AI Models for vivo X300 E — What runs on 12GB

vivo X300 E is not officially announced yet — chip and RAM below are consistent leaked specs. This page updates the moment official specs land.

25 great · 13 slow · 26 won't fit

Chip

Snapdragon 8 Gen 5

Memory bandwidth

76.8 GB/s

NPU

—

RAM options

12 GB

Usable for models

~9 GB

Year

2026

What runs on the vivo X300 E

All 64 models at their recommended quant, on the 12GB configuration. Tap a model for the full report.

	Model	Params	Quant	Needs	Speed	Verdict
S	Qwen3 0.6B	0.6B	Q8_0	1.3 GB	~57.6 tokens/s	✓ Runs great
S	Qwen3 1.7B	1.7B	Q8_0	2.6 GB	~19.2 tokens/s	✓ Runs great
S	Llama 3.2 1B	1.2B	Q4_K_M	1.5 GB	~43.2 tokens/s	✓ Runs great
S	Llama 3.2 3B	3.2B	Q4_K_M	2.9 GB	~17.3 tokens/s	✓ Runs great
S	Gemma 3 1B	1B	Q4_K_M	1.5 GB	~43.2 tokens/s	✓ Runs great
S	DeepSeek R1 Distill 1.5B	1.8B	Q4_K_M	1.9 GB	~31.4 tokens/s	✓ Runs great
S	SmolLM2 1.7B	1.7B	Q4_K_M	1.9 GB	~31.4 tokens/s	✓ Runs great
S	SmolLM3 3B	3.1B	Q4_K_M	2.8 GB	~18.2 tokens/s	✓ Runs great
S	Qwen 3.5 2B	2B	Q4_K_M	2.1 GB	~26.6 tokens/s	✓ Runs great
S	Ternary Bonsai 8B	8B	PQ2_0	3.5 GB	~15.7 tokens/s	✓ Runs great
S	Ternary Bonsai 4B	4B	PQ2_0	2 GB	~31.4 tokens/s	✓ Runs great
S	Ternary Bonsai 1.7B	1.7B	PQ2_0	1.2 GB	~69.1 tokens/s	✓ Runs great
S	Ministral 3 3B	3B	Q4_K_M	3 GB	~16.5 tokens/s	✓ Runs great
S	LFM2.5 8B-A1B	8B	Q4_K_M	6.6 GB	~53.2 tokens/s	✓ Runs great
S	OvisOCR2 0.8B	0.8B	Q4_K_M	1.3 GB	~57.6 tokens/s	✓ Runs great
S	Tini Cybersec 8B-A1B	8.5B	Q4_K_M	6.7 GB	~56.5 tokens/s	✓ Runs great
S	Qwen3 4B	4B	Q4_K_M	3.5 GB	~13.8 tokens/s	✓ Runs great
S	Gemma 3 4B	4.3B	Q4_K_M	3.5 GB	~13.8 tokens/s	✓ Runs great
S	Phi-4 Mini 3.8B	3.8B	Q4_K_M	3.5 GB	~13.8 tokens/s	✓ Runs great
S	Qwen 3.5 4B	4B	Q4_K_M	3.7 GB	~12.8 tokens/s	✓ Runs great
S	Agents-A1 4B	4B	Q4_K_M	3.7 GB	~12.8 tokens/s	✓ Runs great
S	Nemotron 3 Nano 4B	4B	Q4_K_M	3.8 GB	~12.3 tokens/s	✓ Runs great
S	Fara 1.5 4B	4.5B	Q4_K_M	4 GB	~11.9 tokens/s	✓ Runs great
S	Gemma 4 E2B	2B	Q4_K_M	4 GB	~11.1 tokens/s	✓ Runs great
A	Bonsai 27B (1-bit)	27B	Q1_0	6.5 GB	~9.1 tokens/s	✓ Runs great
A	Mistral 7B v0.3	7.2B	Q4_K_M	5.7 GB	~7.9 tokens/s	! Runs, barely
A	DeepSeek R1 Distill 7B	7.6B	Q4_K_M	6.1 GB	~7.4 tokens/s	! Runs, barely
A	Llama 3.1 8B	8B	Q4_K_M	6.3 GB	~7.1 tokens/s	! Runs, barely
A	Ministral 8B	8B	Q4_K_M	6.3 GB	~7.1 tokens/s	! Runs, barely
A	Qwen3 8B	8.2B	Q4_K_M	6.4 GB	~6.9 tokens/s	! Runs, barely
A	Gemma 4 E4B	4B	Q4_K_M	6.1 GB	~6.9 tokens/s	! Runs, barely
A	Ministral 3 8B	8B	Q4_K_M	6.6 GB	~6.6 tokens/s	! Runs, barely
A	Ornith 1.0 9B	9B	Q4_K_M	7.1 GB	~6.2 tokens/s	! Runs, barely
A	Qwen 3.5 9B	9B	Q4_K_M	7.2 GB	~6.1 tokens/s	! Runs, barely
A	Qwythos 9B v2	9B	Q4_K_M	7.2 GB	~6.1 tokens/s	! Runs, barely
A	grug 9B (ProCreations)	9.4B	Q4_K_M	7.5 GB	~5.9 tokens/s	! Runs, barely
A	Fara 1.5 9B	9.4B	Q4_K_M	7.5 GB	~5.9 tokens/s	! Runs, barely
B	Gemma 4 12B	12B	Q4_0	8.8 GB	~4.9 tokens/s	! Runs, barely
F	Gemma 3 12B	12.2B	Q4_K_M	9.1 GB	—	✕ Won't fit
F	Grug 12B	12B	Q4_K_M	9.5 GB	—	✕ Won't fit
F	Ternary Bonsai 27B	27B	PQ2_0	10.1 GB	—	✕ Won't fit
F	Ministral 3 14B	14B	Q4_K_M	10.2 GB	—	✕ Won't fit
F	Qwen3 14B	14.8B	Q4_K_M	11.1 GB	—	✕ Won't fit
F	Phi-4 14B	14.7B	Q4_K_M	11.2 GB	—	✕ Won't fit
F	GPT-OSS 20B	21B	MXFP4	14.8 GB	—	✕ Won't fit
F	Gemma 4 26B-A4B	26B	Q4_0	17.5 GB	—	✕ Won't fit
F	Qwen3 30B A3B	30.5B	Q4_K_M	22.3 GB	—	✕ Won't fit
F	Qwen 3.5 27B	27B	Q4_K_M	20 GB	—	✕ Won't fit
F	Qwen 3.5 35B-A3B	35B	Q4_K_M	26.2 GB	—	✕ Won't fit
F	Qwen 3.6 27B	27B	Q4_K_M	18.5 GB	—	✕ Won't fit
F	Qwen 3.6 35B-A3B	35B	Q4_K_M	23.9 GB	—	✕ Won't fit
F	Nemotron 3 Nano 30B-A3B	30B	Q4_K_M	28.5 GB	—	✕ Won't fit
F	Ornith 1.0 35B-A3B	35B	Q4_K_M	25.3 GB	—	✕ Won't fit
F	Salience 1.5 Flash	30B	Q4_K_M	22.3 GB	—	✕ Won't fit
F	Fara 1.5 27B	27B	Q4_K_M	20.9 GB	—	✕ Won't fit
F	KAT-Coder V2.5 Dev	35B	Q4_K_M	25.5 GB	—	✕ Won't fit
F	Llama 3.3 70B	70B	Q4_K_M	50.1 GB	—	✕ Won't fit
F	Qwen3 32B	32.8B	Q4_K_M	23.7 GB	—	✕ Won't fit
F	Ornith 1.0 397B	397B	Q4_K_M	285.6 GB	—	✕ Won't fit
F	Gemma 4 31B	31B	Q4_K_M	22 GB	—	✕ Won't fit
F	Hunyuan 3 (Hy3)	298.8B	Q4_K_M	212.8 GB	—	✕ Won't fit
F	Inkling	952.4B	Q8_0	966.9 GB	—	✕ Won't fit
F	Laguna XS 2.1	33B	Q4_K_M	24.2 GB	—	✕ Won't fit
F	Laguna S 2.1	118B	Q4_K_M	80.5 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 — ~57.6 tokens/s at Q8_0, using 1.3 of ~9GB.

Best for Coding

Tini Cybersec 8B-A1B

Top code completion & explain-this pick here — ~56.5 tokens/s at Q4_K_M, using 6.7 of ~9GB.

Best for Reasoning

DeepSeek R1 Distill 1.5B

Top math & step-by-step thinking pick here — ~31.4 tokens/s at Q4_K_M, using 1.9 of ~9GB.

FAQ

What is the biggest AI model the vivo X300 E can run?

Bonsai 27B (1-bit) (27B parameters) at Q1_0 — it needs 6.5GB of the ~9GB usable on the 12GB vivo X300 E and runs at ~9.1 tokens/s.

How much of the vivo X300 E's 12GB RAM can AI models actually use?

About 9GB. Android keeps roughly 2–4GB for the system and resident apps, so of the 12GB about 9GB is actually available to a model.

Can the vivo X300 E run Llama 3.1 8B?

Yes — at Q4_K_M it needs 6.3GB of the ~9GB usable and runs at ~7.1 tokens/s.

How fast is local AI on the vivo X300 E?

The Snapdragon 8 Gen 5 has 76.8GB/s of memory bandwidth, which is what decode speed scales with. Small models like Ternary Bonsai 1.7B reach ~69.1 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 X300 E?

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 12GB of RAM.

Is 12GB of RAM enough for local AI?

38 of the 64 models we track fit on the vivo X300 E — 25 run great and 13 run with compromises. 26 models (mostly 12B+) don't fit at their recommended quant.

Other vivo phones

vivo X200 Pro vivo X100 Pro vivo X300 Pro vivo X Fold3 vivo X Fold3 Pro vivo X Fold5 vivo X Fold6