AI Models for vivo X Fold3 — What runs on 16GB

32 great · 22 slow · 40 won't fit

Chip

Snapdragon 8 Gen 2

Memory bandwidth

67.2 GB/s

NPU

26 TOPS

RAM options

12 / 16 GB

Usable for models

~12 GB

Year

2024

Specs checked against manufacturer and public documentation on 2026-07-18. Sold in China only; available elsewhere via import.

What runs on the vivo X Fold3

All 94 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	~50.4 tokens/s	✓ Runs great
Qwen3 1.7B	1.7B	Q8_0	2.6 GB	~16.8 tokens/s	✓ Runs great
Llama 3.2 1B	1.2B	Q4_K_M	1.5 GB	~37.8 tokens/s	✓ Runs great
Llama 3.2 3B	3.2B	Q4_K_M	2.9 GB	~15.1 tokens/s	✓ Runs great
Gemma 3 1B	1B	Q4_K_M	1.5 GB	~37.8 tokens/s	✓ Runs great
DeepSeek R1 Distill 1.5B	1.8B	Q4_K_M	1.9 GB	~27.5 tokens/s	✓ Runs great
SmolLM2 1.7B	1.7B	Q4_K_M	1.9 GB	~27.5 tokens/s	✓ Runs great
SmolLM3 3B	3.1B	Q4_K_M	2.8 GB	~15.9 tokens/s	✓ Runs great
Qwen 3.5 2B	2B	Q4_K_M	2.1 GB	~23.3 tokens/s	✓ Runs great
Ternary Bonsai 4B	4B	Q2_0	2 GB	~27.5 tokens/s	✓ Runs great
Ternary Bonsai 1.7B	1.7B	Q2_0	1.2 GB	~60.5 tokens/s	✓ Runs great
LFM2.5 2.6B	2.7B	Q4_K_M	2.6 GB	~17.8 tokens/s	✓ Runs great
LFM2.5 8B-A1B	8B	Q4_K_M	6.6 GB	~46.5 tokens/s	✓ Runs great
LFM2.5-VL 3B	3.1B	Q4_K_M	2.6 GB	~17.8 tokens/s	✓ Runs great
OvisOCR2 0.8B	0.8B	Q4_K_M	1.3 GB	~50.4 tokens/s	✓ Runs great
G9v3 3B	3B	Q4_K_M	2.8 GB	~15.9 tokens/s	✓ Runs great
Tini Cybersec 8B-A1B	8.5B	Q4_K_M	6.7 GB	~49.4 tokens/s	✓ Runs great
Ling 3.0 Tiny	7.9B	Q4_K_M	6.3 GB	~34.8 tokens/s	✓ Runs great
Ministral 3 3B	3B	Q4_K_M	3 GB	~14.4 tokens/s	✓ Runs great
Ternary Bonsai 8B	8B	Q2_0	3.5 GB	~13.7 tokens/s	✓ Runs great
Granite 4.2 3B	3B	Q4_K_M	3.1 GB	~13.7 tokens/s	✓ Runs great
Qwen3 4B	4B	Q4_K_M	3.5 GB	~12.1 tokens/s	✓ Runs great
Gemma 3 4B	4.3B	Q4_K_M	3.5 GB	~12.1 tokens/s	✓ Runs great
Phi-4 Mini 3.8B	3.8B	Q4_K_M	3.5 GB	~12.1 tokens/s	✓ Runs great
Qwen 3.5 4B	4B	Q4_K_M	3.7 GB	~11.2 tokens/s	✓ Runs great
Agents-A1 4B	4B	Q4_K_M	3.7 GB	~11.2 tokens/s	✓ Runs great
Nanbeige 4.2 3B	4.2B	Q4_K_M	3.7 GB	~11.2 tokens/s	✓ Runs great
Nemotron 3 Nano 4B	4B	Q4_K_M	3.8 GB	~10.8 tokens/s	✓ Runs great
AREX Turbo 4B	4.5B	Q4_K_M	4 GB	~10.4 tokens/s	✓ Runs great
Fara 1.5 4B	4.5B	Q4_K_M	4 GB	~10.4 tokens/s	✓ Runs great
Gemma 4 E2B	2B	Q4_K_M	4 GB	~9.8 tokens/s	✓ Runs great
Bonsai 27B (1-bit)	27B	Q1_0	4.8 GB	~8 tokens/s	✓ Runs great
Mistral 7B v0.3	7.2B	Q4_K_M	5.7 GB	~6.9 tokens/s	! Runs, barely
Llama 3.1 8B	8B	Q4_K_M	6.3 GB	~6.2 tokens/s	! Runs, barely
Ministral 8B	8B	Q4_K_M	6.3 GB	~6.2 tokens/s	! Runs, barely
DeepSeek R1 Distill 7B	7.6B	Q4_K_M	6.1 GB	~6.4 tokens/s	! Runs, barely
Qwen3 8B	8.2B	Q4_K_M	6.4 GB	~6 tokens/s	! Runs, barely
Gemma 4 E4B	4B	Q4_K_M	6.1 GB	~6 tokens/s	! Runs, barely
Ministral 3 8B	8B	Q4_K_M	6.6 GB	~5.8 tokens/s	! Runs, barely
Granite 4.2 8B	8B	Q4_K_M	6.7 GB	~5.7 tokens/s	! Runs, barely
Qwen 3.5 9B	9B	Q4_K_M	7.2 GB	~5.3 tokens/s	! Runs, barely
Ornith 1.0 9B	9B	Q4_K_M	7.1 GB	~5.4 tokens/s	! Runs, barely
GRM 3.2 Cliff 9B	9.4B	Q4_K_M	7.5 GB	~5.1 tokens/s	! Runs, barely
Qwythos 9B v2	9B	Q4_K_M	7.2 GB	~5.3 tokens/s	! Runs, barely
grug 9B (ProCreations)	9.4B	Q4_K_M	7.5 GB	~5.1 tokens/s	! Runs, barely
Fara 1.5 9B	9.4B	Q4_K_M	7.5 GB	~5.1 tokens/s	! Runs, barely
UI-Mate 9B	9B	Q4_K_M	7.4 GB	~5.1 tokens/s	! Runs, barely
Gemma 3 12B	12.2B	Q4_K_M	9.1 GB	~4.1 tokens/s	! Runs, barely
Gemma 4 12B	12B	Q4_0	8.8 GB	~4.3 tokens/s	! Runs, barely
Ternary Bonsai 27B	27B	Q2_0	8.4 GB	~4.2 tokens/s	! Runs, barely
Grug 12B	12B	Q4_K_M	9.5 GB	~3.9 tokens/s	! Runs, barely
Ministral 3 14B	14B	Q4_K_M	10.2 GB	~3.7 tokens/s	! Runs, barely
Qwen3 14B	14.8B	Q4_K_M	11.1 GB	~3.4 tokens/s	! Runs, barely
Phi-4 14B	14.7B	Q4_K_M	11.2 GB	~3.3 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
Apodex 1.1 Mini	35B	Q4_K_M	26 GB	—	✕ Won't fit
Ling 3.0 Flash	124B	Q4_K_M	82.3 GB	—	✕ Won't fit
Qwen3.8-Flash-Next	125B	IQ4_XS	107.7 GB	—	✕ Won't fit
GLM-5.3	744B	Q4_K_XL	543.3 GB	—	✕ Won't fit
GLM-5.3-Flash	320B	Q4_K_XL	232.7 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
DeepSeek V4 Flash Vision Exp	284B	Q4_K_XL	183.3 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

Best for Chat

Qwen3 0.6B

Top everyday assistant & writing pick here — ~50.4 tokens/s at Q8_0, using 1.3 of ~12GB.

Best for Coding

G9v3 3B

Top code completion & explain-this pick here — ~15.9 tokens/s at Q4_K_M, using 2.8 of ~12GB.

Best for Reasoning

DeepSeek R1 Distill 1.5B

Top math & step-by-step thinking pick here — ~27.5 tokens/s at Q4_K_M, using 1.9 of ~12GB.

FAQ

What is the biggest AI model the vivo X Fold3 can run?

Bonsai 27B (1-bit) (27B parameters) at Q1_0 — it needs 4.8GB of the ~12GB usable on the 16GB vivo X Fold3 and runs at ~8 tokens/s.

How much of the vivo X Fold3'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 vivo X Fold3 run Llama 3.1 8B?

Yes — at Q4_K_M it needs 6.3GB of the ~12GB usable and runs at ~6.2 tokens/s.

How fast is local AI on the vivo X Fold3?

The Snapdragon 8 Gen 2 has 67.2GB/s of memory bandwidth, which is what decode speed scales with. Small models like Ternary Bonsai 1.7B reach ~60.5 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 vivo X Fold3?

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 94 models we track fit on the vivo X Fold3 — 32 run great and 22 run with compromises. 40 models (mostly 12B+) don't fit at their recommended quant.

Other vivo phones

vivo X200 Pro vivo X100 Pro vivo X300 Pro vivo X300 E vivo X Fold3 Pro vivo X Fold5 vivo X Fold6 vivo S2 vivo V80 Lite 5G (Pakistan)