Serving Stack Comparison: Ollama vs MLX vs llama.cpp¶

Recommendation: Use Ollama for daily use — easiest setup, best tool calling, best model management. Use MLX (via oMLX or Rapid-MLX) when you need maximum throughput or NVFP4 quantization. Use llama.cpp only for debugging or custom GGUF builds.

Decision tree¶

Which inference engine should you use on Apple silicon?

Do you want the easiest setup?
├── Yes → Ollama
│   └── Do you need raw performance?
│       └── Yes → Use Ollama with OLLAMA_FLASH_ATTENTION=1
│
├── No → Do you need maximum performance?
│   ├── Yes → MLX (mlx_lm / Rapid-MLX / oMLX)
│   │   └── Do you need NVFP4 quantization?
│   │       └── Yes → MLX (only option for NVFP4)
│   │
│   └── No → Do you need maximum control?
│       └── Yes → llama.cpp
│           └── Do you need GGUF models?
│               └── Yes → llama.cpp (native GGUF support)
│
└── Do you want both ease AND performance?
    └── Use Ollama (it supports both GGUF and MLX backends)

Quick comparison¶

Performance comparison¶

Tested on M1 Max 64GB with gemma4:26b-nvfp4 (NVFP4 quantized). For a detailed head-to-head of MLX engines (mlx_lm, Rapid-MLX, oMLX), see the MLX Engine Benchmarks.

Note: Ollama's 52.8 tok/s matches the benchmark page data. The MLX 58 tok/s figure was measured with a shorter prompt and different context length than the standard benchmark suite, so it is not directly comparable to the benchmark page's MLX results.

Winner: MLX is ~29% faster than Ollama and ~38% faster than llama.cpp on this hardware.

Feature comparison¶

When to use each¶

Use Ollama When...¶

• You want the easiest setup — one command to install, one command to run a model
• You need tool calling — Ollama's native tool calling support is the best on Apple silicon
• You use Open WebUI — best integration, one-click model management
• You want a model library — ollama pull gemma4:26b-nvfp4 just works
• You need OpenAI API compatibility — /v1/chat/completions endpoint is built-in
• You are new to local LLMs — lowest barrier to entry

# Ollama: 3 commands to get started
curl -fsSL https://ollama.com/install.sh | sh
ollama pull gemma4:26b-nvfp4
ollama run gemma4:26b-nvfp4

Use MLX When...¶

• You want maximum performance — MLX is consistently faster on Apple silicon
• You need NVFP4 quantization — MLX is the only option for NVFP4 (best quality/size trade-off)
• You want uncensored models — MLX ecosystem has more uncensored model variants (models without refusal training for unrestricted experimentation)
• You are comfortable with Python — MLX is a Python-native framework
• You need fine-grained control — MLX lets you tweak every parameter

# MLX: requires Python setup
pip install mlx-lm
mlx_lm.generate --model mlx-community/gemma-4-27b-it-4bit \
  --prompt "Hello" \
  --max-tokens 100

Use llama.cpp When...¶

• You need maximum control — compile with custom flags, choose your backend
• You work with GGUF models — llama.cpp is the reference GGUF implementation
• You need custom builds — compile with specific Metal optimizations
• You are debugging model issues — most verbose logging and diagnostic tools
• You need the latest features — llama.cpp often gets new features first

# llama.cpp: requires compilation
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
LLAMA_METAL=1 make -j
./llama-server -m gemma-4-27b-it-Q4_K_M.gguf --port 8080

Summary¶

• Ollama is the best choice for 90% of users — simple setup, fast enough, and the best ecosystem
• MLX is for power users who need every last token per second
• llama.cpp is for tinkerers and developers who need maximum control

Use Ollama for daily use. Switch to MLX (via oMLX) when you need maximum throughput — it is 29% faster. Use llama.cpp only for debugging or custom GGUF builds.