From Ollama to MLX: Achieving 2-3x Performance on Apple Silicon

I've spent the last year running local language models on my Mac Studio using Ollama. It's been convenient—a single command pulls models and spins up an API. But when I ran benchmarks comparing Ollama against MLX (Apple's machine learning framework), the numbers were striking: MLX delivered 230 tokens/second while Ollama maxed out at 20-40 tokens/second on the same hardware. For a developer trying to keep costs down by running models locally, that gap felt like leaving free performance on the table.

The frustrating part? Ollama still doesn't support MLX natively. But that's changed the conversation entirely. There's a thriving ecosystem of MLX-first tools emerging, and for Mac users, they're worth serious consideration.

Why Ollama Leaves Performance on the Table

Ollama is built around <code>llama.cpp</code>, a C/C++ inference engine optimized for CPU-only deployment and GGUF models. While it's excellent for portability across platforms (Linux, Windows, macOS, Windows), it treats Apple Silicon as just another CPU—not the uniquely powerful architecture it is.

The fundamental issue: Ollama doesn't directly use Apple's Metal GPU or the unified memory architecture that makes M1-M4 chips special. Instead, models are quantized into GGUF format and run through llama.cpp's generic Metal backend, which never reaches the efficiency ceiling of an Apple-optimized stack.

Real-world performance shows the cost. On an M4 MacBook Pro, Ollama achieves around 9-13 tokens/second on mid-sized models (12-22B parameters). The same model in MLX hits 30-45 tokens/second—a 40% speed improvement just by switching frameworks. On larger models, the gap widens to 2-3x.

Ollama's strength is ergonomics—it prioritizes developer convenience with a one-command setup and a REST API compatible with OpenAI's interface. But convenience at the expense of available performance eventually becomes a liability, especially when running models locally is fundamentally about keeping costs down.

Enter MLX: Apple's Response to CUDA

MLX is Apple's answer to NVIDIA's CUDA ecosystem for on-device AI. Released in late 2023 and actively developed by Apple's machine learning team, MLX is built from the ground up to exploit Apple Silicon's unified memory architecture—the shared CPU/GPU memory that makes Apple's chips fundamentally different from traditional GPU-centric designs.

What Unified Memory Means for Performance

Traditional discrete GPUs (like NVIDIA cards) require constant data shuttling between CPU and GPU memory, which creates latency and power overhead. Apple Silicon uses a single pool of memory accessible by CPU and GPU simultaneously. This eliminates the bottleneck entirely.

MLX's design exploits this ruthlessly:

• Direct GPU acceleration through Metal performance primitives
• Zero-copy memory access between CPU and GPU
• Automatic optimization of compute kernels for each chip (M1, M2, M3, M4)
• Efficient quantization with mixed-bit support (3-bit, 4-bit, 6-bit, 8-bit) tightly integrated with hardware acceleration

The result: Llama 3.2 3B (1.8GB) generates at 152 tokens/second on an M4 Max. For context, that's faster per-token speed than many cloud API endpoints while running entirely offline, on-device, with zero inference costs.

The MLX Ecosystem: More Than One Tool

MLX isn't monolithic. The community has built several entry points:

1. mlx-lm (Official Python Package)

The canonical way to run models. Install via <code>pip install mlx-lm</code>, then:

mlx_lm.chat --model mlx-community/Mistral-7B-Instruct-v0.3-4bit

This works instantly and supports over 1,000 pre-converted models from the mlx-community on HuggingFace. You can also fine-tune locally using LoRA (Low-Rank Adaptation), something Ollama never enabled.

Best for: Terminal-first developers, Python automation, fine-tuning workflows.

2. LM Studio (GUI, with MLX Engine Support)

LM Studio added MLX support in version 0.3.4 (October 2024). For users who prefer a graphical interface, it's the best middle ground. Models run 20-30% faster with MLX vs GGUF, with lower memory consumption.

The workflow is straightforward: select a model from the UI, switch the engine from "llama.cpp" to "MLX" in settings, and chat. That's it.

Performance gain: 20-30% faster than GGUF equivalents, lower RAM footprint.
Best for: GUI users, development/testing, quick model switching.

3. Swama (Swift-Native, MLX-Only)

Swama is a fascinating outlier—an entirely new open-source runtime written in Swift, optimized exclusively for MLX on macOS. Built by Trans-N (a Tokyo-based AI firm), it's designed to push MLX to its absolute limits.

Benchmarks speak: Swama is 1.5-2x faster than Ollama, achieving performance close to MLX's theoretical ceiling. It supports the full OpenAI API surface (chat completions, embeddings, streaming) and recently added multimodal vision support.

The trade-off: Swama is new (released mid-2024) with a smaller community than Ollama or LM Studio. But if you're locked into Apple Silicon and care about raw performance, it's worth a trial.

# Instant model aliases, auto-downloads if needed
swama run qwen3 "Explain distributed systems"
swama run llama3.2 "Translate this to French"
swama serve --port 28100  # OpenAI-compatible API

Best for: Performance-obsessed users, Mac-native deployments, production Apple Silicon serving.

4. llm-mlx (CLI Plugin by Simon Willison)

For users of Simon Willison's <code>llm</code> CLI tool, there's a dedicated MLX plugin that integrates seamlessly. Achieved 152 tokens/second on Llama 3.2 3B in practice—remarkable for such a small model.

Best for: <code>llm</code> CLI enthusiasts, minimalist workflows.

5. Msty and LM Studio (Proprietary Alternatives)

Msty is a newer closed-source tool offering a hybrid experience: local + cloud model switching. LM Studio remains the most polished GUI option. Both support MLX, but neither matches Swama's performance focus.

Benchmarks: The Numbers That Matter

Real-world testing on Mac Studio M2 Ultra (192GB) with Qwen-2.5 models:

Framework	Tokens/Second	Latency (P99)	Memory (4-bit 7B)	Notes
MLX	~230	12ms	4-5GB	Highest sustained throughput; most stable per-token latency
Swama	~190-200	13ms	4.5-5GB	MLX-native, slightly lower than pure MLX due to server overhead
MLC-LLM	~190	13ms	4.5GB	Lower first-token latency on moderate prompts; paged KV caching
Ollama	20-40	50-100ms	6-7GB	Lowest throughput; higher latency variance
LM Studio (GGUF)	20-35	60ms	6-8GB	Broader model support; slower than MLX
LM Studio (MLX)	30-50	40ms	4.5GB	20-30% faster than GGUF variant

On M4 MacBook Pro (lower memory bandwidth):

• MLX: 45-65 tokens/second (larger models drop further)
• Ollama: 9-15 tokens/second
• Gap: 40% faster at minimum, up to 4-5x on larger models

Cold start times (model load + initialization):

• Ollama: ~0.6s
• LM Studio (MLX): ~2-3s
• MLX (direct): ~3-5s
• Swama: ~1-2s

Once loaded, MLX wins decisively. Cold-start differences become negligible in production.

Long-Context Handling (Critical for Real Work)

Ollama degrades sharply beyond 32k-token contexts. MLX maintains stable throughput up to 32k via rotating KV caches. For longer contexts, MLC-LLM's paged attention becomes the better choice—but MLX's memory efficiency still wins for typical chat/code-generation workflows (4k-32k tokens).

The Hidden Cost: Quantization and Model Availability

Ollama uses GGUF exclusively. While GGUF is stable, it's a closed ecosystem. Converting models to GGUF is a one-way process; you can't easily go back.

MLX offers:

• Native GPTQ/AWQ support: Leverage community quantization recipes
• Mixed-bit formats: 3-bit, 4-bit, 6-bit, 8-bit variants, hardware-optimized
• 1000+ pre-converted models: mlx-community on HuggingFace, continuously expanding
• Fine-tuning on-device: Use LoRA to customize models without cloud services

For developers building production systems, this flexibility matters. You're not locked into one quantization path.

Practical Setup: Three Approaches

Approach 1: Terminal + mlx-lm (Fastest Start)

pip install mlx-lm
mlx_lm.chat --model mlx-community/Mistral-7B-Instruct-v0.3-4bit

Time to inference: 2 minutes. Cost: Free. Performance: Best-in-class.

Approach 2: LM Studio GUI (Best GUI Experience)

Download LM Studio from lmstudio.ai. Version 0.3.4+ ships with MLX support built-in. Switch the engine in settings and start chatting.

Time to first chat: 5 minutes. Cost: Free. Performance: 20-30% faster than Ollama.

Approach 3: Swama for Maximum Performance

brew tap Trans-N-ai/swama
brew install swama
swama run mistral-7b "Your prompt here"
swama serve --port 28100  # OpenAI API on localhost:28100

Time to setup: 3 minutes. Cost: Free (open-source). Performance: 1.5-2x faster than Ollama.

When Ollama Still Makes Sense

MLX isn't a universal replacement. Ollama remains the right choice when:

• Cross-platform matters: You need the same runtime on macOS, Linux, and Windows
• Community size: You want the largest ecosystem of pre-built models and integrations
• Simplicity first: You're prototyping and don't want framework noise
• GGUF models only: You have existing GGUF assets and don't want to convert

For pure Mac deployments focused on performance? MLX wins.

Cost Savings from Local Inference

Running Llama 3.2 7B locally costs essentially nothing after hardware purchase. The same model on AWS Bedrock or Claude API costs $0.20 per million tokens (inference).

Monthly cost comparison (100M tokens):

• MLX on Mac: $0 (amortized hardware cost: ~$8/month for electricity)
• Claude API: ~$20
• AWS Bedrock: ~$20

Over a year, switching from API inference to MLX saves $240 while improving latency (local is instant vs API roundtrip) and privacy (data never leaves your machine).

Suggested Article Titles

1. "From Ollama to MLX: Why Apple Silicon Deserves Better Performance" — Focuses on performance narrative
2. "MLX on macOS: The 2x-3x Performance Boost Your Mac Studio Needs" — Emphasizes speed gains
3. "Beyond Ollama: Unlocking Apple Silicon's True Potential with MLX" — Highlights untapped power
4. "Running Local LLMs on Mac: Why MLX Outpaces Ollama (and How to Get Started)" — Practical, comparison-focused
5. "Mac Studio AI Runtime Showdown: MLX vs Ollama Performance Deep Dive" — Technical deep dive

The Road Ahead: Ollama MLX Support

Ollama's core team commenced development on MLX support in early 2025 (GitHub Issue #8459). As of late 2025, this work remains in beta/development phase—not yet production-ready. If/when Ollama ships native MLX support, it would unify the ecosystem and eliminate the need for multiple tools.

Until then, using MLX directly (via mlx-lm, LM Studio, or Swama) is the only way to access Apple Silicon's true performance potential.

Final Verdict

Ollama is an excellent entry point to local LLMs, but it's fundamentally a compromise on Apple hardware—trading performance for portability. Once you're locked into macOS and care about cost/efficiency, MLX becomes the obvious choice.

Start with LM Studio if you want a GUI. Move to mlx-lm for terminal workflows. Consider Swama if raw performance is your only metric. In each case, you'll gain 40-200% speed improvements over Ollama while keeping everything local, private, and free.

The Mac is powerful. Let your inference framework match that power.

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Tool Comparison Reference

Tool	Setup Time	Performance	GUI	MLX Support	Best For
Ollama	2 min	20-40 t/s	❌	⏳ (In Dev)	Prototyping, cross-platform
mlx-lm	2 min	230 t/s	❌	✅ Native	Terminal, Python, fine-tuning
LM Studio	5 min	30-50 t/s (MLX engine)	✅	✅	GUI users, development
Swama	3 min	190-200 t/s	⚙️ CLI	✅ Native	Max performance, production
Msty	5 min	Varies	✅	✅	Hybrid local/cloud workflows

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Further Reading & Resources

• MLX GitHub: github.com/ml-explore/mlx
• mlx-community HuggingFace: huggingface.co/mlx-community
• MLX Examples: github.com/ml-explore/mlx-examples
• Swama (Open-Source): github.com/Trans-N-ai/swama
• Academic Benchmarks: Production-Grade Local LLM Inference on Apple Silicon (Persistent Systems)

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References \& Resources

Official MLX Ecosystem

MLX Framework | https://github.com/ml-explore/mlx
Apple's array framework for machine learning on Apple Silicon, with unified memory architecture, lazy computation, and multi-device support (CPU/GPU).

mlx-lm | https://github.com/ml-explore/mlx-lm
Official Python package for running and fine-tuning LLMs with MLX; provides CLI tools for model conversion, chat inference, and LoRA-based training.

mlx-examples | https://github.com/ml-explore/mlx-examples
Comprehensive collection of MLX examples covering transformers, image generation (Stable Diffusion, FLUX), speech recognition (Whisper), and multimodal models (CLIP, LLaVA).

mlx-community (HuggingFace) | https://huggingface.co/mlx-community
HuggingFace organization hosting 1,000+ pre-converted MLX models, including Llama, Mistral, Qwen, Phi, and fine-tuned variants in mixed-bit quantization formats.

Performance Analysis \& Benchmarks

"Run LLMs on macOS using llm-mlx" | https://simonwillison.net/2025/Feb/15/llm-mlx/
Simon Willison's tutorial on achieving 152 tokens/second with Llama 3.2 3B using the llm-mlx plugin; covers integration with the popular <code>llm</code> CLI tool.

"Run LLMs on macOS using llm-mlx and Apple's MLX Framework" | https://simonw.substack.com/p/run-llms-on-macos-using-llm-mlx-and
Extended Substack guide covering MLX basics, performance comparison, and practical setup for macOS users.

"Local AI with MLX on the Mac: Practical Guide for Apple Silicon" | https://www.markus-schall.de/en/2025/09/mlx-on-apple-silicon-as-local-ki-compared-with-ollama-co/
Detailed benchmarks comparing MLX, Ollama, and other frameworks on Mac Studio; includes throughput, latency, and memory usage analysis (November 2025).

"From Ollama to MLX: My Journey with Apple's Game-Changing AI Framework" | https://technovangelist.com/blogs/from-ollama-to-mlx---my-journey-with-apples-game-changing-ai-framework
Practitioner's perspective on migration from Ollama to MLX, covering performance gains, setup, and real-world usage.

"Tested Local LLMs on a Maxed-Out M4 MacBook Pro" | https://www.reddit.com/r/ollama/comments/1j0by7r/tested_local_llms_on_a_maxed_out_m4_macbook_pro/
Community benchmark post comparing Ollama and MLX on high-end M4 hardware; provides real-world throughput data.

"Production-Grade Local LLM Inference on Apple Silicon" | https://arxiv.org/pdf/2511.05502.pdf
Academic benchmark paper (Persistent Systems, September 2025) comparing MLX, MLC-LLM, Ollama, and llama.cpp across multiple Apple Silicon configurations and model sizes.

MLX-Native Runtimes

Swama | https://github.com/Trans-N-ai/swama
Open-source Swift-native MLX runtime achieving 1.5-2x faster inference than Ollama; includes OpenAI-compatible API and multimodal vision support.

"Swama: Locally Optimized LLM Execution Platform for macOS" | https://trans-n.ai/2025/06/12/250612-2/
Official announcement and documentation for Swama, covering features, performance metrics, and setup instructions.

Specialized Tools \& Projects

MLX Llama TTS Assistant | https://github.com/streamlinecoreinitiative/MLX_Llama_TTS_MPS
On-device AI assistant running Llama 3 4-bit with Kokoro text-to-speech on Apple Silicon; demonstrates end-to-end MLX application.

mlx-knife | https://github.com/mzau/mlx-knife
Lightweight Ollama-like CLI tool for managing and running MLX models locally; features streaming chat, model health checks, and cache management (v1.0+, August 2025).

pyOllaMx | https://github.com/kspviswa/pyOllaMx
Community project bridging Ollama and MLX ecosystems; enables GGUF/MLX model conversions and hybrid workflows.

Nexa SDK | https://github.com/NexaAI/nexa-sdk
Cross-platform local inference engine supporting GGUF, MLX, and Qualcomm NPU models; includes OpenAI-compatible API and multimodal support.

GUI \& User-Friendly Tools

LM Studio | https://lmstudio.ai/
Full-featured GUI application for local LLM inference with MLX engine support (v0.3.4+); achieves 20-30% performance boost over GGUF backend.

"Ollama vs LM Studio on macOS" | https://www.chrislockard.net/posts/ollama-vs-lmstudio-macos/
Comparative analysis of Ollama and LM Studio for macOS users; covers setup, performance, and use-case recommendations.

Msty | https://msty.ai/
Modern closed-source desktop application supporting hybrid local/cloud inference; compatible with MLX and cloud providers.

Model Conversion \& Fine-Tuning Guides

"A Deep Dive on Converting MLX Models to GGUF for Ollama" | https://www.arsturn.com/blog/from-fine-tune-to-front-line-a-deep-dive-on-converting-mlx-models-to-gguf-for-ollama
Step-by-step guide covering MLX fine-tuning, model fusion, GGUF conversion, and Ollama deployment; includes practical code examples.

MLX-to-GGUF Conversion | https://github.com/ml-explore/mlx/discussions/1507
GitHub discussion on converting fine-tuned LoRA adapters from MLX format to GGUF for Ollama compatibility.