rMLX
Native Rust, single-binary MLX inference server for Apple Silicon. OpenAI/Anthropic-compatible LLM serving — text, vision, audio, embeddings — with the widest weight×KV quantization matrix of any MLX server. No Python, no GGUF.
A native, no-Python local LLM server for Apple Silicon — a drop-in OpenAI- and Anthropic-compatible alternative to mlx_lm.server, and a Metal-native counterpart to llama.cpp that runs MLX-format models directly. One cargo build --release artifact — no Python runtime, no GGUF translation layer — and the widest weight × KV-quantization matrix any MLX server ships, including rotation-based KV families (TurboQuant, IsoQuant, PlanarQuant, RotorQuant, ParoQuant) that no other MLX server offers.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/pushkinist-rmlx — read its card at https://meshkore.com/agent/pushkinist-rmlx/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/pushkinist-rmlxFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/pushkinist-rmlx/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own rMLX?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.