Best Local AI Models for an M2 Pro Mac With 32GB Memory
Calculated local AI model fits for an Apple M2 Pro Mac with 32GB unified memory.
Yes. A computer with 32GB RAM can run small local AI models. A safe first choice from the current MamiLens list is Qwen 2.5 14B. This is a memory-fit estimate, not a speed promise.
The device we checked
MamiLens keeps 6GB for the operating system and background work. That leaves a working model budget of about 26GB.
Best models to start with
Qwen 2.5 14B
Qwen 2.5 14B is a curated dense profile for general, writing, coding workloads.
- Calculated weights
- 9 GB
- Estimated total RAM
- 32 GB
- RAM left
- 0 GB
- Expected path
- Unified memory
- Speed
- 7 to 15 tokens/s estimate
- Evidence
- Calculated estimate
ollama run qwen2.5:14bOpen evidence-labelled model profile →Phi-4 14B
A compact Microsoft model aimed at strong reasoning, mathematics and instruction following.
- Calculated weights
- 9.1 GB
- Estimated total RAM
- 32 GB
- RAM left
- 0 GB
- Expected path
- Unified memory
- Speed
- 7 to 15 tokens/s estimate
- Evidence
- Calculated estimate
ollama run phi4:14bOpen evidence-labelled model profile →DeepSeek R1 14B
DeepSeek R1 14B is a curated dense profile for reasoning, math, coding workloads.
- Calculated weights
- 9 GB
- Estimated total RAM
- 32 GB
- RAM left
- 0 GB
- Expected path
- Unified memory
- Speed
- 7 to 15 tokens/s estimate
- Evidence
- Calculated estimate
ollama run deepseek-r1:14bOpen evidence-labelled model profile →Cogito v1 Preview 14B
A text assistant with an optional extended reasoning mode for writing, coding, mathematics and tools. Follow its published system instruction to enable reasoning.
- Calculated weights
- 9 GB
- Estimated total RAM
- 32 GB
- RAM left
- 0 GB
- Expected path
- Unified memory
- Speed
- 7 to 15 tokens/s estimate
- Evidence
- Calculated estimate
ollama run cogito:14bOpen evidence-labelled model profile →Gemma 4 12B QAT
Gemma 4 12B QAT is a curated dense profile for general, writing, reasoning workloads.
- Calculated weights
- 7.2 GB
- Estimated total RAM
- 32 GB
- RAM left
- 0 GB
- Expected path
- Unified memory
- Speed
- 9 to 19 tokens/s estimate
- Evidence
- Calculated estimate
ollama run gemma4:12b-it-qatOpen evidence-labelled model profile →Recommended inference engine
LM Studio or MLX LM
Both can use Apple unified memory. LM Studio is easier; MLX LM gives Apple-native control.
Unified memory helps model access, but chip generation still changes speed.How accurate is this result?
This page uses the same calculator rules as the live MamiLens tool. It includes model weights, runtime overhead, operating-system reserve, context reserve and simultaneous-user cache when selected. A displayed speed range is calculated from published memory bandwidth. It is not a benchmark from this exact computer.
Start with the first model. Test a normal task, record response speed and watch memory use before using it for important work.