oMLX vs Port Radar for macOS: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of oMLX and Port Radar for macOS — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
oMLX
jundot
An open-source LLM inference server for Apple Silicon with continuous batching and tiered KV caching, managed from the macOS menu bar.
Key features
- Tiered KV Caching: Persists past context across a hot in-memory tier and a cold SSD tier, so cached context stays reusable across requests even when the conversation context changes mid-session.
- Continuous Batching: Serves concurrent requests through a batched scheduler rather than one-at-a-time, keeping throughput up when several clients or agent loops hit the server together.
- Menu Bar Management: Controls the server, pinned models, on-demand model swapping and context limits from a native macOS menu bar app with in-app auto-update.
- Native Metal Custom Kernels: Ships precompiled kernels in the official DMG that give large speedups on affected model families — roughly 30x faster fused DSA prefill for GLM 5.2 (845 vs ~29 tok/s measured on an M3 Ultra) with lower memory use.
- OpenAI-Compatible Endpoint: Exposes every discovered model at http://localhost:8000/v1 so existing OpenAI clients, coding agents and SDKs connect without modification.
- Multi-Modality Model Support: Auto-discovers and serves text LLMs, vision-language models, OCR models, embedding models and rerankers from subdirectories of the model directory.
- Admin Dashboard: Provides a web UI at /admin for real-time monitoring, model management, chat, benchmarking and per-model settings in eight languages, with all CDN dependencies vendored for fully offline operation.
- Experimental Multi-Mac Inference: Source builds can split one model across unequal-memory Macs using MLX pipeline ranks over Ring or Thunderbolt RDMA, with a cluster dashboard for peer discovery and SSH/runtime verification.
Best for
- Local Coding Agents: Back Claude Code, OpenCode, Codex or Copilot with an on-device model where cached context makes repeated agent turns fast enough to be usable.
- Private Inference: Keep prompts, code and documents entirely on the Mac with no cloud provider in the path and no per-token billing.
- Serving a Team from One Mac: Run the OpenAI-compatible endpoint on a high-memory Mac so other machines on the network can use larger models than they could host themselves.
- Model Benchmarking: Compare throughput and per-model settings across quantizations and families from the built-in benchmark tools in the admin dashboard.
- Multi-Modal Local Pipelines: Serve embeddings, rerankers and OCR alongside chat models from a single endpoint to build local RAG without extra infrastructure.
- Running Oversized Models: Use experimental cluster mode to split a model that will not fit on one machine across several Apple Silicon Macs.
Port Radar for macOS
Juan Sebastian Solano
Free open-source Mac menu bar app that lists every listening localhost port and uses on-device Apple Intelligence to explain what each process is.
Key features
- Menu Bar Port Scanner: Lists every listening localhost port in the menu bar with port number, PID, owning project, runtime, and the exact command line.
- Apple Intelligence Explanations: Ask in plain language what a process is, why it has been running, and whether stopping it is safe; answers are generated on-device with no cloud call.
- Project Grouping: Groups processes by the project directory that owns them and flags shared or orphaned processes with no obvious parent.
- One-Click Cloudflare Tunnels: Share any local port as a public URL through a Cloudflare quick tunnel, auto-installing cloudflared with no CLI, ngrok, or account setup.
- Clean Process Control: Stop a process gracefully or force-quit it with a confirmation step, directly from the menu bar.
- Live Tunnel Management: See which tunnels are currently live and public, copy their URLs, and stop them at any time.
- Fully On-Device Privacy: All inspection and AI explanation happens locally; no process data or command lines are sent off the machine.
- Open Source Under Apache 2.0: The full source is published on GitHub, so the app can be audited or built from source.
Best for
- Port Conflict Debugging: Finding out which forgotten process is holding port 3000 before starting a new dev server.
- Runaway Process Triage: Identifying a Node or Python process quietly eating CPU and deciding whether it is safe to kill.
- Preview Sharing: Handing a teammate or client a live public URL for a work-in-progress local app in seconds.
- Multi-Project Development: Keeping track of which of several simultaneously running projects owns each active port.
- Onboarding and Handover: Letting a developer new to a codebase understand what the local stack actually starts up.
- Privacy-Sensitive Environments: Getting AI assistance about local processes in settings where sending command lines to a cloud model is unacceptable.