Best Local LLM for a Mac M4 with 24GB
With 24 GB of unified memory, a Mac mini M4 Pro handles Gemma 3 27B at Q4 fully in memory, and its 273 GB/s bandwidth generates tokens noticeably faster than the base M4. Ornith 1.0 35B fits too at about 20 GB, though it leaves little headroom for macOS.
Model data last verified on June 27, 2026. All VRAM figures are for the Q4 quantization Ollama serves by default.
The hardware in question
Apple Mac mini M4 Pro (24 GB)
$1,599
M4 Pro chip, 14-core CPU, 20-core GPU, 24 GB unified memory, 273 GB/s bandwidth
Comfortable 34B Q4 inference and light 70B quantized experiments
Full entry in the hardware guideEvery model that fits 24 GB of unified memory
13 of the 16 models in our verified catalog fit this budget at Q4, sorted with the largest fit first. Click any model for its full spec sheet, hardware cross-reference and run commands.
| Model | Params | Quant | Min VRAM | Context | Fit |
|---|---|---|---|---|---|
| Ornith 1.0 35B#1 SWE-bench open | 35B | Q4 | 20 GB | 262K | Tight fit |
| Gemma 3 27B | 27B | Q4 | 15 GB | 128K | 9 GB free |
| OpenAI gpt-oss-20bHistoric first | 20B total / 3.6B active (MoE) | Q4 | 12 GB | 128K | 12 GB free |
| Gemma 4 12BBest for fine-tuning | 12B | Q4 | 8 GB | 256K | 16 GB free |
| Phi-4 14BBest small coder | 14B | Q4 | 8 GB | 16K | 16 GB free |
| Mistral Nemo 12B | 12B | Q4 | 7 GB | 128K | 17 GB free |
| Llama 3.1 8B | 8B | Q4 | 5 GB | 128K | 19 GB free |
| Qwen3 8BMost popular | 8B | Q4 | 5 GB | 128K | 19 GB free |
| DeepSeek R1 Distill 8BMost private | 8B | Q4 | 5 GB | 128K | 19 GB free |
| Mistral 7B v0.3 | 7B | Q4 | 4 GB | 32K | 20 GB free |
| Qwen 2.5 Coder 7B | 7B | Q4 | 4 GB | 128K | 20 GB free |
| Gemma 3 4B | 4B | Q4 | 3 GB | 128K | 21 GB free |
| Llama 3.2 3BFastest | 3B | Q4 | 2 GB | 128K | 22 GB free |
Unified memory is shared with macOS, so models within 4 GB of the ceiling are flagged as tight fits. Expect to close other apps or drop to a smaller quantization for those.
Run the top picks with Ollama
One command each. Ollama pulls the Q4_K_M build by default and exposes an OpenAI-compatible endpoint at localhost:11434/v1.
ollama run hf.co/deepreinforce-ai/Ornith-1.0-35B-GGUFollama run gemma3:27bollama run gpt-oss:20bFrequently asked questions
What is the best local LLM for a 24GB M4 Mac?
Gemma 3 27B for general use: it needs about 15 GB at Q4, runs fully in the 24 GB unified pool, and leaves room for macOS. For coding, Ornith 1.0 35B at about 20 GB is the top open-weight model on SWE-bench Verified, but keep other apps closed while it runs.
Can a 24GB Mac run a 70B model?
Not well. Llama 3.3 70B needs about 48 GB at Q4; squeezing a 70B into 24 GB means Q3 or lower, which hits memory limits and throttles speed. The 48 GB Mac mini M4 Pro is the realistic entry point, since it fits 70B at Q4 entirely in memory.
Mac M4 Pro 24GB or a 16GB GPU PC for local AI?
They cover a similar model range, up to the 27B class at Q4. The Mac wins on noise, power draw and setup simplicity, while a current 16 GB GDDR7 card generally generates tokens faster thanks to higher memory bandwidth.
Is 24GB enough, or should I get the 48GB configuration?
24 GB covers everything up to the 27B class comfortably. If you want Llama 3.3 70B, you need the 48 GB configuration (around $2,099), which fits it at Q4 with headroom. The memory is soldered, so choose at purchase time.
Different budget or use case?
The faceted model browser combines VRAM, family, license and task filters over the same verified catalog.