AI tool comparison
LM Studio + Locally AI vs RLM
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
LM Studio + Locally AI
LM Studio buys the best iOS local LLM app to go cross-device
75%
Panel ship
—
Community
Free
Entry
LM Studio, the most popular desktop app for running local large language models, has acquired Locally AI — the leading iOS and iPadOS app for on-device inference on Apple Silicon. Locally AI's creator Adrien Grondin is joining LM Studio full-time to lead cross-device native AI experiences. The acquisition signals LM Studio's ambition to own the full local AI stack: macOS, Windows, Linux, and now iPhone and iPad. Locally AI was notable for its deep Apple Silicon integration, using Core ML and Metal Performance Shaders to run models like Llama 3 and Phi-3 natively on A-series and M-series chips. The app had a dedicated following among privacy-conscious users who wanted a clean iOS interface without compromising their data to cloud services. LM Studio brings a larger model library, server mode, and a more mature MLX/GGUF toolchain. For local AI enthusiasts, this is a consolidation play in a space that was starting to fragment across too many single-platform apps. A unified LM Studio experience across desktop and mobile would be a significant UX improvement. It also sets up an interesting competition with Apple's own on-device AI ambitions in iOS 19.
Developer Tools
RLM
Run recursive self-calling LLMs with sandboxed execution environments
75%
Panel ship
—
Community
Paid
Entry
RLM (Recursive Language Model) is a plug-and-play Python inference library that lets you run models that call themselves recursively within configurable sandboxed execution environments. Rather than a fixed inference pipeline, RLM exposes the recursive call graph as a first-class primitive — models can iterate, self-correct, and re-invoke themselves across different environments without special orchestration glue. The library was first published in December 2025 and has accumulated 3,498 stars on GitHub. It targets researchers and engineers exploring architectures where the model itself controls how many times it reasons before committing to an output — a capability becoming central to advanced reasoning systems but usually buried in proprietary labs. Why it matters: most open-source inference tools treat the model as a stateless function. RLM bets that the next wave of reasoning breakthroughs comes from architectures where inference depth is dynamic and model-controlled. Early adopters are using it to reproduce recursive reasoning experiments without access to frontier-model APIs.
Reviewer scorecard
“This is the right move for LM Studio. The desktop client is already excellent and Locally AI's Core ML integration is the best iOS inference wrapper available. Combining Grondin's Apple-native work with LM Studio's model management and server mode could produce something genuinely special for local AI power users.”
“Finally a clean abstraction for recursive inference without building the scaffolding yourself. The sandbox configurability means you can experiment with different execution environments without rewriting your harness each time. For researchers reproducing chain-of-recursive-thought papers, this cuts setup time dramatically.”
“Acquisitions in open-source adjacent tools often mean the indie app loses what made it great. Locally AI was clean and opinionated; LM Studio is powerful but has more surface area. There's real risk the mobile experience gets de-prioritized once the acquisition honeymoon ends.”
“3,500 stars is respectable but the library is still at v0.x with no production deployments publicly documented. Recursive self-calling can blow up token costs exponentially if you're not careful about termination conditions. Until there's clearer documentation on guardrails and cost controls, treat this as a research toy, not production infra.”
“The race to own the local AI client layer is just beginning. LM Studio is positioning itself as the VLC of AI — runs everything, everywhere, free. If they nail the cross-device sync story (shared model library, shared chats), they become the default for privacy-first AI.”
“Recursive inference is one of the key unlock mechanisms for models that self-improve their reasoning at test time. RLM democratizes this capability at a moment when OpenAI and Anthropic are building proprietary versions internally. The researcher who masters this abstraction today has a significant head start.”
“Being able to run the same model on my MacBook and iPhone with the same interface is a genuine quality-of-life win. I use local models for confidential creative writing and the iOS gap has always been frustrating. This closes it.”
“For creative applications — iterative story refinement, self-critiquing copy — recursive inference is genuinely useful and RLM makes it accessible. The open sandbox model means you can wire it to any content generation pipeline without vendor lock-in.”
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