Best AI Models for Mac Studio M1 Ultra (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?
225 of 338 featured models run great on Mac Studio M1 Ultra (64 GB) · best pick: Qwen3.8 27B at ~32.1 tok/s
largest that fits: 116.8B (GPT OSS 120B, Q4_0) · 235B needs CPU offload or more memory
What LLMs Can Mac Studio M1 Ultra (64 GB) Run?
1168 models · 872 run great · 214 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 M1 Ultra (64 GB)
| Model | Quant | VRAM | Speed | Context | Score | Fit |
|---|---|---|---|---|---|---|
| Q4_K_M | 9.0 GB | 68.0 t/s | 131K | S85, Runs great | ||
Llm Jp 4.1 33B ThinkingChatFunctions | Q4_K_M | 24.5 GB | 27.0 t/s | 66K | A68, Runs well | |
InternVL3 1BVision | Q4_K_M | 2.0 GB | 903.2 t/s | — | S100, Runs great | |
Fanar 2 27B InstructChatFunctions | Q4_K_M | 19.3 GB | 31.3 t/s | 131K | A71, Runs well | |
Kimi Linear Tiny RandomChatFunctions | BF16 | 0.3 GB | 676.1 t/s | — | S100, Runs great | |
| Q4_K_M | 17.5 GB | 39.1 t/s | 33K | A75, Runs well | ||
LocoOperator 4BChatCodeFunctions | Q4_K_M | 4.2 GB | 193.1 t/s | 262K | S99, Runs great | |
Hy MT2 7BTranslation | Q4_K_M | 7.3 GB | 103.9 t/s | 262K | S92, Runs great | |
NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16ChatFunctions | Q4_K_M | 54.5 GB | 49.5 t/s | 262K | A65, Runs well | |
| Q4_K_M | 9.0 GB | 68.0 t/s | 131K | S85, Runs great | ||
OneRec 1.7BChatFunctions | Q4_K_M | 3.5 GB | 309.4 t/s | 41K | S100, Runs great | |
| Q4_K_M | 4.8 GB | 212.1 t/s | — | S100, Runs great | ||
Emo 1b14b 1TChat | Q4_K_M | 9.0 GB | 203.8 t/s | 4K | S100, Runs great | |
The GuageLLM 23MChat | Q4_K_M | 0.0 GB | 28000.0 t/s | 0K | S100, Runs great | |
Ornith 1.0 35BChatFunctions | Q4_K_M | 21.5 GB | 81.6 t/s | 262K | S88, Runs great | |
Gemma 3 4B T1 ITChat | Q4_K_M | 3.4 GB | 187.9 t/s | 131K | S99, Runs great |
Mac Studio M1 Ultra (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 32.1 tokens per second. 225 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 (800 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
- Qwen3.8 27B (27.8B at Q4_K_M, ~32.1 tok/s)
- Gemma 4 26B A4B IT (25.8B at Q4_K_M, ~91.7 tok/s)
- Muse Glimmer 30B (29.8B at Q4_K_M, ~30.5 tok/s)
- Gemma 4 31B IT (31.3B at Q4_K_M, ~27.5 tok/s)
- NVIDIA Nemotron 3.5 Lightning 30B A3B BF16 (31.6B at Q4_K_M, ~77 tok/s)
Challenging
- GPT OSS 120B (116.8B at Q4_0, ~80.8 tok/s) fits but uses 98% of memory, so keep context short
- NVIDIA Nemotron 3 Super 120B A12B BF16 (123.6B at IQ3_M, ~35.5 tok/s) fits but uses 93% of memory, so keep context short
- Ling 3.0 Flash (127.5B at IQ3_M, ~76.9 tok/s) fits but uses 97% 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 Studio M1 Ultra (64 GB) Specifications
- Brand
- Apple
- Chip
- M1 Ultra
- Type
- Desktop
- Unified Memory
- 64 GB
- Memory Bandwidth
- 800.0 GB/s
- GPU Cores
- 48
- CPU Cores
- 20
- Neural Engine
- 22.0 TOPS
- Release Date
- 2022-03-18
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
4.6 s
512 tok prompt
Long chat
36.5 s
4,096 tok prompt
Document / codebase
292 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 M1 Ultra (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 32.1 tokens per second
- 17.9 GB needed at Q4_K_M
Highest coding llmrun Score among official models that fit Mac Studio M1 Ultra (64 GB).
Bigger models: step up to 96 GB
Mac Studio (M3 Ultra, 96GB)
Apple
- 32 GB more memory
Unlocks 3 more official models, incl. Ling 3.0 Flash (127.5B at IQ4_XS).
Frequently Asked Questions
Can Mac Studio M1 Ultra (64 GB) run Qwen3.8 27B?
Yes. The Mac Studio M1 Ultra (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 ~32.1 tok/s. 225 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 Studio M1 Ultra (64 GB)?
The Mac Studio M1 Ultra (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 Studio M1 Ultra (64 GB) good for AI?
With 64 GB unified memory and 800.0 GB/s bandwidth, the Mac Studio M1 Ultra (64 GB) is excellent for running local AI models. Models that run well include Gemma 4 26B A4B IT (25.8B at Q4_K_M, ~91.7 tok/s); Muse Glimmer 30B (29.8B at Q4_K_M, ~30.5 tok/s); Gemma 4 31B IT (31.3B at Q4_K_M, ~27.5 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 Studio M1 Ultra (64 GB)?
For the Mac Studio M1 Ultra (64 GB), the best pick right now is Qwen3.8 27B at ~32.1 tok/s. Also running well: Gemma 4 26B A4B IT (25.8B at Q4_K_M, ~91.7 tok/s); Muse Glimmer 30B (29.8B at Q4_K_M, ~30.5 tok/s); Gemma 4 31B IT (31.3B at Q4_K_M, ~27.5 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 M1 Ultra (64 GB) for AI inference?
With 800.0 GB/s memory bandwidth, the Mac Studio M1 Ultra (64 GB) achieves approximately 124 tok/s on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~62 tok/s. In the ranking, the top pick Qwen3.8 27B runs at ~32.1 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 = (800 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 M1 Ultra (64 GB)
Real-world results typically within ±20%.
Can I run AI offline on Mac Studio M1 Ultra (64 GB)?
Yes — once you download a model, it runs entirely on the Mac Studio M1 Ultra (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.