headless-macs

Tool Comparison — When to Use Each

Quick Selection Guide

I want to…	Use
Run models with minimal setup, pull from a registry	Ollama
Maximum generation speed for a coding agent (Claude Code, Cursor, Aider)	Rapid-MLX
Serve a specific HuggingFace model not in Rapid-MLX’s alias list	mlx-lm
Add embeddings or reranking to a RAG pipeline	Infinity
Pool multiple Macs to run a model too large for one machine	Exo

---

The Combinations That Make Sense

Setup	Tools
Solo node, general use	Ollama only
Solo node, coding agent (Claude Code / Cursor)	Rapid-MLX + Infinity
Solo node, RAG app	Ollama + Infinity
Solo node, custom HuggingFace models	mlx-lm + Infinity
Multi-Mac cluster	Exo + Infinity on a dedicated node

---

Side-by-Side Comparison

Dimension	Ollama	Rapid-MLX	mlx-lm	Infinity	Exo
Primary use	General inference + model management	Max speed + tool calling	Raw HF model serving	Embeddings + reranking	Distributed inference
API	OpenAI-compatible	OpenAI-compatible	OpenAI-compatible	OpenAI-compatible	OpenAI-compatible
Default port	11434	8000	8080	7997	52415
Install method	curl install.sh	brew / pip3	pip3	pip3	brew / pip3
Model source	ollama pull <model>	Alias-based (rapid-mlx models)	HuggingFace repo ID	HuggingFace repo ID	Auto via cluster
Apple Silicon acceleration	Metal (MLX backend preview)	Native MLX	Native MLX	MPS (--device mps)	MLX per node
Prompt caching	Partial	Yes, incl. DeltaNet for RNN hybrids	No	N/A	Partial
Tool calling	Basic	17 parsers + auto-recovery	No	N/A	Basic
Reasoning separation	No	Yes (Qwen3, DeepSeek-R1)	No	N/A	No
Built-in diagnostics	Log inspection	rapid-mlx doctor	Log inspection	Log inspection	Log inspection
Runs as	LaunchDaemon (root)	LaunchDaemon (root)	LaunchDaemon (root)	LaunchDaemon (root)	LaunchAgent (user)
Maturity	Stable	Beta (v0.6, April 2026)	Stable	Stable	Beta
Best for	General use, easy model management	Coding agents, max speed	Custom HF models	RAG embeddings	Multi-Mac clusters

---

Ollama

Strengths

• Easiest model management — ollama pull, ollama list, ollama run
• Large model registry with quantised variants
• MLX backend on Apple Silicon since Ollama 0.19 (March 2026) — narrows speed gap with pure MLX tools
• Modelfile system bakes num_ctx and sampling parameters into model metadata — the only way clients see the correct context window (see docs/modelfile-guide.md)
• Most mature and widely tested option

Weaknesses

• VS Code Copilot agent mode is broken for local GGUF models — tool call loop bug (microsoft/vscode-copilot-chat #3566). Use Zoo Code for agentic tasks.
• Basic tool-call support — no auto-recovery on malformed output (mitigated by Modelfile tuning)
• num_ctx must be set via Modelfile, not the UI — Ollama UI override does not update the metadata that clients read

When to choose Ollama

• You want to get started quickly
• You need to manage many different models via a registry
• You’re serving GGUF models with controlled context windows (Modelfile required)
• You’re running general-purpose workloads with Zoo Code or Opilot as the client

---

Rapid-MLX

Rapid-MLX is a production-grade MLX-based inference server built specifically for Apple Silicon. It reimplements the serving stack with continuous batching, optimised prefill chunking, DeltaNet state snapshots, 17 tool-call format parsers with auto-recovery, and reasoning/content separation for Qwen3 and DeepSeek-R1.

Previously benchmarked at 2–4.2× faster than Ollama. Since Ollama 0.19 adopted the MLX backend on Apple Silicon (March 2026), the raw generation speed gap has narrowed to ~15–30%. Rapid-MLX retains meaningful advantages in serving sophistication over Ollama 0.19.

Strengths

• Fastest generation on Apple Silicon
• DeltaNet prompt caching — works for hybrid RNN-attention models (Mamba, Jamba) that Ollama and mlx-lm cannot cache
• 17 tool-call parsers with auto-recovery on malformed output — critical for coding agent reliability
• Reasoning separation for Qwen3 / DeepSeek-R1 (--no-thinking flag strips reasoning tokens)
• rapid-mlx doctor built-in self-diagnostic
• --prefill-step-size 8192 fixes slow cold-start on long prompts

Weaknesses

• Beta (v0.6, April 2026) — not yet suitable as your only option in critical production
• Model aliases (rapid-mlx models) don’t cover every HF model — use mlx-lm for those
• First serve downloads the model — API unavailable until download completes
• Vision/audio require extras: pip install 'rapid-mlx[vision]'

When to choose Rapid-MLX

• Primary use case is a coding agent (Claude Code, Cursor, Aider, Continue)
• You need reliable tool calling
• Generation speed is the priority
• You’re running Qwen3 or DeepSeek-R1 and want reasoning separation

Rapid-MLX vs mlx-lm Use Rapid-MLX as the default. Fall back to raw mlx-lm only when you need a specific HuggingFace model path not covered by Rapid-MLX’s model aliases.

---

mlx-lm

Apple’s own ML framework serving layer. Provides an OpenAI-compatible HTTP server for HuggingFace models.

Strengths

• Direct HuggingFace model path support — any mlx-community/ quantised model
• Minimal dependency surface
• Official Apple framework

Weaknesses

• Lower-level than Rapid-MLX — no prompt caching, no tool-call recovery, no reasoning separation
• Requires downloading the model before the server can start (plist is written but not bootstrapped if default_model is empty)
• No built-in diagnostics

When to choose mlx-lm

• You need a specific HuggingFace model not aliased in Rapid-MLX
• You want minimal dependencies
• You’re experimenting with Apple’s MLX framework directly

Important: MLX models do not respect Modelfile num_ctx. The context window is fixed at model conversion time. If clients need to see a specific context window via model metadata, use GGUF + Ollama instead.

---

Infinity

Production-grade embedding and reranking server. Uses MPS (Metal Performance Shaders) for GPU-accelerated inference on Apple Silicon.

Key endpoints

• POST http://host:7997/v1/embeddings — OpenAI-compatible embedding
• POST http://host:7997/v1/rerank — Cross-encoder reranking
• GET http://host:7997/v1/models — List loaded models

Strengths

• MPS-accelerated — significantly faster than CPU-only embedding servers
• OpenAI-compatible — drop-in for openai.Embedding.create()
• Supports both embeddings and reranking from a single server
• High throughput for RAG pipelines

Weaknesses

• Embedding-only — not a generation server
• Requires --device mps flag (set in plist — handled automatically by install-tools.sh)
• Without --device mps, falls back to CPU at ~10× lower throughput

When to choose Infinity

• Any RAG pipeline — pairs with Ollama or Rapid-MLX for generation
• When you need reranking (cross-encoder) in addition to embeddings
• When embedding throughput matters at scale

---

Exo

Clusters multiple Apple Silicon Macs into a single distributed inference node. Pools unified memory across devices to run models larger than any single machine can hold.

Strengths

• Run 405B models across 3× Mac Mini M4 64GB (pooling 192GB)
• Tailscale discovery works across networks (not just LAN)
• OpenAI-compatible API

Weaknesses

• Runs as LaunchAgent (user-level), not LaunchDaemon — requires auto-login for headless boot
• Each node must have Exo installed and running
• Beta — less tested than single-node options
• Bonjour discovery limited to LAN; Tailscale recommended for production

When to choose Exo

• You have multiple Apple Silicon Macs you want to use as a cluster
• The model you need is larger than any single machine’s unified memory
• You’re comfortable with a more complex setup

Requirements

• Auto-login configured on every node (sysadminctl -autologin set)
• Tailscale installed and running on all nodes (for cross-network discovery)
• Same Exo version on all nodes

This site is open source. Improve this page.