Device profile · macOS
Best local LLMs for Apple M4 Max (128GB)
Apple M4 Max (128GB) has ~96 GB usable for model weights and runs 64 of 67 popular models. Best tool: LM Studio.
- Usable memory
- ~96 GB
- Models run
- 64
- Too large
- 3
- Top pick
- 117B
Runs at Q4_K_M using ~62.4 GB of ~96 GB usable.
Runs on Apple M4 Max (128GB)
- Yesgpt-oss 120B117B MoE · ~62.4 GB at Q4_K_M
- YesLlama 4 Scout109B MoE · ~64.2 GB at Q4_K_M
- S YesSarvam-105B105B MoE · ~67.5 GB at Q4_K_M
- YesQwen2.5 72B72B · ~50.2 GB at Q4_K_M
- YesLlama 3.3 70B70B · ~45.3 GB at Q4_K_M
- YesMixtral 8x7B46.7B MoE · ~28.9 GB at Q4_K_M
- CR YesCommand R 35B35B · ~22.3 GB at Q4_K_M
- Yi YesYi 1.5 34B34B · ~21.4 GB at Q4_K_M
- YesQwen2.5 32B32B · ~22.1 GB at Q4_K_M
- YesQwen3 32B32B · ~22 GB at Q4_K_M
- YesDeepSeek-R1-Distill-Qwen 32B32B · ~22.1 GB at Q4_K_M
- YesQwen2.5 Coder 32B32B · ~20.7 GB at Q4_K_M
- YesGranite 4.0 H Small32B MoE · ~20.4 GB at Q4_K_M
- YesQwen3 30B-A3B30.5B MoE · ~20.7 GB at Q4_K_M
- S YesSarvam-30B30B MoE · ~21.7 GB at Q4_K_M
- YesGemma 2 27B27B · ~18.7 GB at Q4_K_M
- YesGemma 3 27B27B · ~18.6 GB at Q4_K_M
- YesMistral Small 3 24B24B · ~16.3 GB at Q4_K_M
- S YesSarvam-M 24B24B · ~16.3 GB at Q4_K_M
- Yesgpt-oss 20B21B MoE · ~13.2 GB at Q4_K_M
- YesDeepSeek-V2-Lite16B MoE · ~12.2 GB at Q4_K_M
- YesPhi-4 14B14B · ~10.8 GB at Q4_K_M
- YesQwen2.5 14B14B · ~10.7 GB at Q4_K_M
- YesQwen3 14B14B · ~10.7 GB at Q4_K_M
- YesDeepSeek-R1-Distill-Qwen 14B14B · ~10.7 GB at Q4_K_M
- YesQwen2.5 Coder 14B14B · ~10.1 GB at Q4_K_M
- YesMistral Nemo 12B12.2B · ~8.6 GB at Q4_K_M
- YesGemma 3 12B12B · ~8.9 GB at Q4_K_M
- YesLlama 3.2 Vision 11B10.7B · ~9 GB at Q4_K_M
- FN YesFalcon3 10B10B · ~7.5 GB at Q4_K_M
- YesGemma 2 9B9B · ~7.3 GB at Q4_K_M
- GL YesGLM-4 9B9B · ~7.3 GB at Q4_K_M
- YesQwen2.5-VL 7B8.29B · ~7.1 GB at Q4_K_M
- YesLlama 3.1 8B8B · ~6.4 GB at Q4_K_M
- YesQwen3 8B8B · ~6.5 GB at Q4_K_M
- YesDeepSeek-R1-Distill-Llama 8B8B · ~6.4 GB at Q4_K_M
- YesMistral 7B7B · ~5.8 GB at Q4_K_M
- YesQwen2.5 7B7B · ~6.1 GB at Q4_K_M
- YesDeepSeek-R1-Distill-Qwen 7B7B · ~6.1 GB at Q4_K_M
- YesQwen2.5 Coder 7B7B · ~5.8 GB at Q4_K_M
- YesGemma 3 4B4B · ~3.8 GB at Q4_K_M
- YesQwen3 4B4B · ~3.8 GB at Q4_K_M
- YesPhi-3.5-mini 3.8B3.82B · ~3.7 GB at Q4_K_M
- YesPhi-4-mini 3.8B3.8B · ~3.8 GB at Q4_K_M
- YesQwen2.5-VL 3B3.75B · ~4.4 GB at Q4_K_M
- YesQwen2.5 3B3.09B · ~3.3 GB at Q4_K_M
- YesQwen2.5 Coder 3B3.09B · ~3 GB at Q4_K_M
- YesLlama 3.2 3B3B · ~3.2 GB at Q4_K_M
- YesSmolLM3 3B3B · ~3 GB at Q4_K_M
- YesGemma 2 2B2.61B · ~2.9 GB at Q4_K_M
- YesGranite 3.1 2B2.53B · ~2.8 GB at Q4_K_M
- S YesSarvam-1 2B2B · ~2.7 GB at Q4_K_M
- YesSmolLM2 1.7B1.7B · ~2.2 GB at Q4_K_M
- YesQwen3 1.7B1.7B · ~2.4 GB at Q4_K_M
- YesQwen2.5 1.5B1.54B · ~2.2 GB at Q4_K_M
- YesQwen2.5 Coder 1.5B1.54B · ~2 GB at Q4_K_M
- TL YesTinyLlama 1.1B1.1B · ~1.8 GB at Q4_K_M
- YesLlama 3.2 1B1B · ~1.8 GB at Q4_K_M
- YesGemma 3 1B1B · ~1.8 GB at Q4_K_M
- YesQwen3 0.6B0.6B · ~1.5 GB at Q4_K_M
- YesQwen2.5 0.5B0.494B · ~1.5 GB at Q4_K_M
- YesQwen2.5 Coder 0.5B0.494B · ~1.4 GB at Q4_K_M
- YesSmolLM2 360M0.362B · ~1.2 GB at Q4_K_M
- YesSmolLM2 135M0.135B · ~1 GB at Q4_K_M
Too large for this device
Best way to run models on macOS
Beginner: LM Studio, Polished GUI, ships MLX on Apple Silicon, one-click model downloads.
Power user: mlx-lm, Apple's MLX framework, usually the fastest on Apple Silicon for the same quant.
vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
Full macOS tool guide →FAQ
What is the best local LLM for Apple M4 Max (128GB)?
gpt-oss 120B is the strongest model that runs comfortably, using ~62.4 GB at Q4_K_M of the ~96 GB usable on Apple M4 Max (128GB).
How much of Apple M4 Max (128GB)'s memory can I use for a model?
About 96 GB. Apple Silicon shares one unified memory pool; roughly 66-75% is available to the GPU for model weights, the rest is reserved for macOS.
Which tool should I use on macOS?
LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.) or mlx-lm for speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
Sources
Memory figures are estimates. See methodology.