Best AI Models for Mac Studio M2 Max (32 GB)
32 GB unified − 3.5 GB OS overhead = 28.5 GB available for AI models
How does this device compare with your machine?
116 of 311 featured models run great on Mac Studio M2 Max (32 GB) · best pick: Qwen3.8 27B at ~16.1 tok/s
largest that fits: 46.7B (Mixtral 8x7B Instruct v0.1, IQ4_XS) · 70B needs Q2 or offload
What LLMs Can Mac Studio M2 Max (32 GB) Run?
1106 models · 517 run great · 392 run well
Score: model quality, 100 = average model. Fit: how well it runs on this hardware, S best to F won't fit. Ordered by quality, recency and Fit. How ranking works
Showing compatibility for Mac Studio M2 Max (32 GB)
| Model | Quant | VRAM | Speed | Context | Score | Fit |
|---|---|---|---|---|---|---|
VibeThinker 1.5BChatMathCode | Q4_K_M | 1.8 GB | 197.2 t/s | 131K | S100, Runs great | |
Dhara 70MChatCode | BF16 | 0.5 GB | 571.4 t/s | 1K | S100, Runs great | |
Qwen3.5 9B Claude 4.6 Opus Reasoning DistilledChatReasoning | Q4_K_M | 6.6 GB | 44.0 t/s | 262K | A77, Runs well | |
| Q4_K_M | 1.6 GB | 359.0 t/s | 41K | S100, Runs great | ||
AriaVision | Q4_K_M | 20.2 GB | 62.8 t/s | 66K | A84, Runs well | |
| Q4_K_M | 7.3 GB | 51.9 t/s | 66K | A81, Runs well | ||
Qwen3 Tiny RandomChatFunctions | Q4_K_M | 0.3 GB | 903.2 t/s | 41K | S100, Runs great | |
JiRackUltra 32BChatFunctions | Q4_K_M | 24.3 GB | 13.7 t/s | 131K | B46, Decent | |
Sweep Next Edit v2 7BChatCode | Q4_K_M | 5.8 GB | 56.1 t/s | 33K | A82, Runs well | |
| Q4_K_M | 7.9 GB | 52.9 t/s | — | A81, Runs well | ||
Qwen3 8BChat | Q4_K_M | 7.8 GB | 53.0 t/s | — | A81, Runs well | |
| Q4_K_M | 24.2 GB | 13.7 t/s | — | B46, Decent | ||
| BF16 | 3.4 GB | 140.7 t/s | — | S96, Runs great | ||
| Q4_K_M | 1.7 GB | 191.8 t/s | 4K | S99, Runs great | ||
HunyuanOCRVision | Q4_K_M | 1.8 GB | 261.7 t/s | 131K | S100, Runs great | |
| Q4_K_M | 7.6 GB | 50.7 t/s | 41K | A80, Runs well |
Mac Studio M2 Max (32 GB) has 28.5 GB of memory for models. In the ranking below, Qwen3.8 27B leads at Q4_K_M: it needs 17.9 GB (63% of that memory) and generates about 16.1 tokens per second. 116 of the 311 featured models earn a Fit S here, meaning they load with room for context and run fast. Decode speed is set mostly by memory bandwidth (400 GB/s), so smaller and mixture-of-experts models are noticeably quicker than dense models of the same quality. 70B-class models need about 46 GB at Q4_K_M, more than the 28.5 GB available; they fit only at Q2 with a quality loss, or by offloading layers to system RAM.
Runs Well
- Qwen3.6 35B A3B (36.0B at Q4_K_M, ~54.5 tok/s)
- Gemma 4 26B A4B IT (25.8B at Q4_K_M, ~63.2 tok/s)
- NVIDIA Nemotron 3.5 Lightning 30B A3B BF16 (31.6B at Q4_K_M, ~64.3 tok/s)
- GLM 4.7 Flash (31.2B at Q4_K_M, ~50.4 tok/s)
- GPT OSS 20B (20.9B at Q4_K_M, ~71.2 tok/s)
Challenging
- Qwen3.8 27B (27.8B at Q4_K_M, ~16.1 tok/s) fits but uses 63% of memory, so keep context short
- Muse Glimmer 30B (29.8B at Q4_K_M, ~15.3 tok/s) fits but uses 69% of memory, so keep context short
- Gemma 4 31B IT (31.3B at Q4_K_M, ~13.7 tok/s) fits but uses 76% of memory, so keep context short
- 70B-class models need ~46 GB at Q4_K_M, so only Q2 fits
- 120B-class models need ~77 GB at Q4_K_M, so they need CPU offload or more memory
Mac Studio M2 Max (32 GB) Specifications
- Brand
- Apple
- Chip
- M2 Max
- Type
- Desktop
- Unified Memory
- 32 GB
- Memory Bandwidth
- 400.0 GB/s
- GPU Cores
- 30
- CPU Cores
- 12
- Neural Engine
- 15.8 TOPS
- Release Date
- 2023-06-13
Get Started
Prompt Processing
Estimated for Qwen3.8 27B, the compute-bound phase that reads your prompt before the first reply token appears.
Short chat
6.6 s
512 tok prompt
Long chat
53.0 s
4,096 tok prompt
Document / codebase
424 s
32,768 tok prompt
Prefill is compute-bound and a different number from the decode tok/s shown elsewhere on this page — how prompt processing works →
Performance figures are estimates calibrated as of 2026-09-21 — see calibration basis →
What to do next with Mac Studio M2 Max (32 GB)
Which model to run first, a faster option with the same memory, and what more memory unlocks. Computed from the rankings and specs above.
Best coding model here
Qwen3.8 27B
27.8B at Q4_K_M
- Coding llmrun Score 112.1
- about 16.1 tokens per second
- 17.9 GB needed at Q4_K_M
Highest coding llmrun Score among official models that fit Mac Studio M2 Max (32 GB).
Bigger models: step up to 48 GB
MacBook Pro 16" M4 Max (48 GB)
Apple
- 16 GB more memory
- 37% higher memory bandwidth
Unlocks 16 more official models, incl. Qwen3 Next 80B A3B Instruct (81.3B at IQ4_XS).
Frequently Asked Questions
Can Mac Studio M2 Max (32 GB) run Qwen3.8 27B?
Yes. The Mac Studio M2 Max (32 GB) with 32 GB unified memory can run Qwen3.8 27B (Q4_K_M, 17.9 GB, 63% of memory). It is also the table's best pick, at ~16.1 tok/s. 116 of the 311 featured models get a Fit of S (runs great) here. The largest featured model that fits at Q4 or better is Mixtral 8x7B Instruct v0.1 (46.7B at IQ4_XS). 70B-class models need about 46 GB at Q4_K_M, so only Q2 (with a quality loss) or CPU offload gets them running in 28.5 GB. Check the compatibility table above for the full list with Fit, VRAM usage and estimated speed. Models are checked against the 28.5 GB available to AI (that is, the 32 GB of RAM minus roughly 3.5 GB for macOS), while Apple Silicon's unified memory allows the GPU to draw from that same pool without copying data.
How much memory is available for AI on Mac Studio M2 Max (32 GB)?
The Mac Studio M2 Max (32 GB) has 32 GB unified memory. After macOS reserves ~3.5 GB for the operating system, approximately 28.5 GB is available for AI models. Model rankings here use that 28.5 GB limit instead of the total 32 GB. Unlike discrete GPUs where VRAM is separate from system RAM, a unified-memory device shares one memory pool between the CPU and GPU — this means no data copying overhead, but you share memory with macOS and open apps.
Is Mac Studio M2 Max (32 GB) good for AI?
With 32 GB unified memory and 400.0 GB/s bandwidth, the Mac Studio M2 Max (32 GB) is very good for running local AI models. Models that run well include Qwen3.6 35B A3B (36.0B at Q4_K_M, ~54.5 tok/s); Gemma 4 26B A4B IT (25.8B at Q4_K_M, ~63.2 tok/s); NVIDIA Nemotron 3.5 Lightning 30B A3B BF16 (31.6B at Q4_K_M, ~64.3 tok/s). This is a strong configuration for AI: mid-size models run at high quality with room for context.
What's the best model for Mac Studio M2 Max (32 GB)?
For the Mac Studio M2 Max (32 GB), the best pick right now is Qwen3.8 27B at ~16.1 tok/s. Also running well: Qwen3.6 35B A3B (36.0B at Q4_K_M, ~54.5 tok/s); Gemma 4 26B A4B IT (25.8B at Q4_K_M, ~63.2 tok/s); NVIDIA Nemotron 3.5 Lightning 30B A3B BF16 (31.6B at Q4_K_M, ~64.3 tok/s). The best choice depends on your use case: coding assistants benefit from code-tuned models, general chat from instruction-tuned ones. The list is ranked by quality, recency and Fit.
How fast is Mac Studio M2 Max (32 GB) for AI inference?
With 400.0 GB/s memory bandwidth, the Mac Studio M2 Max (32 GB) achieves approximately 62 tok/s on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~31 tok/s. In the ranking, the top pick Qwen3.8 27B runs at ~16.1 tok/s. Apple Silicon achieves high efficiency (~70%) thanks to unified memory — there's no PCIe bottleneck between CPU and GPU.
Tokens per second (estimate): Our tok/s is the decode speed for one user, estimated from memory bandwidth divided by the model size, times an efficiency factor for the platform. Real results typically land within ±20%. It does not cover prompt processing, which is measured separately as time to first token. Read more = (400 GB/s ÷ model GB) × efficiency
Apple Silicon achieves ~70% bandwidth efficiency thanks to Unified memory: Apple Silicon Macs pool their memory between CPU and GPU. We subtract the 3.5 GB reserved for macOS from the total RAM to find what's available for a model; thus, a 32 GB Mac counts as having 28.5 GB usable. Other unified-memory devices expose a lower fixed limit instead, which we apply. Read more and Metal acceleration.
Estimated speed on Mac Studio M2 Max (32 GB)
Real-world results typically within ±20%.
Can I run AI offline on Mac Studio M2 Max (32 GB)?
Yes — once you download a model, it runs entirely on the Mac Studio M2 Max (32 GB) without internet. Applications like Ollama and LM Studio make it straightforward to download, manage, and run models locally. All your conversations stay private on your device with zero data sent to external servers. This is one of the key advantages of local AI: complete privacy, no API costs, and no rate limits.