Apple · M2 Max · Desktop
Best AI Models for Mac Studio M2 Max (32 GB)
Runs 27–35B models well; 70B doesn't fit in 28.5 GB.
| # | Model | Speed | Score |
|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB | 16.1 tok/s | |
| 2 | Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB | 16.1 tok/s | |
| 3 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB | 63.2 tok/s | |
| 4 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB | 71.2 tok/s | |
| 5 | Q4_K_M · 19.3 GB | 64.3 tok/s |
| # | Model | Speed | Score |
|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB | 16.1 tok/s | |
| 2 | Qwen3.6 35B A3BAlibaba Q4_K_M · 22.0 GB | 54.5 tok/s | |
| 3 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB | 63.2 tok/s | |
| 4 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB | 71.2 tok/s | |
| 5 | Q4_K_M · 19.3 GB | 64.3 tok/s |
| # | Model | Speed | Score |
|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB | 16.1 tok/s | |
| 2 | Qwen3.5 27BAlibaba Q4_K_M · 17.9 GB | 16.1 tok/s | |
| 3 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB | 63.2 tok/s | |
| 4 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB | 71.2 tok/s | |
| 5 | Q4_K_M · 19.3 GB | 64.3 tok/s |
| # | Model | Speed | Score |
|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB | 16.1 tok/s | |
| 2 | Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB | 16.1 tok/s | |
| 3 | Qwen3.6 35B A3BAlibaba Q4_K_M · 22.0 GB | 54.5 tok/s | |
| 4 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB | 63.2 tok/s | overall |
| 5 | Q4_K_M · 19.3 GB | 64.3 tok/s |
| # | Model | Speed ↓ | Score |
|---|---|---|---|
| 1 | GPT OSS 20BOpenAIHow to run → Q4_K_M · 13.1 GB | 71.2 tok/s | |
| 2 | Q4_K_M · 19.3 GB | 64.3 tok/s | |
| 3 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB | 63.2 tok/s | |
| 4 | Qwen3 30B A3B Thinking 2507Alibaba Q4_K_M · 19.4 GB | 59.0 tok/s | |
| 5 | Qwen3 30B A3B Instruct 2507Alibaba Q4_K_M · 19.4 GB | 59.0 tok/s |
The Mac Studio M2 Max is the fastest 32 GB machine: it runs 27–35B models well. macOS keeps about 3.5 GB, leaving 28.5 GB for models. With 21 TFLOPS it reads long prompts faster than most 32 GB machines: a 4K-token prompt takes 53 s. M2 Max generation, released 2023; the newer M3 Max writes -16% faster with 1.0× the compute.
Mac Studio M2 Max strengths and weaknesses
Strengths
- Runs 35B MoE models: Qwen3.6 35B A3B at 54.5 tok/s in 22 GB
- Room for long context: Qwen3.8 27B at Q4 uses 17.9 GB, leaving 10.6 GB free
Weaknesses
- 70B models don't fit: Llama 3.3 70B Instruct needs 51.3 GB at Q4 vs 28.5 GB usable here
- Slow prompt reading: A 32K-token prompt on Qwen3.8 27B waits 7.1 min for the first token
- No CUDA: vLLM, AWQ, GPTQ and FP8 builds don't run; use GGUF or MLX
Memory for models
28.5 GB of 32 GB70B models need 51.3 GB at Q4, more than the 28.5 GB available. The 64 GB config fits Gemma 4 31B IT →
Other memory sizes of the same machine first, then machines and GPUs people weigh against it, each with the one reason it is worth a look. Speeds on Qwen3.6 35B A3B.
Output speed depends on memory bandwidth (400 GB/s). Grey rows need more than the 28.5 GB available.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB44.0tok/s
- 14BQwen3 14B · Q4_K_M · 11.9 GB29.5tok/s
- 27–31BQwen3.8 27B · Q4_K_M · 17.9 GB16.1tok/s
- 35B MoEQwen3.6 35B A3B · Q4_K_M · 22.0 GB54.5tok/s
- 70BLlama 3.3 70B Instruct · does not fitneeds 51.3 GB
- 120B MoEGPT OSS 120B · does not fitneeds 70.8 GB
Estimated speeds: Q4_K_M, 4K context, one user. How we estimate
Max generations: writing vs compute
M1 Max → M5 MaxWriting speed follows memory bandwidth and barely moved: 54.5 to 58.8 tok/s since the M1 Max. Compute, which sets how fast prompts are read, grew 3.5×. Indexed to the M1 Max; speeds on Qwen3.6 35B A3B, same 32 GB.
All 1106 models on the Mac Studio M2 Max
Featured onlyBest quant that fits in 28.5 GB, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works
| Model | Quant | VRAM | Speed | Context | Score | Fit |
|---|---|---|---|---|---|---|
| BF16 | 20.2 GB | 15.8 t/s | — | B55, Decent | ||
| Q2_K | 25.6 GB | 36.2 t/s | 33K | B59, Decent | ||
Llama 3.1 70B InstructChatFunctions | IQ2_XS | 28 GB | 12.0 t/s | 131K | D23, Barely runs | |
Qwen 72BChat | IQ2_XXS | 26.6 GB | 12.8 t/s | 33K | C33, Usable | |
Qwen2.5 72BChatFunctions | IQ2_S | 28.4 GB | 11.8 t/s | 131K | D19, Barely runs | |
Qwen2 VL 72B InstructVision | IQ2_XS | 27.7 GB | 12.2 t/s | 33K | D24, Barely runs | |
| IQ2_S | 28.4 GB | 11.8 t/s | 33K | D19, Barely runs | ||
| IQ2_XS | 28 GB | 12.0 t/s | — | D23, Barely runs | ||
| IQ2_XS | 28 GB | 12.0 t/s | — | D23, Barely runs | ||
Hermes 2 Theta Llama 3 70BChatFunctions | IQ2_M | 26.8 GB | 11.3 t/s | 8K | D29, Barely runs | |
| IQ2_M | 26.8 GB | 11.3 t/s | 8K | D29, Barely runs |
Specs
- Chip
- M2 Max
- CPU cores
- 12
- GPU cores
- 30
- Unified memory
- 32 GB
- For models
- 28.5 GB (macOS keeps 3.5 GB)
- Memory bandwidth
- 400 GB/s
- FP16 (dense)
- 21.5 TFLOPS
- Neural Engine
- 16 TOPS
- Released
- 2023-06-13
Mac Studio M2 Max FAQ
How much memory is available for AI on the Mac Studio M2 Max (32 GB)?
About 28.5 GB of the 32 GB. macOS keeps roughly 3.5 GB for itself and other apps.
What is the largest model the Mac Studio M2 Max (32 GB) can run?
Qwen3.6 35B A3B (36.0B) at Q4_K_M uses 22.0 GB, 77% of the available memory, and runs at about 54.5 tok/s.
Can the Mac Studio M2 Max (32 GB) run Qwen3.8 27B?
Yes. Qwen3.8 27B fits at Q4_K_M, using 17.9 GB of the 28.5 GB available, and it is the best model that runs well on this machine.
Can the Mac Studio M2 Max (32 GB) run 70B models?
No. Llama 3.3 70B Instruct needs 51.3 GB at Q4, more than the 28.5 GB available.
How long does the Mac Studio M2 Max (32 GB) take to read a long prompt?
On Qwen3.8 27B, about 53 s for a 4K-token prompt and 7.1 min for a 32K-token document before the first word appears.
Is renting a GPU cheaper than buying the Mac Studio M2 Max?
An RTX A6000 (48 GB) rents from $0.33/hr.