Local ML Model Workbench
We tested and ranked 29 Local ML Model Workbench apps for Mac — updated August 2026.
Every app here was run on our own Apple-silicon Macs and scored on the published rubric — capability, craft, privacy, value. We watch the network while we test. How we rank →
You have a dataset to label, a model to fine-tune, or an inference pipeline to benchmark — and you would rather keep your data on your own machine than upload it to a training service. This category covers 29 Mac apps built for ML practitioners who want to run, annotate, train, or evaluate models using their own Apple Silicon hardware. The field ranges from image-annotation studios running Segment Anything and YOLO auto-labelers on-device, to LLM workbenches that execute open models via MLX with live GPU telemetry, to specialized tools for LoRA fine-tuning, transformer training on Metal, and even molecular-property prediction with local neural networks. We scored each app on Capability, Craft, Privacy, and Value. Most process everything on-device — your images, datasets, and model weights stay on your Mac — though a few expose local API servers that other tools on your network can reach. Prices cluster at the low end: the majority are free, and the priciest one-time purchase is under twenty dollars. The ranked list follows.
The ranking — all 29
RectLabel Pro
Ryo KawamuraThe one-time-purchase edition of RectLabel's annotation toolkit, running SAM 2/3, Cellpose, RF-DETR, and YOLO26 auto-labelers entirely on-device with exports to every major detection format.
” RectLabel is an offline image annotation tool; Automatic labeling using Core ML models including RF-DETR and YOLO26; Label polygons and pixels using Segment Anything Model 2 — test note
Open Otter
杰 何A power-user LLM workstation that runs open models natively on Apple Silicon via MLX, exposes an OpenAI-compatible API server, and supports distributed inference across multiple Macs.
” Run large language models locally with full Apple Silicon GPU acceleration... using the MLX framework; No cloud processing — test note
RectLabel
Ryo KawamuraThe subscription edition of RectLabel's annotation engine — same SAM 2/3, Cellpose, and Core ML auto-labeling as the Pro version, with identical YOLO/COCO/CreateML/DOTA export capabilities.
” offline image annotation tool; Automatic labeling using Core ML models including RF-DETR and YOLO26; Segment Anything Model — test note
anubis Pro
John TavernaA native Apple Silicon benchmarking workbench for local LLMs, with per-token metrics, confidence intervals, arena head-to-head comparisons, and live hardware telemetry across five inference backends.
” benchmarking tool for local large language models on Mac; Apple Intelligence - on-device Foundation Models; Ollama, LM Studio, MLX-LM support — test note
Rapidscall
Cato Hernes JensenA no-code workbench that trains custom transformer language models directly on your Mac's GPU through Metal, covering the full pipeline from tokenization and optimization to checkpointing and live loss visualization.
” Train your own AI language models on your Mac. No cloud services... no data leaves your device; Rapidscall runs entirely on your Mac's GPU using Metal; Everything runs locally — test note
LoRAbit
JEAHOON BANGHandles the full on-device LoRA fine-tuning pipeline — dataset preparation, preflight validation, MLX-powered adapter training, and export of inference-ready model folders — filling a gap most local AI tools ignore entirely.
” on-device LoRA training workspace; Train LoRA adapters locally with MLX; selected models, datasets... stay inside the workspace you choose — test note
Contour - Segment Anything
Magenta CreationsA native macOS front-end for Segment Anything that produces prompt-driven or box-drawn instance masks in batch, exporting directly to COCO JSON, YOLO TXT, and transparent PNG cutouts.
” on their own machine, without sending a single pixel to the cloud; prompt-driven instance masks — test note
OrchardGrid
斌 王Turns your Apple devices into a shared AI server, exposing chat, image generation, vision, and speech capabilities through a single HTTPS API — all running on the Neural Engine.
” Every inference runs on the Apple Silicon you already own; runs on the Neural Engine; prompts and responses never touch our servers — test note
Subjective Designer
SXP StudioA node-based real-time visual-effects canvas with 170-plus nodes and Metal GPU rendering, whose CoreML inference node lets you pipe model output into live shader chains.
” CoreML model inference; Metal rendering with real-time shader compilation; built entirely on Apple technologies — test note
MolLens
万飞 陈Runs real neural networks — Graphormer for HOMO-LUMO gap prediction, ChemBERTa for ADMET profiling — alongside RDKit cheminformatics, entirely offline so proprietary molecular structures never leave the Mac.
” a locally running Graphormer model; 'Powered by ChemBERTa'; 'All neural network inference and physical calculations execute purely on your Mac's Apple Silicon' — test note
How to choose Local ML Model Workbench for Mac
Decide whether you need an annotation tool, an inference workbench, or a training environment — these apps cluster into those three jobs, and choosing the wrong shape wastes time. RectLabel, Contour, and SimpleML focus on labeling images for object detection and segmentation. Open Otter, OrchardGrid, and LocalGate AI run pre-trained models for inference and serve them over local APIs. Rapidscall and LoRAbit handle actual on-device training and fine-tuning. Match the framework to your hardware budget. Apps built on MLX or Metal squeeze the most from Apple Silicon's unified memory — Open Otter and Rapidscall both leverage this. Tools using Core ML run efficiently but limit you to converted model formats. If you already run Ollama or LM Studio, anubis Pro can benchmark those backends directly without duplicating infrastructure. Check what leaves the machine. Every app here runs inference or training locally, but some expose network APIs — OrchardGrid serves an HTTPS endpoint, Open Otter runs an OpenAI-compatible server — that, while local by design, do open a port. If your threat model requires a fully closed network stack, stick with the annotation tools and standalone trainers. Finally, weigh price against scope. RectLabel Pro's flat $19.99 buys the same annotation engine as the subscription edition. Rapidscall and LoRAbit are free. anubis Pro benchmarks across five local backends for $4.99. The free apps are genuinely capable here — paid tiers typically add convenience, not core ML features.
Questions people ask
Can I run Hugging Face models locally on a Mac without the command line?
Several apps here handle that. Open Otter downloads and runs Hugging Face models natively on Apple Silicon via MLX — no terminal work required. LoRAbit goes further, letting you fine-tune downloaded models with LoRA adapters entirely on-device. LocalGate AI serves your downloaded models through a menu-bar gateway. All three keep the models and your data on the Mac; the only downloads are the model weights themselves.
Is there a GUI for Ollama on Mac that adds benchmarking or monitoring?
anubis Pro is the closest match. It connects to your running Ollama instance, pulls models directly from the app, and runs structured benchmarks with per-token timing, confidence intervals, and live hardware telemetry. It also supports LM Studio, MLX, and vLLM backends, so you can compare performance across engines in a single native SwiftUI interface for $4.99.
Do I need a high-end Mac to train or fine-tune models locally?
Any Apple Silicon Mac handles the annotation and inference tools in this list. For actual on-device training — Rapidscall's transformer training or LoRAbit's LoRA fine-tuning — more unified memory helps: 16 GB is comfortable for smaller models, and 32 GB or more opens up larger architectures. Metal GPU acceleration is built into both apps, so no discrete GPU is needed.