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AppleM5 MaxDesktop

Best AI Models for Mac Studio M5 Max (48 GB)

Memory:48 GB UnifiedBandwidth:614.0 GB/sGPU Cores:40 GPU coresCPU Cores:18 CPU coresNeural Engine:38.0 TOPS

48 GB unified − 3.5 GB OS overhead = 44.5 GB available for AI models

How does this device compare with your machine?

205 of 335 featured models run great on Mac Studio M5 Max (48 GB) · best pick: Qwen3.8 27B at ~24.7 tok/s

largest that fits: 81.3B (Qwen3 Next 80B A3B Instruct, IQ4_XS) · 120B needs Q2 or offload

What LLMs Can Mac Studio M5 Max (48 GB) Run?

335 models · 205 run great · 56 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 M5 Max (48 GB)

LLM models compatible with Mac Studio M5 Max (48 GB) — ranked by quality, recency and Fit
ModelVRAMScoreFit
D1 3B
3.1B·Q4_K_M·186.9 t/s·33K ctx
2.6 GBS99, Runs great
FrogNano 4B 2609
4.7B·Q4_K_M·131.8 t/s·262K ctx
3.4 GBS95, Runs great
ZAYA1 VL 8B
9.7B·Q4_K_M·110.4 t/s·33K ctx
7.5 GBS93, Runs great
1.2B·Q4_K_M·421.4 t/s·8K ctx
1.1 GBS100, Runs great
QwQ 32B
32.8B·Q4_K_M·21.0 t/s·41K ctx
24.3 GBB61, Decent
North Mini Code 1.0
30.5B·Q4_K_M·68.9 t/s·500K ctx
19.4 GBS86, Runs great
Clef
27.4B·Q4_K_M·25.0 t/s·262K ctx
17.6 GBA66, Runs well
Cosmos Reason2 2B
2.4B·Q4_K_M·267.0 t/s
3.5 GBS100, Runs great
D1 Omni 600M
587M·Q4_K_M·596.9 t/s·128K ctx
1.2 GBS100, Runs great
2.5B·BF16·78.1 t/s·500K ctx
7.1 GBS87, Runs great
LFM2.5 VL 450M
449M·Q4_K_M·671.6 t/s·128K ctx
0.8 GBS100, Runs great
Qwen3.5 27B
27.8B·Q4_K_M·24.7 t/s·262K ctx
17.9 GBA65, Runs well
Holo4 35B A3B
35.1B·Q4_K_M·73.8 t/s·262K ctx
21.5 GBS86, Runs great
Molmo2 8B
8.7B·Q4_K_M·74.1 t/s·37K ctx
7.9 GBS86, Runs great
Intern Decision 4B
4.5B·Q4_K_M·134.7 t/s·262K ctx
3.4 GBS95, Runs great
Qwen3.5 2B
2.3B·Q4_K_M·242.8 t/s·262K ctx
1.9 GBS100, Runs great

Mac Studio M5 Max (48 GB) has 44.5 GB of memory for models. In the ranking below, Qwen3.8 27B leads at Q4_K_M: it needs 17.9 GB (40% of that memory) and generates about 24.7 tokens per second. 205 of the 335 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 (614 GB/s), so smaller and mixture-of-experts models are noticeably quicker than dense models of the same quality. 120B-class models need about 77 GB at Q4_K_M, more than the 44.5 GB available; they fit only at Q2 with a quality loss, or by offloading layers to system RAM.

Runs Well

  • Qwen3.8 27B (27.8B at Q4_K_M, ~24.7 tok/s)
  • Qwen3.6 35B A3B (36.0B at Q4_K_M, ~67.8 tok/s)
  • Gemma 4 26B A4B IT (25.8B at Q4_K_M, ~80.7 tok/s)
  • NVIDIA Nemotron 3.5 Lightning 30B A3B BF16 (31.6B at Q4_K_M, ~72.7 tok/s)
  • GLM 4.7 Flash (31.2B at Q4_K_M, ~61.7 tok/s)

Challenging

  • Muse Glimmer 30B (29.8B at Q4_K_M, ~23.4 tok/s) fits but uses 44% of memory, so keep context short
  • Gemma 4 31B IT (31.3B at Q4_K_M, ~21.1 tok/s) fits but uses 49% of memory, so keep context short
  • DeepSeek R1 Distill Qwen 32B (32.8B at Q4_K_M, ~21 tok/s) fits but uses 55% of memory, so keep context short
  • 120B-class models need ~77 GB at Q4_K_M, so only Q2 fits
  • 235B-class models need ~150 GB at Q4_K_M, so they need CPU offload or more memory

Mac Studio M5 Max (48 GB) Specifications

Brand
Apple
Chip
M5 Max
Type
Desktop
Unified Memory
48 GB
Memory Bandwidth
614.0 GB/s
GPU Cores
40
CPU Cores
18
Neural Engine
38.0 TOPS
Release Date
2026-09-22

Get Started

Ollama (Recommended)

Runs Qwen3.8 27B on Q4_K_M; this is our top choice here.

$curl -fsSL https://ollama.com/install.sh | sh
$ollama run hf.co/unsloth/Qwen3.8-27B-GGUF:Q4_K_M

LM Studio

LM Studio

Download LM Studio, search for "Qwen3.8 27B", and run it with one click.

Prompt Processing

Estimated for Qwen3.8 27B, the compute-bound phase that reads your prompt before the first reply token appears.

244.8tok/s prefill

Short chat

2.1 s

512 tok prompt

Long chat

16.7 s

4,096 tok prompt

Document / codebase

134 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 M5 Max (48 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.

Frequently Asked Questions

Can Mac Studio M5 Max (48 GB) run Qwen3.8 27B?

Yes. The Mac Studio M5 Max (48 GB) with 48 GB unified memory can run Qwen3.8 27B (Q4_K_M, 17.9 GB, 40% of memory). It is also the table's best pick, at ~24.7 tok/s. 205 of the 335 featured models get a Fit of S (runs great) here. The largest featured model that fits at Q4 or better is Qwen3 Next 80B A3B Instruct (81.3B at IQ4_XS). 120B-class models need about 77 GB at Q4_K_M, so only Q2 (with a quality loss) or CPU offload gets them running in 44.5 GB. Check the compatibility table above for the full list with Fit, VRAM usage and estimated speed. Models are checked against the 44.5 GB available to AI (that is, the 48 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 M5 Max (48 GB)?

The Mac Studio M5 Max (48 GB) has 48 GB unified memory. After macOS reserves ~3.5 GB for the operating system, approximately 44.5 GB is available for AI models. Model rankings here use that 44.5 GB limit instead of the total 48 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 M5 Max (48 GB) good for AI?

With 48 GB unified memory and 614.0 GB/s bandwidth, the Mac Studio M5 Max (48 GB) is excellent for running local AI models. Models that run well include Qwen3.6 35B A3B (36.0B at Q4_K_M, ~67.8 tok/s); Gemma 4 26B A4B IT (25.8B at Q4_K_M, ~80.7 tok/s); NVIDIA Nemotron 3.5 Lightning 30B A3B BF16 (31.6B at Q4_K_M, ~72.7 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 M5 Max (48 GB)?

For the Mac Studio M5 Max (48 GB), the best pick right now is Qwen3.8 27B at ~24.7 tok/s. Also running well: Qwen3.6 35B A3B (36.0B at Q4_K_M, ~67.8 tok/s); Gemma 4 26B A4B IT (25.8B at Q4_K_M, ~80.7 tok/s); NVIDIA Nemotron 3.5 Lightning 30B A3B BF16 (31.6B at Q4_K_M, ~72.7 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 M5 Max (48 GB) for AI inference?

With 614.0 GB/s memory bandwidth, the Mac Studio M5 Max (48 GB) achieves approximately 96 tok/s on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~48 tok/s. In the ranking, the top pick Qwen3.8 27B runs at ~24.7 tok/s. Apple Silicon achieves high efficiency (~70%) thanks to unified memory — there's no PCIe bottleneck between CPU and GPU.

= (614 GB/s ÷ model GB) × efficiency

Apple Silicon achieves ~70% bandwidth efficiency thanks to and Metal acceleration.

Estimated speed on Mac Studio M5 Max (48 GB)

~25 tok/s
~68 tok/s
~81 tok/s
~23 tok/s

Real-world results typically within ±20%.

Learn more about tok/s estimation →

Can I run AI offline on Mac Studio M5 Max (48 GB)?

Yes — once you download a model, it runs entirely on the Mac Studio M5 Max (48 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.