C/C++ engine that runs GGUF language models on your own hardware and serves them through an OpenAI-compatible API. LocalAI runs models through a backend built on it. We ran its official Docker server with a 3B model in about 1.6 GB of memory on a CPU-only server.

LM Studio

LM Studio

LM Studio is a free desktop app for local models with a headless server (llmster), local APIs and SDKs. We ran llmster on a CPU-only server and measured install, load time and offline behavior.

Attributellama.cppLM Studio
PricingFreeFree
Price—Free to run models locally; optional cloud plans: Bionic+ $20/mo, Pro $100/mo
Pricing checkedNot checked yetOct 16, 2026
Free trialNot statedNot stated
Open sourceYesNo
Has APIYesYes
Self-hosted optionYesYes
PlatformsLinux, macOS, Windows, Android, ChromeOS, iOS, Web (WebGPU)macOS (Intel and Apple Silicon), Windows, Linux (x64 and ARM64)
Released2023-032023-05
Pros
  • OpenAI-compatible server, so code written for the OpenAI chat API can use a local model by changing the base URL.
  • Its server handles several users at once with continuous batching, on by default.
  • Small footprint in our test: about 1.6 GB of memory for a 3B model, and no DNS lookups.
  • A desktop app and a headless server (llmster), so you can chat with models or serve them to your apps.
  • Local APIs plus Python and TypeScript SDKs.
  • Works offline: with its update and extension hosts blocked, it still loaded and served the model.
  • Used 2 GB of memory with our 3B model, a little less than Ollama's 2.2 GB.
Cons
  • It is an engine, not a model manager: you pick and download GGUF files yourself, where apps like Ollama do that for you.
  • In containers it does not size threads to the CPU limit; with 3 cores, generation was 7.6 times slower until we passed --threads 3.
  • Answer quality is limited by the open-weight model you can run on your hardware.
  • Slow cold load: lms load took 44.6 s, about 34 s of it before the engine started, with nothing in the logs to explain the wait.
  • Closed source and free under LM Studio's terms; only the lms CLI is open source (MIT).
  • Ollama generated text up to 1.38x faster on the same model and cores.

Made up your mind?

llama.cpp

Free

Best for Developers who want direct control over how a local model runs

Visit llama.cpp See full review

LM Studio

Free

Best for People who want a desktop app for downloading and chatting with local models

Visit LM Studio See full review

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Free trials & curated lists

Specs come from each tool's own site and docs (how we test). AIDiveForge is editorially independent.

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