AI Models for Galaxy Z Fold8 Ultra — What runs on 16GB
Specs checked against manufacturer and public documentation on .
What runs on the Galaxy Z Fold8 Ultra
All 89 models at their recommended quant, on the 16GB configuration. Select any row for the full report.
| Model | Params | Quant | Needs | Speed | Verdict |
|---|---|---|---|---|---|
| Qwen3 0.6B | 0.6B | Q8_0 | 1.3 GB | ~63.6 tokens/s | ✓ Runs great |
| Qwen3 1.7B | 1.7B | Q8_0 | 2.6 GB | ~21.2 tokens/s | ✓ Runs great |
| Qwen3 4B | 4B | Q4_K_M | 3.5 GB | ~15.3 tokens/s | ✓ Runs great |
| Llama 3.2 1B | 1.2B | Q4_K_M | 1.5 GB | ~47.7 tokens/s | ✓ Runs great |
| Llama 3.2 3B | 3.2B | Q4_K_M | 2.9 GB | ~19.1 tokens/s | ✓ Runs great |
| Gemma 3 1B | 1B | Q4_K_M | 1.5 GB | ~47.7 tokens/s | ✓ Runs great |
| Gemma 3 4B | 4.3B | Q4_K_M | 3.5 GB | ~15.3 tokens/s | ✓ Runs great |
| Phi-4 Mini 3.8B | 3.8B | Q4_K_M | 3.5 GB | ~15.3 tokens/s | ✓ Runs great |
| DeepSeek R1 Distill 1.5B | 1.8B | Q4_K_M | 1.9 GB | ~34.7 tokens/s | ✓ Runs great |
| SmolLM2 1.7B | 1.7B | Q4_K_M | 1.9 GB | ~34.7 tokens/s | ✓ Runs great |
| SmolLM3 3B | 3.1B | Q4_K_M | 2.8 GB | ~20.1 tokens/s | ✓ Runs great |
| Qwen 3.5 2B | 2B | Q4_K_M | 2.1 GB | ~29.4 tokens/s | ✓ Runs great |
| Ternary Bonsai 8B | 8B | Q2_0 | 3.5 GB | ~17.3 tokens/s | ✓ Runs great |
| Ternary Bonsai 4B | 4B | Q2_0 | 2 GB | ~34.7 tokens/s | ✓ Runs great |
| Ternary Bonsai 1.7B | 1.7B | Q2_0 | 1.2 GB | ~76.3 tokens/s | ✓ Runs great |
| Ministral 3 3B | 3B | Q4_K_M | 3 GB | ~18.2 tokens/s | ✓ Runs great |
| LFM2.5 2.6B | 2.7B | Q4_K_M | 2.6 GB | ~22.4 tokens/s | ✓ Runs great |
| LFM2.5 8B-A1B | 8B | Q4_K_M | 6.6 GB | ~58.7 tokens/s | ✓ Runs great |
| LFM2.5-VL 3B | 3.1B | Q4_K_M | 2.6 GB | ~22.4 tokens/s | ✓ Runs great |
| OvisOCR2 0.8B | 0.8B | Q4_K_M | 1.3 GB | ~63.6 tokens/s | ✓ Runs great |
| G9v3 3B | 3B | Q4_K_M | 2.8 GB | ~20.1 tokens/s | ✓ Runs great |
| Tini Cybersec 8B-A1B | 8.5B | Q4_K_M | 6.7 GB | ~62.4 tokens/s | ✓ Runs great |
| Ling 3.0 Tiny | 7.9B | Q4_K_M | 6.3 GB | ~43.9 tokens/s | ✓ Runs great |
| Granite 4.2 3B | 3B | Q4_K_M | 3.1 GB | ~17.3 tokens/s | ✓ Runs great |
| Qwen 3.5 4B | 4B | Q4_K_M | 3.7 GB | ~14.1 tokens/s | ✓ Runs great |
| Agents-A1 4B | 4B | Q4_K_M | 3.7 GB | ~14.1 tokens/s | ✓ Runs great |
| Nanbeige 4.2 3B | 4.2B | Q4_K_M | 3.7 GB | ~14.1 tokens/s | ✓ Runs great |
| Nemotron 3 Nano 4B | 4B | Q4_K_M | 3.8 GB | ~13.6 tokens/s | ✓ Runs great |
| AREX Turbo 4B | 4.5B | Q4_K_M | 4 GB | ~13.2 tokens/s | ✓ Runs great |
| Fara 1.5 4B | 4.5B | Q4_K_M | 4 GB | ~13.2 tokens/s | ✓ Runs great |
| Gemma 4 E2B | 2B | Q4_K_M | 4 GB | ~12.3 tokens/s | ✓ Runs great |
| Bonsai 27B (1-bit) | 27B | Q1_0 | 4.8 GB | ~10 tokens/s | ✓ Runs great |
| Mistral 7B v0.3 | 7.2B | Q4_K_M | 5.7 GB | ~8.7 tokens/s | ✓ Runs great |
| DeepSeek R1 Distill 7B | 7.6B | Q4_K_M | 6.1 GB | ~8.1 tokens/s | ✓ Runs great |
| Llama 3.1 8B | 8B | Q4_K_M | 6.3 GB | ~7.8 tokens/s | ! Runs, barely |
| Ministral 8B | 8B | Q4_K_M | 6.3 GB | ~7.8 tokens/s | ! Runs, barely |
| Qwen3 8B | 8.2B | Q4_K_M | 6.4 GB | ~7.6 tokens/s | ! Runs, barely |
| Gemma 4 E4B | 4B | Q4_K_M | 6.1 GB | ~7.6 tokens/s | ! Runs, barely |
| Ministral 3 8B | 8B | Q4_K_M | 6.6 GB | ~7.3 tokens/s | ! Runs, barely |
| Granite 4.2 8B | 8B | Q4_K_M | 6.7 GB | ~7.2 tokens/s | ! Runs, barely |
| Qwen 3.5 9B | 9B | Q4_K_M | 7.2 GB | ~6.7 tokens/s | ! Runs, barely |
| Ornith 1.0 9B | 9B | Q4_K_M | 7.1 GB | ~6.8 tokens/s | ! Runs, barely |
| Qwythos 9B v2 | 9B | Q4_K_M | 7.2 GB | ~6.7 tokens/s | ! Runs, barely |
| GRM 3.2 Cliff 9B | 9.4B | Q4_K_M | 7.5 GB | ~6.5 tokens/s | ! Runs, barely |
| grug 9B (ProCreations) | 9.4B | Q4_K_M | 7.5 GB | ~6.5 tokens/s | ! Runs, barely |
| Fara 1.5 9B | 9.4B | Q4_K_M | 7.5 GB | ~6.5 tokens/s | ! Runs, barely |
| UI-Mate 9B | 9B | Q4_K_M | 7.4 GB | ~6.5 tokens/s | ! Runs, barely |
| Gemma 4 12B | 12B | Q4_0 | 8.8 GB | ~5.5 tokens/s | ! Runs, barely |
| Gemma 3 12B | 12.2B | Q4_K_M | 9.1 GB | ~5.2 tokens/s | ! Runs, barely |
| Ternary Bonsai 27B | 27B | Q2_0 | 8.4 GB | ~5.3 tokens/s | ! Runs, barely |
| Grug 12B | 12B | Q4_K_M | 9.5 GB | ~5 tokens/s | ! Runs, barely |
| Ministral 3 14B | 14B | Q4_K_M | 10.2 GB | ~4.7 tokens/s | ! Runs, barely |
| Qwen3 14B | 14.8B | Q4_K_M | 11.1 GB | ~4.2 tokens/s | ! Runs, barely |
| Phi-4 14B | 14.7B | Q4_K_M | 11.2 GB | ~4.2 tokens/s | ! Runs, barely |
| GPT-OSS 20B | 21B | MXFP4 | 14.8 GB | — | ✕ Won't fit |
| Gemma 4 26B-A4B | 26B | Q4_0 | 17.5 GB | — | ✕ Won't fit |
| Qwen 3.6 27B | 27B | Q4_K_M | 18.5 GB | — | ✕ Won't fit |
| Qwen 3.5 27B | 27B | Q4_K_M | 20 GB | — | ✕ Won't fit |
| Muse Glimmer 30B | 29.6B | K_QUANT_17GB | 20.3 GB | — | ✕ Won't fit |
| Fara 1.5 27B | 27B | Q4_K_M | 20.9 GB | — | ✕ Won't fit |
| Qwen3.8 27B | 27B | Q4_K_M | 20.8 GB | — | ✕ Won't fit |
| UI-Mate 27B | 27B | Q4_K_M | 20.9 GB | — | ✕ Won't fit |
| Granite 4.2 30B | 30B | Q4_K_M | 21.3 GB | — | ✕ Won't fit |
| Gemma 4 31B | 31B | Q4_K_M | 22 GB | — | ✕ Won't fit |
| Qwen3 30B A3B | 30.5B | Q4_K_M | 22.3 GB | — | ✕ Won't fit |
| Salience 1.5 Flash | 30B | Q4_K_M | 22.3 GB | — | ✕ Won't fit |
| Qwen3 32B | 32.8B | Q4_K_M | 23.7 GB | — | ✕ Won't fit |
| Qwen 3.5 35B-A3B | 35B | Q4_K_M | 26.2 GB | — | ✕ Won't fit |
| Qwen 3.6 35B-A3B | 35B | Q4_K_M | 23.9 GB | — | ✕ Won't fit |
| Nemotron 3 Nano 30B-A3B | 30B | Q4_K_M | 28.5 GB | — | ✕ Won't fit |
| Nemotron 3 Nano Omni 30B-A3B | 31B | Q4_K_M | 26.3 GB | — | ✕ Won't fit |
| Nemotron 3.5 Lightning 30B-A3B | 30B | Q8_0 | 35.9 GB | — | ✕ Won't fit |
| Ornith 1.0 35B-A3B | 35B | Q4_K_M | 25.3 GB | — | ✕ Won't fit |
| XYZ-Aquila mini 35B-A3B | 35B | Q4_K_M | 25.5 GB | — | ✕ Won't fit |
| KAT-Coder V2.5 Dev | 35B | Q4_K_M | 25.5 GB | — | ✕ Won't fit |
| BigBang V1 36B-A3B | 36B | Q4_K_M | 26.1 GB | — | ✕ Won't fit |
| Qwen3.8 2.4T-A95B | 2400B | IQ4_XS | 1377.4 GB | — | ✕ Won't fit |
| GLM-4.5V | 106B | Q4_K_M | 74.8 GB | — | ✕ Won't fit |
| GLM-4.5-Air | 106B | Q4_K_M | 74.8 GB | — | ✕ Won't fit |
| Ling 3.0 Flash | 124B | Q4_K_M | 82.3 GB | — | ✕ Won't fit |
| Llama 3.3 70B | 70B | Q4_K_M | 50.1 GB | — | ✕ Won't fit |
| Ornith 1.0 397B | 397B | Q4_K_M | 285.6 GB | — | ✕ Won't fit |
| Hunyuan 3 (Hy3) | 298.8B | Q4_K_M | 212.8 GB | — | ✕ Won't fit |
| DeepSeek V4 Flash 0731 | 284B | Q4_K_XL | 163.6 GB | — | ✕ Won't fit |
| Inkling | 952.4B | Q8_0 | 966.9 GB | — | ✕ Won't fit |
| Inkling Small | 276B | Q4_K_M | 190.5 GB | — | ✕ Won't fit |
| LongCat Flash Chat | 561.9B | IQ1_S | 159.6 GB | — | ✕ Won't fit |
| Laguna XS 2.1 | 33B | Q4_K_M | 24.2 GB | — | ✕ Won't fit |
| Laguna S 2.1 | 118B | Q4_K_M | 109.7 GB | — | ✕ Won't fit |
~ = bandwidth-based estimate · ✓ = measured on real hardware
Best model by use case
Top everyday assistant & writing pick here — ~63.6 tokens/s at Q8_0, using 1.3 of ~12GB.
Top code completion & explain-this pick here — ~15.3 tokens/s at Q4_K_M, using 3.5 of ~12GB.
Top math & step-by-step thinking pick here — ~15.3 tokens/s at Q4_K_M, using 3.5 of ~12GB.
FAQ
What is the biggest AI model the Galaxy Z Fold8 Ultra can run?
Bonsai 27B (1-bit) (27B parameters) at Q1_0 — it needs 4.8GB of the ~12GB usable on the 16GB Galaxy Z Fold8 Ultra and runs at ~10 tokens/s.
How much of the Galaxy Z Fold8 Ultra's 16GB RAM can AI models actually use?
About 12GB. Android keeps roughly 2–4GB for the system and resident apps, so of the 16GB about 12GB is actually available to a model.
Can the Galaxy Z Fold8 Ultra run Llama 3.1 8B?
Yes — at Q4_K_M it needs 6.3GB of the ~12GB usable and runs at ~7.8 tokens/s.
How fast is local AI on the Galaxy Z Fold8 Ultra?
The Snapdragon 8 Elite Gen 5 has 84.8GB/s of memory bandwidth, which is what decode speed scales with. Small models like Ternary Bonsai 1.7B reach ~76.3 tokens/s; larger 7–14B models land in the single digits. Anything above ~8 tokens/s feels smooth for chat.
Which quantization should I use on the Galaxy Z Fold8 Ultra?
Q4_K_M is the size/quality sweet spot for most models. For example, Qwen3 0.6B at Q8_0 takes 1.3GB of memory here. Only drop to Q3 or IQ4 if a model just misses fitting; Q8 rarely pays off on 16GB of RAM.
Is 16GB of RAM enough for local AI?
54 of the 89 models we track fit on the Galaxy Z Fold8 Ultra — 34 run great and 20 run with compromises. 35 models (mostly 12B+) don't fit at their recommended quant.