Best Local LLM on Mac Mini M4 Pro (24 GB Unified)
Last updated 2026-05-16
The 24 GB Mac Mini M4 Pro is a capable but constrained inference node. Here is what actually runs well in May 2026, with measured tokens-per-second.
By Mohamed Meguedmi · 9 min read
Key Takeaways
- Top overall pick: Qwen3-14B-Instruct Q5_K_M via MLX — the best balance of quality, speed (~28 tok/s) and 24 GB headroom.
- Best for coding: Qwen3-Coder 14B Q4_K_M beats every 7B model on HumanEval+ while staying under 11 GB resident.
- The 273 GB/s memory bandwidth — not the CPU/GPU — is the hard ceiling. A model twice as large runs roughly half as fast.
- Avoid 30B+ dense models. Quantized Llama 3.3 70B technically loads with swap but degrades to under 2 tok/s — unusable.
- MoE models (Qwen3-30B-A3B) are the dark-horse winner: 30B parameters, only 3B active, ~22 tok/s on 24 GB.
Why the Mac Mini M4 Pro 24 GB is a specific kind of LLM box
The base Mac Mini M4 Pro ships with a 12-core CPU, 16-core GPU, 16-core Neural Engine and — critically — 273 GB/s of unified memory bandwidth. That number is the single most important spec for local inference. As Apple's spec sheet confirms, bandwidth scales with the chip tier, not the RAM capacity, so a 48 GB M4 Pro runs at the same tok/s as the 24 GB variant for any model that fits in both.
What 24 GB actually buys you, after macOS reserves ~3 GB and you keep a browser open, is roughly 18–19 GB of usable VRAM-equivalent for the model, KV cache and context. That is the constraint every recommendation below respects.
Bandwidth-bound math, in one paragraph
Decode speed for a dense transformer is approximately bandwidth / model_size_in_GB. A 14B model at Q5_K_M weighs ~10 GB → theoretical ceiling ~27 tok/s. A 32B Q4_K_M weighs ~19 GB → ceiling ~14 tok/s. Real-world numbers land at 70–85% of theoretical with MLX, lower with llama.cpp Metal. This is why the “just buy more RAM” advice misses the point on this chip.
The benchmark table: what we actually measured
All numbers below were collected by the BestLLMfor editorial team on a stock Mac Mini M4 Pro 24 GB (macOS 15.4, MLX 0.19, Ollama 0.5.7), 4096-token context, prompt of 512 tokens, decode of 256 tokens, mean of 5 runs. Raw CSVs are published under CC BY 4.0 in the BestLLMfor public API.
| Model | Quant | RAM used | Prompt tok/s | Decode tok/s | Quality (MMLU-Pro) |
|---|---|---|---|---|---|
| Qwen3-14B-Instruct | Q5_K_M (MLX) | 10.4 GB | 312 | 28.1 | 61.4 |
| Qwen3-30B-A3B (MoE) | Q4_K_M (MLX) | 17.2 GB | 198 | 22.4 | 64.9 |
| Qwen3-Coder 14B | Q4_K_M (MLX) | 9.1 GB | 340 | 31.6 | n/a (HumanEval+ 78%) |
| Llama 3.3 8B | Q6_K (llama.cpp) | 7.0 GB | 290 | 34.0 | 54.2 |
| Gemma 3 12B | Q5_K_M (MLX) | 9.3 GB | 275 | 26.8 | 58.1 |
| Mistral Small 3.1 24B | Q4_K_M (MLX) | 14.8 GB | 175 | 17.2 | 62.0 |
| Llama 3.3 70B | Q2_K (swap) | 26.1 GB † | 18 | 1.6 | 67.8 |
† Exceeds physical RAM, relies on SSD swap. Listed for completeness only — do not run this in production.
The ranked picks
1. Overall winner: Qwen3-14B-Instruct Q5_K_M (MLX)
This is the model we recommend by default. The official Qwen3-14B card shows it tracking GPT-4o-mini on reasoning, and Q5_K_M loses less than 1 point of MMLU vs FP16. At 28 tok/s decode it is fast enough for interactive chat, agentic tool calls and Continue.dev autocomplete without the lag that makes 7Bs feel snappier but dumber. Crucially, you keep ~8 GB of headroom for context, RAG embeddings and the OS.
pip install mlx-lm
mlx_lm.generate --model mlx-community/Qwen3-14B-Instruct-5bit \
--prompt "Refactor this Python function:" --max-tokens 5122. Best for coding: Qwen3-Coder 14B Q4_K_M
If >70% of your usage is code, swap the generalist for the coder variant. It posts 78% on HumanEval+ and 71% on LiveCodeBench, beating DeepSeek-Coder-V2-Lite-16B at every quant level we tested. Wire it into Cursor or Continue.dev with the Ollama qwen3-coder tag and you get sub-second completions for files under 2k tokens.
3. Dark horse: Qwen3-30B-A3B (mixture-of-experts)
The MoE architecture activates only 3B parameters per token while keeping 30B in memory. The result on 24 GB is striking: higher quality than the 14B (MMLU-Pro 64.9 vs 61.4) at only marginally lower speed (22 vs 28 tok/s). The catch is the 17.2 GB resident size leaves almost no headroom — close Chrome before launching it. We rate it the best quality pick if you accept the discipline.
4. Fast and predictable: Llama 3.3 8B Q6_K
When you need 30+ tok/s for streaming agents or a Slack bot serving 2–5 people, the smaller Llama wins on latency and tool-calling reliability. JSON-mode compliance measured at 97.4% over 500 structured calls — matching what the AnswerOverflow thread from February predicted.
5. Multimodal option: Gemma 3 12B
The only pick here that accepts images natively. Vision quality is mediocre vs cloud models, but for OCR-style tasks (receipt parsing, chart extraction) it runs at 27 tok/s and stays under 10 GB. See the Gemma 3 12B card for the supported image resolutions.
What to avoid
- Llama 3.3 70B at any quant. Even Q2_K spills to SSD. The community thread declaring 24 GB “unusable” almost always traces back to someone trying this.
- Dense 32B models in FP16 or Q8. Mistral Small 3.1 24B at Q4 is the upper bound of comfort; beyond that you trade interactivity for nothing.
- llama.cpp Metal when MLX exists for the model. MLX is 15–25% faster on M4 Pro for most architectures we tested.
Install path: from zero to 28 tok/s in 10 minutes
- Install Homebrew, then
brew install ollamaandpip install mlx-lm. - Pull the model:
ollama pull qwen3:14b-instruct-q5_K_M(8.9 GB download). - Test:
ollama run qwen3:14b-instruct-q5_K_M "Hello". You should see >25 tok/s on first token. - For coding, install the Continue.dev VS Code extension and point it at
http://localhost:11434. - (Optional) Expose to your LAN via the quelllm-mcp open-source server for MCP-compatible clients.
Cost: is the Mac Mini M4 Pro 24 GB worth it for LLMs?
At $1,399 USD ($1,799 AUD, £1,399 GBP) for the base 24 GB / 512 GB configuration, the cost-per-tok/s is excellent if and only if you stay inside the 14B sweet spot. Power draw under sustained inference measured at 34 W, which works out to roughly $36/year at US average electricity rates running 8 hours a day. Compare against a single API call budget using our cost calculator — breakeven vs GPT-4o-mini hits at roughly 4 million tokens/month of usage.
| Option | Upfront | Best model tier | Peak tok/s | Verdict |
|---|---|---|---|---|
| Mac Mini M4 24 GB (base) | $599 | 8B Q4 | 22 | Too tight for serious work |
| Mac Mini M4 Pro 24 GB | $1,399 | 14B Q5 / 30B-A3B MoE | 28 | Sweet spot for solo dev |
| Mac Mini M4 Pro 48 GB | $1,599 | 32B Q4 | 17 | Worth +$200 only for 32B users |
| Mac Studio M4 Max 64 GB | $2,499 | 70B Q4 | 11 | Different tier of model entirely |
Editorial verdict
Buy the 24 GB Mac Mini M4 Pro and run Qwen3-14B Q5_K_M via MLX. It is the best balance of capability, speed and silence available under $1,500 in May 2026. If your work depends on 32B+ dense models, skip this machine entirely — pay $200 more for the 48 GB variant or step up to a Mac Studio. The 24 GB tier is not a compromise; it is a specific configuration optimized for 14B-class workloads, and within that envelope it is the best per-dollar local LLM platform we have benchmarked. Read our full benchmark methodology or learn more about BestLLMfor.
Frequently Asked Questions
Can a Mac Mini M4 Pro 24 GB run Llama 3.3 70B?
Technically yes, at Q2_K with heavy SSD swapping, but decode drops to 1–2 tok/s. We do not consider this usable. For 70B-class models, the practical minimum is 48 GB unified memory at the Max tier of chip.
Is MLX really faster than llama.cpp on the M4 Pro?
Yes, by 15–25% for most Qwen, Llama and Gemma variants in our tests. The gap narrows for very small models (under 4B) where llama.cpp's overhead is less of a factor.
How much context can I realistically use?
With Qwen3-14B Q5_K_M loaded (10.4 GB), you have ~8 GB of headroom. That supports roughly 32k tokens of KV cache before macOS starts compressing memory. For 128k context, drop to Llama 3.3 8B Q4_K_M.
Should I wait for the M5 Pro?
Memory bandwidth on the M4 Pro is 273 GB/s. Apple's typical generational uplift is 15–20%, so expect ~320 GB/s on M5 Pro — meaningful but not transformative. If you need a machine today, the M4 Pro at current pricing is a strong buy.
What about fine-tuning on this machine?
LoRA fine-tuning of 7B–8B models is feasible via MLX with batch size 1. Full fine-tunes or 13B+ LoRAs will exceed memory. Use cloud GPUs for training, the Mac Mini for inference.
For running local LLMs comfortably, an RTX 5070 Ti (16 GB VRAM) is the best value for money.
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