AI Models for Mac Studio M5 Max — What runs on 36GB
Specs checked against manufacturer and public documentation on . Results below are estimates, not measurements.
What runs on the Mac Studio M5 Max
All 89 models use the recommended quant when it fits, or the largest smaller tracked fallback for this 36GB configuration. Select any row for the full report and step-by-step setup guide.
| Model | Params | Quant | Needs | Speed | Verdict |
|---|---|---|---|---|---|
| Ling 3.0 Flash | 124B | IQ1_Ssmaller version | 29.8GB | ~202.6–283.7 tokens/s | ✓ Runs great |
| BigBang V1 36B-A3B | 36B | Q4_K_M | 26.3GB | ~126–176.4 tokens/s | ✓ Runs great |
| Qwen 3.5 35B-A3B | 35B | Q4_K_M | 26.4GB | ~122–170.8 tokens/s | ✓ Runs great |
| Qwen 3.6 35B-A3B | 35B | Q4_K_M | 24.1GB | ~121.4–170 tokens/s | ✓ Runs great |
| Ornith 1.0 35B-A3B | 35B | Q4_K_M | 25.5GB | ~126.6–177.2 tokens/s | ✓ Runs great |
| XYZ-Aquila mini 35B-A3B | 35B | Q4_K_M | 25.7GB | ~125.4–175.5 tokens/s | ✓ Runs great |
| KAT-Coder V2.5 Dev | 35B | Q4_K_M | 25.7GB | ~125.4–175.5 tokens/s | ✓ Runs great |
| Laguna XS 2.1 | 33B | Q4_K_M | 24.4GB | ~124.6–174.5 tokens/s | ✓ Runs great |
| Qwen3 32B | 32.8B | Q4_K_M | 23.9GB | ~11.6–16.3 tokens/s | ✓ Runs great |
| Nemotron 3 Nano Omni 30B-A3B | 31B | Q4_K_M | 26.5GB | ~106.1–148.5 tokens/s | ✓ Runs great |
| Gemma 4 31B | 31B | Q4_K_M | 22.2GB | ~12.6–17.6 tokens/s | ✓ Runs great |
| Qwen3 30B A3B | 30.5B | Q4_K_M | 22.5GB | ~114.3–160 tokens/s | ✓ Runs great |
| Nemotron 3 Nano 30B-A3B | 30B | Q4_K_M | 28.7GB | ~93.5–130.9 tokens/s | ✓ Runs great |
| Nemotron 3.5 Lightning 30B-A3B | 30B | Q4_0smaller version | 20.7GB | ~121.7–170.4 tokens/s | ✓ Runs great |
| Salience 1.5 Flash | 30B | Q4_K_M | 22.5GB | ~111.8–156.5 tokens/s | ✓ Runs great |
| Granite 4.2 30B | 30B | Q4_K_M | 21.5GB | ~13–18.2 tokens/s | ✓ Runs great |
| Muse Glimmer 30B | 29.6B | K_QUANT_17GB | 20.5GB | ~13.7–19.2 tokens/s | ✓ Runs great |
| Qwen 3.5 27B | 27B | Q4_K_M | 20.2GB | ~13.8–19.3 tokens/s | ✓ Runs great |
| Qwen 3.6 27B | 27B | Q4_K_M | 18.7GB | ~13.7–19.2 tokens/s | ✓ Runs great |
| Bonsai 27B (1-bit) | 27B | Q1_0 | 5GB | ~60.5–84.7 tokens/s | ✓ Runs great |
| Ternary Bonsai 27B | 27B | Q2_0 | 8.6GB | ~31.9–44.7 tokens/s | ✓ Runs great |
| Fara 1.5 27B | 27B | Q4_K_M | 21.1GB | ~13.1–18.4 tokens/s | ✓ Runs great |
| Qwen3.8 27B | 27B | Q4_K_M | 21GB | ~12.1–16.9 tokens/s | ✓ Runs great |
| UI-Mate 27B | 27B | Q4_K_M | 21.1GB | ~13.1–18.4 tokens/s | ✓ Runs great |
| Gemma 4 26B-A4B | 26B | Q4_0 | 17.7GB | ~103.8–145.3 tokens/s | ✓ Runs great |
| GPT-OSS 20B | 21B | MXFP4 | 15GB | ~110.9–155.2 tokens/s | ✓ Runs great |
| Qwen3 14B | 14.8B | Q4_K_M | 11.3GB | ~25.6–35.8 tokens/s | ✓ Runs great |
| Phi-4 14B | 14.7B | Q4_K_M | 11.4GB | ~25.3–35.4 tokens/s | ✓ Runs great |
| Ministral 3 14B | 14B | Q4_K_M | 10.4GB | ~28–39.3 tokens/s | ✓ Runs great |
| Gemma 3 12B | 12.2B | Q4_K_M | 9.3GB | ~31.5–44.1 tokens/s | ✓ Runs great |
| Gemma 4 12B | 12B | Q4_0 | 9GB | ~32.9–46 tokens/s | ✓ Runs great |
| Grug 12B | 12B | Q4_K_M | 9.7GB | ~29.9–41.8 tokens/s | ✓ Runs great |
| GRM 3.2 Cliff 9B | 9.4B | Q4_K_M | 7.7GB | ~39–54.6 tokens/s | ✓ Runs great |
| grug 9B (ProCreations) | 9.4B | Q4_K_M | 7.7GB | ~39–54.6 tokens/s | ✓ Runs great |
| Fara 1.5 9B | 9.4B | Q4_K_M | 7.7GB | ~39–54.6 tokens/s | ✓ Runs great |
| Qwen 3.5 9B | 9B | Q4_K_M | 7.4GB | ~40.4–56.5 tokens/s | ✓ Runs great |
| Ornith 1.0 9B | 9B | Q4_K_M | 7.3GB | ~41.1–57.5 tokens/s | ✓ Runs great |
| Qwythos 9B v2 | 9B | Q4_K_M | 7.4GB | ~40.4–56.5 tokens/s | ✓ Runs great |
| UI-Mate 9B | 9B | Q4_K_M | 7.6GB | ~39–54.6 tokens/s | ✓ Runs great |
| Tini Cybersec 8B-A1B | 8.5B | Q4_K_M | 6.9GB | ~376–526.3 tokens/s | ✓ Runs great |
| Qwen3 8B | 8.2B | Q4_K_M | 6.6GB | ~46–64.4 tokens/s | ✓ Runs great |
| Llama 3.1 8B | 8B | Q4_K_M | 6.5GB | ~46.9–65.7 tokens/s | ✓ Runs great |
| Ministral 8B | 8B | Q4_K_M | 6.5GB | ~46.9–65.7 tokens/s | ✓ Runs great |
| Ternary Bonsai 8B | 8B | Q2_0 | 3.7GB | ~104.5–146.4 tokens/s | ✓ Runs great |
| Ministral 3 8B | 8B | Q4_K_M | 6.8GB | ~44.2–61.9 tokens/s | ✓ Runs great |
| LFM2.5 8B-A1B | 8B | Q4_K_M | 6.8GB | ~353.8–495.4 tokens/s | ✓ Runs great |
| Granite 4.2 8B | 8B | Q4_K_M | 6.9GB | ~43.4–60.8 tokens/s | ✓ Runs great |
| Ling 3.0 Tiny | 7.9B | Q4_K_M | 6.5GB | ~264.9–370.8 tokens/s | ✓ Runs great |
| DeepSeek R1 Distill 7B | 7.6B | Q4_K_M | 6.3GB | ~48.9–68.5 tokens/s | ✓ Runs great |
| Mistral 7B v0.3 | 7.2B | Q4_K_M | 5.9GB | ~52.3–73.2 tokens/s | ✓ Runs great |
| AREX Turbo 4B | 4.5B | Q4_K_M | 4.2GB | ~79.3–111 tokens/s | ✓ Runs great |
| Fara 1.5 4B | 4.5B | Q4_K_M | 4.2GB | ~79.3–111 tokens/s | ✓ Runs great |
| Gemma 3 4B | 4.3B | Q4_K_M | 3.7GB | ~92–128.8 tokens/s | ✓ Runs great |
| Nanbeige 4.2 3B | 4.2B | Q4_K_M | 3.9GB | ~85.2–119.3 tokens/s | ✓ Runs great |
| Qwen3 4B | 4B | Q4_K_M | 3.7GB | ~92–128.8 tokens/s | ✓ Runs great |
| Qwen 3.5 4B | 4B | Q4_K_M | 3.9GB | ~85.2–119.3 tokens/s | ✓ Runs great |
| Gemma 4 E4B | 4B | Q4_K_M | 6.3GB | ~46–64.4 tokens/s | ✓ Runs great |
| Ternary Bonsai 4B | 4B | Q2_0 | 2.2GB | ~209.1–292.7 tokens/s | ✓ Runs great |
| Nemotron 3 Nano 4B | 4B | Q4_K_M | 4GB | ~82.1–115 tokens/s | ✓ Runs great |
| Agents-A1 4B | 4B | Q4_K_M | 3.9GB | ~85.2–119.3 tokens/s | ✓ Runs great |
| Phi-4 Mini 3.8B | 3.8B | Q4_K_M | 3.7GB | ~92–128.8 tokens/s | ✓ Runs great |
| Llama 3.2 3B | 3.2B | Q4_K_M | 3.1GB | ~115–161 tokens/s | ✓ Runs great |
| SmolLM3 3B | 3.1B | Q4_K_M | 3GB | ~121.1–169.5 tokens/s | ✓ Runs great |
| LFM2.5-VL 3B | 3.1B | Q4_K_M | 2.8GB | ~135.3–189.4 tokens/s | ✓ Runs great |
| Ministral 3 3B | 3B | Q4_K_M | 3.2GB | ~109.5–153.3 tokens/s | ✓ Runs great |
| G9v3 3B | 3B | Q4_K_M | 3GB | ~121.1–169.5 tokens/s | ✓ Runs great |
| Granite 4.2 3B | 3B | Q4_K_M | 3.3GB | ~104.5–146.4 tokens/s | ✓ Runs great |
| LFM2.5 2.6B | 2.7B | Q4_K_M | 2.8GB | ~135.3–189.4 tokens/s | ✓ Runs great |
| Qwen 3.5 2B | 2B | Q4_K_M | 2.3GB | ~176.9–247.7 tokens/s | ✓ Runs great |
| Gemma 4 E2B | 2B | Q4_K_M | 4.2GB | ~74.2–103.9 tokens/s | ✓ Runs great |
| DeepSeek R1 Distill 1.5B | 1.8B | Q4_K_M | 2.1GB | ~209.1–292.7 tokens/s | ✓ Runs great |
| Qwen3 1.7B | 1.7B | Q8_0 | 2.8GB | ~127.8–178.9 tokens/s | ✓ Runs great |
| SmolLM2 1.7B | 1.7B | Q4_K_M | 2.1GB | ~209.1–292.7 tokens/s | ✓ Runs great |
| Ternary Bonsai 1.7B | 1.7B | Q2_0 | 1.4GB | ~460–644 tokens/s | ✓ Runs great |
| Llama 3.2 1B | 1.2B | Q4_K_M | 1.7GB | ~287.5–402.5 tokens/s | ✓ Runs great |
| Gemma 3 1B | 1B | Q4_K_M | 1.7GB | ~287.5–402.5 tokens/s | ✓ Runs great |
| OvisOCR2 0.8B | 0.8B | Q4_K_M | 1.5GB | ~383.3–536.7 tokens/s | ✓ Runs great |
| Qwen3 0.6B | 0.6B | Q8_0 | 1.5GB | ~383.3–536.7 tokens/s | ✓ Runs great |
| Qwen3.8 2.4T-A95B | 2400B | IQ4_XS | 1377.6GB | — | ✕ Won't fit |
| Inkling | 952.4B | Q8_0 | 967.1GB | — | ✕ Won't fit |
| LongCat Flash Chat | 561.9B | IQ1_S | 159.8GB | — | ✕ Won't fit |
| Ornith 1.0 397B | 397B | Q4_K_M | 285.8GB | — | ✕ Won't fit |
| Hunyuan 3 (Hy3) | 298.8B | Q4_K_M | 213GB | — | ✕ Won't fit |
| DeepSeek V4 Flash 0731 | 284B | Q4_K_XL | 163.8GB | — | ✕ Won't fit |
| Inkling Small | 276B | Q4_K_M | 190.7GB | — | ✕ Won't fit |
| Laguna S 2.1 | 118B | Q4_K_M | 109.9GB | — | ✕ Won't fit |
| GLM-4.5V | 106B | Q4_K_M | 75GB | — | ✕ Won't fit |
| GLM-4.5-Air | 106B | Q4_K_M | 75GB | — | ✕ Won't fit |
| Llama 3.3 70B | 70B | Q4_K_M | 50.3GB | — | ✕ Won't fit |
~ = bandwidth-based estimate · open a row to see the recommended app and exact steps
Other Mac Studio M5 Max memory options
FAQ
What is the biggest local AI model the Mac Studio M5 Max · 36GB can run?
Ling 3.0 Flash is the largest model in our 89-model comparison that fits at IQ1_S. It needs about 29.8GB inside our 32.4GB working-memory budget, with an estimated 202.6–283.7 tokens/s decode range.
How much of the 36GB unified memory is available to a local LLM?
We use a conservative 32.4GB working budget, leaving room for macOS, the inference app, and normal background activity. Memory pressure, context length, and other open apps can change the real limit.
Can the Mac Studio M5 Max · 36GB run Llama 3.1 8B?
Yes. At Q4_K_M, our estimate uses 6.5GB and lands around 46.9–65.7 tokens/s.
Can the Mac Studio M5 Max · 36GB run Qwen 3.6 27B?
Yes at Q4_K_M: about 18.7GB of working memory and an estimated 13.7–19.2 tokens/s.
Can the Mac Studio M5 Max · 36GB run DeepSeek V4 Flash 0731?
No tracked quant fits our conservative 32.4GB working-memory budget. The recommended Q4_K_XL alone needs about 163.8GB at 4K context.
Which app should I use for local AI on the Mac Studio M5 Max?
For most exact GGUF repositories and quants on this site, start with LM Studio's graphical Discover, download, load, and chat flow. Jan is the open-source GGUF alternative; Ollama is strongest when the exact model already has a trustworthy catalog package or you want coding/API integrations; Msty is useful for mixed GGUF, MLX, and document workflows. DeepSeek V4 Flash uses a separate Unsloth Desktop path because of its sharded GGUF packaging. For the curated Bonsai 27B build, try Locally AI and first confirm that its catalog shows the model on this Mac.
Can I upgrade the Mac Studio M5 Max to more unified memory later?
No. Apple-silicon unified memory is integrated into the chip package and must be chosen at purchase. Storage upgrades or external SSDs do not increase the memory an LLM can use.
Are these Mac Studio M5 Max local AI speeds measured?
No. Every speed on this page is a formula range based on Apple’s published memory bandwidth, model size, active parameters, and a cooling-aware efficiency range. App, backend, context length, and thermals can change real results.