# Apple MLX vs Mozilla llamafile: 2026 Empirical Benchmark & Cost Shootout

> **Direct Answer (October 2026 Verdict)**: Apple MLX (mlx-lm) wins on macOS M1/M2/M3/M4 hardware with 185 tokens/sec 8B Q4 inference and zero-copy unified memory arrays across 28.8k+ GitHub stars (96.6), while Mozilla llamafile wins on universal cross-OS portability by packaging model weights and an OpenAI server into a single zero-install executable across macOS, Windows, Linux, and BSD with 26.4k+ GitHub stars (95.8).

- **Category**: Apple Silicon Unified Memory Engine vs Single-File 6-OS Portable LLM Binary Shootout
- **Canonical URL**: https://exactbaseline.com/vs/mlx-vs-llamafile
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## Apple MLX Key Advantages
- 28,816+ GitHub stars built by Apple machine learning research with zero-copy CPU/GPU unified memory tensors
- 185 tokens/sec on 8B Q4 models (+35% faster than generic GGUF wrappers on M-series Macs) plus fast local LoRA training
- Powers Exo distributed Mac clusters and native Swift macOS/iOS on-device AI apps

## Mozilla llamafile Key Advantages
- 26,361+ GitHub stars combining llama.cpp with Cosmopolitan Libc into a single self-contained executable file
- Runs identically across macOS, Windows, Linux, FreeBSD, OpenBSD, and NetBSD with zero Python or CUDA setup
- 98ms cold start with built-in local web chat UI and OpenAI-compatible `/v1/chat/completions` endpoint