Which Mac Mini M4 Pro RAM config should I buy for local AI?

48 GB is the sweet spot. It runs 32B-class models (Qwen 3 32B, Qwen 2.5 Coder 32B, DeepSeek R1 Distill 32B) with headroom and costs significantly less than 64 GB. Pick 64 GB only if you specifically need to run a 70B-class model — even then it's tight (42 GB model + headroom on 48 GB cap).

How fast does the Mac Mini M4 Pro run local LLMs?

The M4 Pro has 273 GB/s memory bandwidth. At ~70% efficiency on Q4_K_M models that gives roughly: 8B model → ~38 tok/s, 14B → ~22 tok/s, 32B → ~10 tok/s, 70B → ~5 tok/s (and 70B only fits on the 64 GB config with most of your stack closed). MLX models in LM Studio typically run 10-20% faster than GGUF in Ollama.

Can the Mac Mini M4 Pro run Llama 3.3 70B?

On the 64 GB variant, yes — but tight. Q4_K_M is ~42 GB, leaving ~6 GB for context and macOS. You need to keep Chrome, Docker, and other dev tools closed. On 48 GB, Q3_K_M (~32 GB) fits with similar trimming. On 24 GB it won't fit at any quant.

How much does running a Mac Mini M4 Pro for local AI actually cost?

Idle: 4-7W. Inference: 30-50W under continuous load. At a typical US electricity rate of $0.16/kWh, an 8-hour-a-day mixed workload runs $20-30/year — roughly the cost of one month of Claude Pro. Hardware breakeven vs cloud APIs depends on token volume.

Should I use Ollama, LM Studio, or llama.cpp on Mac Mini M4 Pro?

All three work. Ollama: best CLI ergonomics, easy model management, integrates with the most agents (Claude Code, Aider, Cursor). LM Studio: better MLX support (10-20% faster), nicer GUI for casual use. llama.cpp: lowest-level control, best for benchmarks. Most users start with Ollama and switch to LM Studio when they want MLX speed.

Best local AI to run on Mac Mini M4 Pro (2026 guide)

Pick your tier

Which RAM config to buy.

24 GB · the entry tier

Runs 7B–14B models comfortably. Great for everyday assistants (Llama 3.1 8B, Qwen 3 14B equivalents, Phi-4 14B). Good speculative-decoding host for a small draft model.

Llama 3.1 8B · ~38 tok/s
Phi-4 14B · ~22 tok/s
Won't fit anything 32B+

48 GB · the sweet spot

Runs every 32B model with headroom — Qwen 2.5 Coder 32B, Qwen 3 32B, DeepSeek R1 Distill 32B. Best price-per-capability tier on the M4 Pro. What r/LocalLLaMA recommends most.

Qwen 2.5 Coder 32B · ~10 tok/s
Qwen 3 32B · ~10 tok/s
Won't comfortably fit 70B

64 GB · the 70B tier

The only M4 Pro config that fits Llama 3.3 70B at Q4_K_M. Tight: ~42 GB model + ~6 GB context + macOS. Close Chrome and Docker. For comfortable 70B work, the M4 Max with 96 GB is the more honest recommendation.

Llama 3.3 70B · ~5 tok/s · tight
Better fit on M4 Max 96 GB
Babysit recommended for long sessions

What runs on each tier

Models that fit (with headroom).

Every model in our database, sorted by Q4_K_M memory cost. Tier assumes ~40% headroom for context, OS, and a normal dev stack. tok/s estimates are single-batch on the M4 Pro's 273 GB/s bandwidth at ~70% efficiency.

Model	Q4_K_M size	Min Mac mini tier	tok/s on M4 Pro
Gemma 3 1B →	0.7 GB	24 GB+	~273 tok/s
TinyLlama 1.1B →	0.8 GB	24 GB+	~239 tok/s
Llama 3.2 3B →	2.0 GB	24 GB+	~96 tok/s
Phi-4 Mini 3.8B →	2.5 GB	24 GB+	~76 tok/s
Gemma 3 4B →	3.0 GB	24 GB+	~64 tok/s
Qwen 3 4B →	3.0 GB	24 GB+	~64 tok/s
Llama 3.1 8B →	4.6 GB	24 GB+	~42 tok/s
Qwen 3.5 9B →	5.5 GB	24 GB+	~35 tok/s
Qwen 3 8B →	5.5 GB	24 GB+	~35 tok/s
Phi-4 14B →	8.7 GB	24 GB+	~22 tok/s
Qwen 3 14B →	9.5 GB	24 GB+	~20 tok/s
Kimi VL A3B Thinking →	9.5 GB	24 GB+	~20 tok/s
Mistral Small 3.1 24B →	14.4 GB	24 GB+	~13 tok/s
Gemma 3 27B →	16.5 GB	24 GB+	~12 tok/s
Qwen 2.5 Coder 32B →	19.0 GB	48 GB+	~10 tok/s
Qwen 3 32B →	19.0 GB	48 GB+	~10 tok/s
Qwen 3 30B-A3B (MoE) →	19.0 GB	48 GB+	~10 tok/s
DeepSeek R1 Distill 32B →	19.0 GB	48 GB+	~10 tok/s
Llama 3.3 70B →	40.6 GB	64 GB+	~5 tok/s

Setup

Run a model in 5 minutes.

# 1. install Ollama (or LM Studio for MLX speed)
brew install --cask ollama

# 2. install DevPulse — pre-flight + babysit
brew install --cask devpulse        # (coming soon — DMG meanwhile)

# 3. pre-flight: will it fit?
devpulse ai --before-load 20000 --auto-clean   # 20 GB ≈ 32B Q4_K_M

# 4. pull and run
ollama pull qwen2.5-coder:32b
ollama run qwen2.5-coder:32b

# 5. for hours-long sessions, babysit it
devpulse babysit --target-free-mb 4096 --json > session.log &

Cost vs cloud

What it actually costs to run.

The Mac Mini M4 Pro idles at 4–7 W and pulls 30–50 W under inference. At a typical US electricity rate of $0.16/kWh, an 8-hour-a-day mixed workload runs roughly $20–30/year — about one month of Claude Pro.

For the full break-even math (Mac purchase amortized vs API cost):

Local-vs-cloud cost calculator →

Common gotchas

What people get wrong.

Confusing the 24 GB and 64 GB configs

The Mac Mini M4 Pro spec page lists 24 / 48 / 64 GB. Buyers often misremember which they bought. devpulse status reads it from the kernel — the only ground truth.

“Model B = RAM GB” folk rule

Common shorthand on Reddit. It's almost right but ignores quantization and context. A 32B at Q4_K_M is ~20 GB; a 32B at F16 is ~64 GB. Always check the actual quant.

Buying 64 GB for 70B without trimming the stack

70B Q4_K_M = 42 GB. macOS + Chrome + Docker can easily eat 15-20 GB more. On a 64 GB machine the model OOMs. Either close everything, use Q3_K_M (~32 GB), or buy an M4 Max with 96 GB.

Skipping MLX for Ollama

Ollama uses GGUF; LM Studio supports MLX models built specifically for Apple Silicon. Same parameter count, 10-20% faster on Mac. Worth switching for long-running workloads.

The right model on the right Mac.

DevPulse tells you what fits — and what to free up if it doesn't.

Download for macOS

macOS 14+ · Apple Silicon & Intel · Free during launch

Best local AI to run on Mac Mini M4 Pro.