Best AI Models for Mac Mini M5 Pro (64 GB)
64 GB unified − 3.5 GB OS overhead = 60.5 GB available for AI models
How does this device compare with your machine?
99 of 338 featured models run great on Mac Mini M5 Pro (64 GB) · best pick: Qwen3.8 27B at ~12.3 tok/s
largest that fits: 116.8B (GPT OSS 120B, Q4_0) · 235B needs CPU offload or more memory
What LLMs Can Mac Mini M5 Pro (64 GB) Run?
1168 models · 471 run great · 416 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 Mini M5 Pro (64 GB)
| Model | Quant | VRAM | Speed | Context | Score | Fit |
|---|---|---|---|---|---|---|
Qwen3.5 9B AbliteratedChatFunctions | Q4_K_M | 6.2 GB | 36.2 t/s | 262K | A73, Runs well | |
Deepseek Vl2 TinyVision | BF16 | 9.4 GB | 46.2 t/s | — | A78, Runs well | |
Turkish Gemma 4B T1 ScoutChatFunctionsReasoning | Q4_K_M | 3.6 GB | 66.3 t/s | 131K | S85, Runs great | |
LFM2 VL 1.6BVision | Q4_K_M | 1.5 GB | 162.8 t/s | 128K | S97, Runs great | |
| Q4_K_M | 1.7 GB | 167.9 t/s | 33K | S98, Runs great | ||
| Q4_K_M | 7.6 GB | 38.9 t/s | 41K | A75, Runs well | ||
| Q4_K_M | 7.3 GB | 39.8 t/s | 66K | A75, Runs well | ||
Vaultgemma 1BChat | BF16 | 3.7 GB | 93.8 t/s | — | S90, Runs great | |
Qwen3 14B PAROChat | Q4_K_M | 3.9 GB | 136.9 t/s | 41K | S96, Runs great | |
Granite 4.0 1BChatFunctions | Q4_K_M | 2.6 GB | 148.2 t/s | 131K | S97, Runs great | |
| Q4_K_M | 1.4 GB | 298.5 t/s | 262K | S100, Runs great | ||
Huihui GPT OSS 20B BF16 AbliteratedChatFunctions | Q4_K_M | 13.1 GB | 59.3 t/s | 131K | A84, Runs well | |
| Q4_K_M | 7.9 GB | 40.6 t/s | — | A75, Runs well | ||
| Q4_K_M | 8.6 GB | 36.2 t/s | — | A73, Runs well | ||
Qwen3 8BChat | Q4_K_M | 7.8 GB | 40.7 t/s | — | A75, Runs well | |
| Q4_K_M | 5.0 GB | 137.6 t/s | 4K | S96, Runs great |
Mac Mini M5 Pro (64 GB) has 60.5 GB of memory for models. In the ranking below, Qwen3.8 27B leads at Q4_K_M: it needs 17.9 GB (30% of that memory) and generates about 12.3 tokens per second. 99 of the 338 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 (307 GB/s), so smaller and mixture-of-experts models are noticeably quicker than dense models of the same quality. 235B-class models need about 150 GB at Q4_K_M, more than the 60.5 GB available, so they require offloading layers to system RAM or a larger-memory setup.
Runs Well
- Qwen3.6 35B A3B (36.0B at Q4_K_M, ~46.5 tok/s)
- Gemma 4 26B A4B IT (25.8B at Q4_K_M, ~53.2 tok/s)
- NVIDIA Nemotron 3.5 Lightning 30B A3B BF16 (31.6B at Q4_K_M, ~58.4 tok/s)
- GLM 4.7 Flash (31.2B at Q4_K_M, ~43.5 tok/s)
- GPT OSS 20B (20.9B at Q4_K_M, ~59.3 tok/s)
Challenging
- Qwen3.8 27B (27.8B at Q4_K_M, ~12.3 tok/s) fits but uses 30% of memory, so keep context short
- Muse Glimmer 30B (29.8B at Q4_K_M, ~11.7 tok/s) fits but uses 32% of memory, so keep context short
- Gemma 4 31B IT (31.3B at Q4_K_M, ~10.5 tok/s) fits but uses 36% of memory, so keep context short
- 235B-class models need ~150 GB at Q4_K_M, so they need CPU offload or more memory
- 405B-class models need ~257 GB at Q4_K_M, so they need CPU offload or more memory
Mac Mini M5 Pro (64 GB) Specifications
- Brand
- Apple
- Chip
- M5 Pro
- Type
- Mini PC
- Unified Memory
- 64 GB
- Memory Bandwidth
- 307.0 GB/s
- GPU Cores
- 20
- CPU Cores
- 18
- Neural Engine
- 38.0 TOPS
- Release Date
- 2026-09-22
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
4.4 s
512 tok prompt
Long chat
35.2 s
4,096 tok prompt
Document / codebase
282 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 Mini M5 Pro (64 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 12.3 tokens per second
- 17.9 GB needed at Q4_K_M
Highest coding llmrun Score among official models that fit Mac Mini M5 Pro (64 GB).
Same 64 GB, faster
Mac Studio M2 Max (64 GB)
Apple
- same memory
- 30% higher memory bandwidth
Higher memory bandwidth means faster token generation.
Bigger models: step up to 96 GB
Mac Studio (M3 Ultra, 96GB)
Apple
- 32 GB more memory
- 167% higher memory bandwidth
Unlocks 3 more official models, incl. Ling 3.0 Flash (127.5B at IQ4_XS).
Frequently Asked Questions
Can Mac Mini M5 Pro (64 GB) run Qwen3.8 27B?
Yes. The Mac Mini M5 Pro (64 GB) with 64 GB unified memory can run Qwen3.8 27B (Q4_K_M, 17.9 GB, 30% of memory). It is also the table's best pick, at ~12.3 tok/s. 99 of the 338 featured models get a Fit of S (runs great) here. The largest featured model that fits at Q4 or better is GPT OSS 120B (116.8B at Q4_0). 235B-class models need around 150 GB at Q4_K_M, meaning they need CPU offloading or extra memory beyond what's available in 60.5 GB. Check the compatibility table above for the full list with Fit, VRAM usage and estimated speed. Models are checked against the 60.5 GB available to AI (that is, the 64 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 Mini M5 Pro (64 GB)?
The Mac Mini M5 Pro (64 GB) has 64 GB unified memory. After macOS reserves ~3.5 GB for the operating system, approximately 60.5 GB is available for AI models. Model rankings here use that 60.5 GB limit instead of the total 64 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 Mini M5 Pro (64 GB) good for AI?
With 64 GB unified memory and 307.0 GB/s bandwidth, the Mac Mini M5 Pro (64 GB) is excellent for running local AI models. Models that run well include Qwen3.6 35B A3B (36.0B at Q4_K_M, ~46.5 tok/s); Gemma 4 26B A4B IT (25.8B at Q4_K_M, ~53.2 tok/s); NVIDIA Nemotron 3.5 Lightning 30B A3B BF16 (31.6B at Q4_K_M, ~58.4 tok/s). This is a premium configuration, with room for large models at good quality and most smaller ones at maximum quality.
What's the best model for Mac Mini M5 Pro (64 GB)?
For the Mac Mini M5 Pro (64 GB), the best pick right now is Qwen3.8 27B at ~12.3 tok/s. Also running well: Qwen3.6 35B A3B (36.0B at Q4_K_M, ~46.5 tok/s); Gemma 4 26B A4B IT (25.8B at Q4_K_M, ~53.2 tok/s); NVIDIA Nemotron 3.5 Lightning 30B A3B BF16 (31.6B at Q4_K_M, ~58.4 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 Mini M5 Pro (64 GB) for AI inference?
With 307.0 GB/s memory bandwidth, the Mac Mini M5 Pro (64 GB) achieves approximately 48 tok/s on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~24 tok/s. In the ranking, the top pick Qwen3.8 27B runs at ~12.3 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 = (307 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 Mini M5 Pro (64 GB)
Real-world results typically within ±20%.
Can I run AI offline on Mac Mini M5 Pro (64 GB)?
Yes — once you download a model, it runs entirely on the Mac Mini M5 Pro (64 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.