#1 · Good
Qwen 3 (32B)
Near GPT-4 intelligence locally. Thinking mode demolishes hard problems. The local AI dream.
chatcodereasoningpowerquality
Apple Silicon hardware guide
Best local LLMs for Mac mini M4 Pro 48GB
Mac mini M4 Pro 48GB with 48GB unified memory is a serious local LLM desktop machine. This page lists local AI models that fit its memory budget, with realistic performance expectations for LM Studio and similar runtimes.
Chip
M4 Pro
Unified memory
48GB
Compatible models
143
Best pick
Qwen 3 (32B)
Quick answer
For Mac mini M4 Pro 48GB, start with Qwen 3 (32B). Models marked “Comfortable” leave useful memory headroom; “Tight but possible” can work, but you should close other apps and prefer lower quantization.
Top compatible local LLMs
#2 · Tight but possible
Kimi Linear 48B-A3B Instruct
Moonshot AI efficient Kimi model with linear-attention style architecture and 3B active parameters. Strong long-context, reasoning and coding signal. MIT licensed.
chatcodereasoningpowermoe
#3 · Comfortable
GLM 4.5 Air (MoE)
Zhipu AI's efficient MoE powerhouse. 106B total parameters, only 14B active at inference — dense-model speed with much larger model quality. Clearly the best in the 16–24GB RAM range. Outperforms Llama 3.3 70B. Apache 2.0.
chatcodepowerqualitygeneral
#4 · Good
Qwen 3.6 (27B)
Qwen 3.6 flagship dense model. Hybrid thinking mode with /think toggle for deep chain-of-thought reasoning. 128K context, 29+ languages. Significantly outperforms Qwen3.5-27B on reasoning, coding & math. Apache 2.0.
chatcodereasoningpowerquality
#5 · Comfortable
Qwen 3 Coder (30B)
Qwen flagship coding model. Designed for agentic coding with 256K context. Outperforms Claude 3.5 Sonnet on SWE-bench. Apache 2.0.
codepowerquality
#6 · Good
Kimi K2.5 (32B/1T MoE)
Moonshot AI's agentic flagship. 1T total MoE parameters with 32B active per forward pass. Unmatched long-context reasoning at 256K tokens. Designed for complex agentic tasks and tool use. Model License — check moonshotai.com for commercial terms.
chatcodereasoningpowerquality
#7 · Comfortable
Gemma 4 26B A4B
Gemma 4 MoE flagship-for-workstations: 26B total with ~4B active parameters. 256K context and excellent quality-per-watt for local inference. Apache 2.0.
chatcodereasoningpowermultimodal
#8 · Good
Gemma 4 31B
Largest Gemma 4 model for premium local quality. Strong coding and reasoning with 256K context and broad multilingual support. Apache 2.0.
chatcodereasoningqualitymultimodal
#9 · Tight but possible
Llama-3.3-Nemotron-Super (49B)
NVIDIA's super-efficient 49B distilled from DeepSeek-R1 + Llama. Outperforms Llama-3.3-70B at half the compute. Strong reasoning, coding & instruction following. Runs on Mac Studio 64GB. NVIDIA Open Model License.
chatreasoningcodepowerquality
#10 · Comfortable
Qwen 3.5 MoE (35B/3B active)
MoE gem — only 3B params active at inference. 19x faster than Qwen3-Max at 256K context. Best quality-per-watt of the series. Hybrid thinking mode. Runs on Mac Studio 32GB. Agentic coding standout.
chatcodereasoningpowerspeed
#11 · Good
Qwen 3.5 (27B)
Dense 27B powerhouse. Hybrid thinking/non-thinking mode. Strong multilingual (29+ languages). 256K context window. Excellent instruction-following and math. Apache 2.0.
chatcodereasoningpowergeneral
#12 · Tight but possible
MiroThinker v1.5 (30B MoE)
⚠️ Despite the small active count, this is a full 30B MoE model (Qwen3-30B-A3B base). ~82 GB full weights (Q4_K_M ≈18 GB). Deep-research agent with 256K context, tool calls, multilingual (EN/ZH). Requires H100 80 GB or serious multi-GPU. Not suitable for M1/M2 or consumer GPUs. Apache 2.0.
reasoningcodepowerquality
Buying note
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