Best Local LLMs for Mac in 2026 — M1, M2, M3, M4 Tested

📚 More on this topic: Running LLMs on Mac M-Series · Ollama vs LM Studio · llama.cpp vs Ollama vs vLLM · VRAM Requirements

Every Mac with Apple Silicon can run local LLMs. The question isn’t whether — it’s which model, and whether it’ll be fast enough to actually use. A model that “fits” in memory but generates 3 tokens per second isn’t useful. A smaller model at 40 tok/s is.

This guide gives you specific model recommendations for every Mac tier, with real performance numbers. No “it depends” — concrete picks you can install right now.

For setup instructions and general architecture details, see our complete M-series guide. This article focuses on which models to run.

Why Mac Is Different

Unified Memory Changes the Math

On a PC, your GPU has its own dedicated VRAM (typically 8-24GB). Models that don’t fit in VRAM either won’t run or crawl at 2-3 tok/s via offloading.

On Mac, there’s no separate GPU memory. Your entire RAM pool — 8GB to 128GB — is shared between CPU and GPU. A Mac Mini with 48GB can load a 32B model that would need a $700+ used RTX 3090 on PC. A Mac Studio with 128GB runs 70B models that require $3,000+ in dual GPUs.

The tradeoff: Mac’s memory bandwidth is lower than a discrete GPU’s. An RTX 3090 pushes 936 GB/s. The M4 Pro pushes 273 GB/s. Token generation speed is directly proportional to memory bandwidth, so Mac is 30-60% slower per token for models that fit in a GPU’s VRAM. But for models that don’t fit — Mac wins by running them at all.

Memory Bandwidth Matters More Than Chip Generation

This is the counterintuitive part. An M3 Max (400 GB/s) generates tokens faster than an M4 Pro (273 GB/s), despite the M4 Pro being a newer chip. For LLM inference, bandwidth is the bottleneck, not compute.

Before buying: check the bandwidth of your specific chip, not just the generation. A $1,799 Mac Mini M4 Pro 48GB will be slower per token than a $2,700 Mac Studio M4 Max 64GB, even if you’re running the same model.

Best Models by Mac Tier

8GB Macs (M1 / M2 / M3 / M4 base)

macOS needs 2-3GB for itself. You have about 5-6GB for a model. This limits you to 3B models comfortably or 7B-8B models with aggressive quantization and short context.

The pick: Llama 3.2 3B for general use. It’s fast, fits easily, and leaves room for context. If you need better reasoning and can tolerate shorter conversations, try Phi-4 Mini.

Skip: Any 7B+ model at Q4 or higher. It’ll technically load but you’ll have 1-2GB for context and system, which means frequent crashes and 4K token limits.

Honest take: 8GB Macs are for casual use with small models. If you’re serious about local AI, the memory upgrade is worth it. An M4 MacBook Air starts at 16GB now — that’s the minimum to buy in 2026.

16GB Macs (M1 Pro 16GB / M2 16GB / M3 16GB / M4 16GB)

The sweet spot for 7B-8B models. You have ~12-13GB available for the model and context.

The pick: Qwen 3 8B (Q4_K_M). It’s the best 8B model available in early 2026 — strong instruction following, good at code, solid reasoning, and the /think mode gives you chain-of-thought when you need it. Run it at Q4_K_M for the best quality-to-size ratio.

Runner-up for coding: Qwen 2.5 Coder 7B. If your primary use case is code completion and generation, this dedicated coding model outperforms general-purpose 8B models on programming tasks.

For reasoning: DeepSeek-R1-Distill-Qwen-8B. When you need step-by-step problem solving, the R1 distill’s chain-of-thought training shows. It’s slower because it generates reasoning tokens, but the answer quality is noticeably better on math and logic.

24GB Macs (M2 Pro 24GB / M4 16GB / M4 Pro 24GB)

The 14B tier opens up. This is where models start getting genuinely good.

The pick: Qwen 3 14B (Q4_K_M). The jump from 8B to 14B is significant — better reasoning, more coherent long-form output, and noticeably fewer hallucinations. This is the Mac Mini M4 Pro 24GB sweet spot.

For reasoning: DeepSeek-R1-Distill-14B. The best reasoning model at this memory tier. Chain-of-thought responses are slower but more accurate than any general-purpose model of the same size.

Don’t bother with: Qwen 3 32B at Q3. It technically fits in 24GB but you’ll have ~6GB for context and overhead. The quality at Q3 quantization is degraded enough that a 14B at Q4 is usually better. Wait for more memory.

36-48GB Macs (M3 Pro 36GB / M4 Pro 48GB / M2 Max 32-48GB)

This is where it gets exciting. 32B models run comfortably and the quality jump is dramatic.

The pick: Qwen 3 32B (Q4_K_M). This is the model that makes Mac local AI worthwhile. Expert-level responses on complex topics. Strong coding ability. Good creative writing. The /think mode handles multi-step reasoning that 14B models fumble. At 48GB you have room for 16K+ context alongside the model.

For coding: Qwen 2.5 Coder 32B. If you write code all day, this is your model. It understands complex codebases, generates better functions, and catches more bugs than general-purpose models. 48GB gives you comfortable room for this plus context.

For reasoning: DeepSeek-R1-Distill-32B. The strongest reasoning model you can run locally on consumer hardware. Chain-of-thought on math, logic, and analysis tasks rivals much larger models.

The Mac Mini M4 Pro 48GB at $1,799 is the best-value setup in this tier. Silent, low power, runs 32B models all day. It’s genuinely the sweet spot for local AI on Mac.

64-96GB Macs (M3 Max 64-96GB / M4 Max 64GB)

70B models become practical. You’re matching what cloud APIs deliver.

The pick: Llama 3.3 70B (Q4_K_M). The 70B tier is a significant leap. These models match GPT-3.5 and approach GPT-4 on many tasks. At 8-15 tok/s on M4 Max, it’s slower than reading speed but perfectly usable for interactive chat. You get 128K context support, strong multilingual performance, and excellent instruction following.

Alternative: Qwen 3 32B at Q6/Q8. If you prefer speed over model size, running 32B at higher quantization gives you better quality than Q4 and 15-28 tok/s. You won’t miss 70B on most everyday tasks.

128GB+ Macs (M4 Max 128GB / M3 Ultra 192GB)

The “no compromises” tier. Run whatever you want.

The pick: Llama 3.1 70B at Q6 or Q8. Higher quantization means less quality loss. At 128GB you have room for the model at Q6 (~55GB) plus generous context. The quality improvement over Q4 is subtle but real, especially on complex reasoning and coding.

If you have 192GB (M3 Ultra): Qwen3 235B-A22B is the most capable model you can run locally. It’s a Mixture of Experts model (22B active per token out of 235B total) that delivers frontier-class quality. Expect 5-10 tok/s — slow, but nothing else matches it locally.

MLX vs Ollama vs LM Studio: Which to Use

All three work on Apple Silicon. The difference is speed, ease, and interface.

When to Use MLX

MLX is Apple’s native machine learning framework, built from the ground up for unified memory. It’s consistently 20-30% faster than llama.cpp across model sizes, with the gap widening on larger models.

Use MLX when:

• You want maximum tok/s and are comfortable with Python
• You’re building applications that need fast local inference
• You’re running larger models where the 20-30% speed gap matters more

pip install mlx-lm
mlx_lm.generate --model mlx-community/Qwen3-8B-4bit --prompt "Hello"

The mlx-community organization on HuggingFace maintains hundreds of pre-converted models. If it exists in GGUF, it probably exists in MLX format too.

When to Use Ollama

Ollama wraps llama.cpp with dead-simple model management. One command to install, one command to run.

Use Ollama when:

• You want the fastest setup possible
• You need an API server for other apps (Open WebUI, Continue, etc.)
• You’re new to local LLMs
• You want to switch between models quickly

ollama run qwen3:8b

For a detailed comparison of inference engines, see our llama.cpp vs Ollama vs vLLM guide.

When to Use LM Studio

LM Studio gives you a ChatGPT-like interface with model browsing, parameter controls, and conversation management. Recent versions use MLX as the backend on Mac, closing the speed gap with MLX-LM.

Use LM Studio when:

• You prefer a GUI over terminal
• You want to browse and compare models visually
• You need fine control over temperature, top-p, and other sampling parameters

Models That Technically Fit But Actually Crawl

This is the trap. A model can load into memory and still be useless.

The rule of thumb: the model file should be no more than 60-70% of your total memory. That leaves room for macOS, the KV cache (context), and framework overhead. A 20GB model on a 48GB Mac is comfortable. A 20GB model on a 24GB Mac is a knife’s edge.

If you’re right at the limit, drop to a lower quantization or a smaller model. A snappy 14B model is more useful than a sluggish 32B.

The Best Mac for Local AI in 2026

The Mac Mini M4 Pro 48GB at $1,799 is the sweet spot. It runs 32B models comfortably, sits silently on your desk, draws 30W under AI load, and costs less per year in electricity than a single month of ChatGPT Plus.

The Bottom Line

The model matters more than the tool. Pick the right model for your memory tier, use whichever app you’re comfortable with, and don’t try to squeeze a model that’s too big. A fast small model beats a slow big one every time.

Quick decision tree:

• 8GB: Llama 3.2 3B via Ollama. Accept the limitations.
• 16GB: Qwen 3 8B via Ollama or LM Studio. This is where it gets useful.
• 24GB: Qwen 3 14B. The Mac Mini M4 Pro entry config.
• 48GB: Qwen 3 32B. The sweet spot — expert-quality responses on consumer hardware.
• 64GB+: Llama 3.3 70B. Cloud-API quality, running on your desk.
• 128GB+: Whatever you want. You’ve won the local AI hardware lottery.

Install Ollama (curl -fsSL https://ollama.com/install.sh | sh), pull your model, and start chatting. Your Mac is already a capable AI workstation — you just need to pick the right model for it.