Is MLX faster than Ollama on Apple Silicon?

Yes, typically 15-30% faster at the same quantization. MLX is Apple's native framework built for Metal and unified memory. Ollama uses llama.cpp under the hood with Metal shaders. In practice on M4 Max, Qwen 3.5 9B at 4-bit hits ~45-60 tok/s in MLX vs ~35-50 tok/s in Ollama. The gap widens on the M4 Ultra and M3 Ultra.

Does Ollama support MLX natively?

Yes, as of Ollama 0.19 (March 2026). Ollama added an optional MLX backend for Apple Silicon Macs with 32 GB+ unified memory. It lifts Qwen 3.5 35B-A3B from ~45 tok/s (llama.cpp Metal) to ~70-80 tok/s (MLX) on M4 Max 32GB. Below 32 GB unified memory, Ollama falls back to llama.cpp automatically.

Which framework uses less memory on Mac?

MLX has ~10% lower memory overhead than GGUF via llama.cpp at the same quantization, because it uses unified memory natively without double buffering. For a 35B-A3B Q4 model, MLX peaks at ~19.5 GB while Ollama peaks at ~21.5 GB on the same Mac.

Should I use MLX or Ollama if I am starting fresh on Mac?

If you want zero setup and a full model library, start with Ollama. If you want maximum throughput, lowest memory, or need to fine-tune, go MLX via LM Studio (GUI) or mlx-lm (CLI). For a M4 Max or higher, MLX is the better long-term investment.

Can I run the same model in both MLX and Ollama?

Yes, but they use different file formats. Ollama uses GGUF files (cross-platform). MLX uses its own .safetensors-based format, typically published by mlx-community on Hugging Face. Most popular models (Qwen 3/3.5, Llama 3/4, Gemma 3, Mistral) have both GGUF and MLX builds available.

What is the best way to run Qwen 3.5 35B-A3B on a Mac?

Use MLX via LM Studio or mlx-lm. Download mlx-community/Qwen3.5-35B-A3B-MLX-4bit (~19.5 GB). On M4 Max 36GB or higher, expect 40-55 tokens/second with full 32K context. On M3 Ultra 512GB at MLX 8-bit, community benchmarks report 80+ tok/s. Ollama via llama.cpp is the fallback for Macs below 32 GB.

Does MLX work on M1, M2, M3 Macs or only M4?

MLX works on all Apple Silicon Macs from M1 onwards. Performance scales with memory bandwidth and GPU cores: M1 (100 GB/s) → M2 (100 GB/s) → M3 (150 GB/s) → M3 Pro (150 GB/s) → M4 (120 GB/s) → M4 Pro (273 GB/s) → M4 Max (546 GB/s) → M3 Ultra (800 GB/s). The Ultra chips are the unambiguous winners for large models.

April 20, 2026mlx, ollama, apple-silicon, benchmarks, mac, llama-cpp, m4

MLX vs Ollama on Apple Silicon (2026) — Real Benchmarks, Memory Usage & When to Use Each

MLX beats Ollama by 15-30% throughput on Apple Silicon and uses ~10% less memory. Full tok/s benchmarks for Qwen 3.5, Llama 4, Gemma 3 on M4 16GB → M3 Ultra 512GB.

If you are running local LLMs on a Mac, the framework choice — MLX or Ollama (llama.cpp) — affects both speed and memory by 10-30%. This guide has the concrete numbers, the trade-offs, and the right pick for each Mac tier.

Quick answers

Throughput: MLX is 15-30% faster at the same quantization on Apple Silicon
Memory overhead: MLX uses ~10% less VRAM due to native unified-memory handling
Ecosystem: Ollama has the bigger model library and easier onboarding; MLX has the best raw performance
Ollama 0.19+ includes an optional MLX backend for Macs with 32 GB+ unified memory
Setup friction: Ollama is one command; MLX via LM Studio is one click; MLX via mlx-lm requires Python setup
Best for Mac ≤24 GB: Ollama (llama.cpp Metal)
Best for Mac ≥32 GB: MLX (via LM Studio or mlx-lm)
Best for production serving: vLLM on Linux; MLX on Mac workstations

What MLX actually is

MLX is Apple's open-source machine-learning framework, released in late 2023 and purpose-built for Apple Silicon. It routes matrix operations directly to Metal and the Neural Engine, uses unified memory without double-buffering, and has a Python API similar to PyTorch/NumPy. mlx-lm is the LLM-specific layer — pip install mlx-lm and mlx_lm.generate --model <hf-repo> --prompt "..." gives you running inference in one line.

Key point: MLX is Apple's PyTorch-equivalent for Apple Silicon. When people talk about MLX for local LLMs, they usually mean the mlx-lm / mlx-vlm libraries that ship with pre-quantized models at mlx-community/* on Hugging Face.

What Ollama actually is

Ollama is a CLI wrapper around llama.cpp. It auto-downloads GGUF files, manages a local model registry, and exposes a REST API on port 11434. Under the hood on Mac, Ollama compiles llama.cpp with Metal backend by default, which is a cross-platform C++ library supporting CUDA/ROCm/Metal/Vulkan.

Starting with Ollama 0.19 (March 2026), Ollama added an experimental MLX backend specifically for Apple Silicon Macs with 32 GB+ unified memory. When enabled, it bypasses llama.cpp and runs inference through MLX directly — getting most of MLX's performance with Ollama's ergonomics.

Benchmarks — tok/s by Mac tier

These are decode-phase throughput numbers from community benchmarks (r/LocalLLaMA, appleinsider, Ollama's own 0.19 release testing), supplemented by internal fit-engine estimates for missing configurations.

Qwen 3.5 9B (Q4_K_M / MLX 4-bit, ~5.5 GB)

Mac	Ollama (llama.cpp Metal)	MLX	MLX advantage
M4 16GB (MacBook Air)	~22-28 tok/s	~25-35 tok/s	+15%
M4 Pro 24GB	~30-38 tok/s	~40-50 tok/s	+28%
M4 Max 36GB	~50-65 tok/s	~65-85 tok/s	+28%
M4 Max 64GB	~52-68 tok/s	~68-88 tok/s	+28%
M3 Ultra 512GB	~85-100 tok/s	~110-140 tok/s	+35%

Qwen 3.5 35B-A3B MoE (Q4_K_M / MLX 4-bit, ~19.5-21.4 GB)

Mac	Ollama	MLX	MLX advantage
M4 Pro 24GB	~15-20 tok/s (tight)	~18-25 tok/s (tight)	+20%
M4 Max 36GB	~30-40 tok/s	~40-55 tok/s	+30%
M4 Max 64GB	~45-58 tok/s	~55-70 tok/s	+22%
M4 Max 128GB	~50-65 tok/s	~60-80 tok/s	+20%
M3 Ultra 512GB	~60-75 tok/s	~80-100 tok/s	+30%

Ollama 0.19 with the optional MLX backend closes this gap almost entirely on 32 GB+ Macs, reaching 85% of pure MLX throughput.

Llama 4 Scout 109B (Q4_K_M, ~61 GB)

Mac	Ollama	MLX	Notes
M4 Max 64GB	OOM	⚠️ tight (59 GB + 5 GB overhead exceeds comfortable headroom)	Skip; use 35B-A3B MoE
M4 Max 128GB	~22-28 tok/s	~28-38 tok/s	Comfortable
M3 Ultra 512GB	~35-45 tok/s	~45-60 tok/s	Plenty of context room

Gemma 3 27B (Q4_K_M, ~15.1 GB)

Mac	Ollama	MLX
M4 Pro 24GB	~18-24 tok/s	~22-30 tok/s
M4 Max 36GB	~32-42 tok/s	~42-55 tok/s
M4 Max 64GB	~36-46 tok/s	~48-62 tok/s

Memory usage — MLX vs Ollama

On a Mac, unified memory is shared between CPU, GPU, and Neural Engine. Both frameworks load the model once, but internal buffer management differs.

Model + Quant	Ollama peak RAM	MLX peak RAM	Difference
Qwen 3.5 9B Q4	~6.8 GB	~6.0 GB	MLX -12%
Qwen 3.5 27B Q4	~18.1 GB	~16.8 GB	MLX -7%
Qwen 3.5 35B-A3B Q4	~23.2 GB	~20.5 GB	MLX -12%
Llama 4 Scout Q4	~65 GB	~61 GB	MLX -6%
Gemma 3 27B Q4	~16.8 GB	~15.4 GB	MLX -8%

Practical implication on a 24 GB Mac: Ollama may OOM on Qwen 3.5 35B-A3B at default settings (~23 GB peak vs 24 GB total - 3.5 GB macOS = ~20.5 GB usable). MLX fits tightly. Below 24 GB unified memory, drop to a 9B or Gemma 3 12B for comfort.

Setup — side by side

Ollama (easiest)

# Install
curl -fsSL https://ollama.com/install.sh | sh

# Run
ollama run qwen3.5:9b
# or
ollama run qwen3.5:35b-a3b

Pros: one-command setup, model registry, REST API, cross-platform. Cons: slower than MLX on Mac, larger memory overhead, limited to GGUF quantization.

Ollama with MLX backend (0.19+)

# Enable MLX backend (Mac with 32 GB+ unified memory)
export OLLAMA_BACKEND=mlx

ollama run qwen3.5:35b-a3b

Pros: 85% of MLX performance with Ollama ergonomics. Cons: requires 32 GB+ unified memory; some models have known bugs on Ollama 0.18.2 (fixed in 0.19).

MLX via LM Studio (GUI)

Download LM Studio from lmstudio.ai
Open the Discover tab
Search a model (e.g. "Qwen3.5-35B-A3B")
Filter by "MLX" — grab the 4-bit build
Load and chat

Pros: best-in-class GUI, model browser, chat UI, OpenAI-compatible API. Cons: GUI app (~800 MB), not scriptable as cleanly as mlx-lm.

MLX via mlx-lm (CLI / Python)

pip install mlx-lm

mlx_lm.generate \
  --model mlx-community/Qwen3.5-35B-A3B-MLX-4bit \
  --prompt "Write a haiku about unified memory." \
  --max-tokens 256

For programmatic use:

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Qwen3.5-35B-A3B-MLX-4bit")
response = generate(model, tokenizer, prompt="Explain MoE in one paragraph.", max_tokens=256)
print(response)

Pros: maximum performance, scriptable, fine-tuning support, full control. Cons: Python environment management; more setup for non-engineers.

llama.cpp directly (for advanced tuning)

git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp && make -j

./llama-cli -m ~/models/Qwen3.5-9B-Q5_K_M.gguf \
  -n 512 --color -cnv \
  -p "You are a local LLM assistant."

Pros: full control over context size, RoPE scaling, KV cache quantization, prompt caching. Cons: more flags, compile step, GGUF-only.

When to pick which

Scenario	Pick
First local LLM on any Mac	Ollama
MacBook Air M4 16 GB, casual chat	Ollama
M4 Pro 24 GB, want faster Qwen 3.5 27B	Ollama 0.19 MLX backend or LM Studio MLX
M4 Max 36 GB+, heavy daily use	MLX via LM Studio
M3 Ultra / Mac Studio, serving agents	MLX via mlx-lm, script everything
Cross-platform dev (Mac + Linux rig)	Ollama or llama.cpp — share GGUF files
Fine-tuning / LoRA / agent training	MLX exclusively (`mlx-lm.lora`)
Want OpenAI-compatible REST API	LM Studio (built in) or Ollama (`/api/chat`)

Common pitfalls

macOS 26 + Ollama 0.18.2 on M4/M5: known bug causing Metal shader crashes under sustained load. Fix: upgrade to Ollama 0.19 or switch to MLX.
MLX memory pressure: macOS swaps aggressively when unified memory crosses ~90% usage. Close Chrome, Docker, IDEs before running 27B+ models on 24 GB Macs.
MLX model format mismatch: don't point MLX at a GGUF file or vice versa. MLX models are at mlx-community/*; GGUF at unsloth/*, bartowski/*, lmstudio-community/*.
Ollama default context: 2048 tokens by default. Set OLLAMA_CONTEXT_LENGTH=8192 (or higher) for real coding work.
Quantization picking: MLX 4-bit ≈ GGUF Q4_K_M. MLX 8-bit ≈ GGUF Q8_0. For identical quality comparison, match these.