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SiliconBench › Compare › Ollama vs MLX vs llama.cpp

Ollama vs MLX vs llama.cpp on Apple Silicon

Which runtime is fastest for local LLMs on your Mac? Real benchmark numbers and tradeoffs explained.

Different runtimes use Apple Silicon differently. MLX is Apple's own framework, compiled for Metal GPU acceleration. Ollama wraps llama.cpp with a server API. llama.cpp has direct Metal support. The fastest option depends on your model, quantization, and workflow.

4 runtimes compared
MLX fastest for small/mid models on M-series
Ollama easiest setup, broadest model library
Metal GPU acceleration available in all four

MLX benchmark data — M4 Max (40-core GPU, 64 GB)

Factory lab measurements using MLX. Qwen 3 4B and Qwen 3 30B A3B (MoE), various quantizations.

Model Quant Runtime Avg tok/s Source
Qwen 3 4B Q4 MLX 148.1 factory lab
Qwen 3 4B Q4_G32 MLX 149.1 factory lab
Qwen 3 4B Q5 MLX 143.2 factory lab
Qwen 3 4B Q6 MLX 136.6 factory lab
Qwen 3 4B Q8 MLX 111.6 factory lab
Qwen 3 30B A3B Q4 MLX 92.1 factory lab
Qwen 3 30B A3B Q5 MLX 84.9 factory lab
Qwen 3 30B A3B Q6 MLX 76.7 factory lab
Qwen 3 30B A3B Q8 MLX 52.6 factory lab

LM Studio benchmark data — M4 Max (128 GB)

LM Studio runs llama.cpp under the hood with its own model manager and UI. Data from community measurements.

Model Quant Runtime Avg tok/s Source
Qwen 3 0.6B Q8_0 LM Studio 184.5 reference run
Gemma 3 4B Q4_0 LM Studio 100.5 reference run
Qwen 3 8B Q4_K_M LM Studio 63.2 reference run
Qwen 2.5 7B Instruct Q8_0 LM Studio 49.7 reference run
Qwen 3 30B A3B Q4_K_M LM Studio 70.2 reference run
Gemma 3 27B Q8_0 LM Studio 14.5 reference run
Qwen 3 235B A22B Q4_K_M LM Studio 8.1 reference run

Note: LM Studio data is on M4 Max 128 GB; MLX data is on M4 Max 64 GB. Direct comparison is not apples-to-apples due to different RAM configs.

llama.cpp benchmark data

llama.cpp with Metal backend. Data from community measurements across multiple chips.

Chip Model Quant Runtime Avg tok/s
M4 (10-core GPU, 16 GB) Llama 2 7B Q4_0 llama.cpp 24.1
M1 Pro (16-core GPU) Llama 2 7B Q4_0 llama.cpp 36.4
M3 Pro (18-core GPU) Llama 2 7B Q4_0 llama.cpp 30.7
M3 Max (40-core GPU, 48 GB) Llama 2 7B Q4_0 llama.cpp 65.9
M2 Ultra (76-core GPU, 192 GB) Llama 2 7B Q4_0 llama.cpp 94.3

Runtime comparison: strengths and tradeoffs

MLX MLX

Apple's own framework. Native Metal GPU. Fastest for supported models.

  • + Fastest throughput on Apple Silicon for supported models
  • + Native Metal GPU — built specifically for M-series
  • + Active development from Apple
  • + Python API for custom workflows
  • − Smaller model library than Ollama/llama.cpp
  • − Requires more technical setup
  • − No built-in server (use mlx-lm or similar)
  • ~ Best for: maximum throughput, research, Python workflows

Ollama Ollama

The easiest way to run LLMs locally. Wraps llama.cpp with a server API.

  • + One-command model download and run
  • + OpenAI-compatible REST API
  • + Largest model library (Modelfile ecosystem)
  • + Works with hundreds of apps (Continue, Open WebUI)
  • − Slightly slower than raw llama.cpp (overhead from server)
  • − Less control over quantization and context settings
  • ~ Best for: daily use, API integrations, coding assistants

llama.cpp llama.cpp

The foundation. Metal GPU backend. Maximum control.

  • + Direct Metal GPU acceleration (same backend as Ollama)
  • + Maximum control over context, threads, batch size
  • + Supports virtually all GGUF quantizations
  • + Runs without a UI or server
  • − Command-line only, steeper learning curve
  • − No built-in model management
  • ~ Best for: power users, custom quantizations, benchmarking

LM Studio LM Studio

Polished UI. llama.cpp backend. Download models from HuggingFace in-app.

  • + Best GUI experience — no terminal required
  • + Direct HuggingFace model search and download
  • + Built-in local server with OpenAI API
  • + Good for non-technical users
  • − Same performance as llama.cpp (same backend)
  • − Not open-source
  • ~ Best for: getting started, GUI workflow, team demos

Which runtime should you use?

Use case Recommended runtime Why
Daily coding assistant (Cursor, Continue, VS Code) Ollama OpenAI-compatible API, stable, easy model swaps
Maximum throughput on M-series chip MLX Native Metal, optimized for Apple Silicon architectures
First time running LLMs locally LM Studio GUI interface, no terminal required, full model catalog
Custom quantizations and context sizes llama.cpp Direct control over all GGUF parameters
Python ML workflow / research MLX Python-native API, composable with NumPy-style operations
Running MoE models (Qwen 30B A3B, Mixtral) MLX or llama.cpp Both handle sparse experts; MLX tends to be faster on Mac
Note: Ollama benchmark data is not yet in the SiliconBench dataset. Most existing community data comes from llama.cpp, MLX, and LM Studio runs. Ollama and llama.cpp typically perform similarly since Ollama wraps llama.cpp. Direct Ollama vs MLX comparisons on the same chip/model are being tracked — check back as more data is collected.

Verdict

MLX is fastest on Apple Silicon. Ollama is most practical for daily use.

On the M4 Max 64 GB, MLX delivers 148 tok/s on Qwen 3 4B Q4 — excellent throughput. For most developers running a coding assistant all day, Ollama's OpenAI-compatible API and ecosystem integrations outweigh the modest speed advantage of MLX. Use MLX when you need maximum throughput and are comfortable with Python. Use Ollama when you want a stable API that works with every LLM-aware app on the market.

Related pages

M4 Max (40-core GPU, 64 GB) benchmarks M4 Max vs M4 Pro Best Mac for local LLMs Quantization guide (Q4 vs Q8) Coding assistant setup guide Llama vs Qwen model comparison

Data

benchmarks.json — full dataset  ·  chips.json — chip summaries  ·  benchmarks.csv — CSV export

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