AI Models for Redmi Note 15 Pro 5G — What runs on 12GB

12 great · 37 slow · 44 won't fit

Chip

Dimensity 7400 Ultra

Memory bandwidth

25.6 GB/s

NPU

—

RAM options

8 / 12 GB

Usable for models

~9 GB

Year

2026

Specs checked against manufacturer and public documentation on 2026-07-17.

What runs on the Redmi Note 15 Pro 5G

All 93 models at their recommended quant, on the 12GB configuration. Select any row for the full report.

Model	Params	Quant	Needs	Speed	Verdict
Qwen3 0.6B	0.6B	Q8_0	1.3 GB	~19.2 tokens/s	✓ Runs great
Ternary Bonsai 1.7B	1.7B	Q2_0	1.2 GB	~23 tokens/s	✓ Runs great
LFM2.5 8B-A1B	8B	Q4_K_M	6.6 GB	~17.7 tokens/s	✓ Runs great
OvisOCR2 0.8B	0.8B	Q4_K_M	1.3 GB	~19.2 tokens/s	✓ Runs great
Tini Cybersec 8B-A1B	8.5B	Q4_K_M	6.7 GB	~18.8 tokens/s	✓ Runs great
Llama 3.2 1B	1.2B	Q4_K_M	1.5 GB	~14.4 tokens/s	✓ Runs great
Gemma 3 1B	1B	Q4_K_M	1.5 GB	~14.4 tokens/s	✓ Runs great
Ling 3.0 Tiny	7.9B	Q4_K_M	6.3 GB	~13.3 tokens/s	✓ Runs great
DeepSeek R1 Distill 1.5B	1.8B	Q4_K_M	1.9 GB	~10.5 tokens/s	✓ Runs great
SmolLM2 1.7B	1.7B	Q4_K_M	1.9 GB	~10.5 tokens/s	✓ Runs great
Ternary Bonsai 4B	4B	Q2_0	2 GB	~10.5 tokens/s	✓ Runs great
Qwen 3.5 2B	2B	Q4_K_M	2.1 GB	~8.9 tokens/s	✓ Runs great
LFM2.5 2.6B	2.7B	Q4_K_M	2.6 GB	~6.8 tokens/s	! Runs, barely
LFM2.5-VL 3B	3.1B	Q4_K_M	2.6 GB	~6.8 tokens/s	! Runs, barely
Qwen3 1.7B	1.7B	Q8_0	2.6 GB	~6.4 tokens/s	! Runs, barely
SmolLM3 3B	3.1B	Q4_K_M	2.8 GB	~6.1 tokens/s	! Runs, barely
G9v3 3B	3B	Q4_K_M	2.8 GB	~6.1 tokens/s	! Runs, barely
Llama 3.2 3B	3.2B	Q4_K_M	2.9 GB	~5.8 tokens/s	! Runs, barely
Ministral 3 3B	3B	Q4_K_M	3 GB	~5.5 tokens/s	! Runs, barely
Ternary Bonsai 8B	8B	Q2_0	3.5 GB	~5.2 tokens/s	! Runs, barely
Granite 4.2 3B	3B	Q4_K_M	3.1 GB	~5.2 tokens/s	! Runs, barely
Qwen3 4B	4B	Q4_K_M	3.5 GB	~4.6 tokens/s	! Runs, barely
Gemma 3 4B	4.3B	Q4_K_M	3.5 GB	~4.6 tokens/s	! Runs, barely
Phi-4 Mini 3.8B	3.8B	Q4_K_M	3.5 GB	~4.6 tokens/s	! Runs, barely
Qwen 3.5 4B	4B	Q4_K_M	3.7 GB	~4.3 tokens/s	! Runs, barely
Nemotron 3 Nano 4B	4B	Q4_K_M	3.8 GB	~4.1 tokens/s	! Runs, barely
Agents-A1 4B	4B	Q4_K_M	3.7 GB	~4.3 tokens/s	! Runs, barely
Nanbeige 4.2 3B	4.2B	Q4_K_M	3.7 GB	~4.3 tokens/s	! Runs, barely
AREX Turbo 4B	4.5B	Q4_K_M	4 GB	~4 tokens/s	! Runs, barely
Fara 1.5 4B	4.5B	Q4_K_M	4 GB	~4 tokens/s	! Runs, barely
Gemma 4 E2B	2B	Q4_K_M	4 GB	~3.7 tokens/s	! Runs, barely
Bonsai 27B (1-bit)	27B	Q1_0	4.8 GB	~3 tokens/s	! Runs, barely
Mistral 7B v0.3	7.2B	Q4_K_M	5.7 GB	~2.6 tokens/s	! Runs, barely
DeepSeek R1 Distill 7B	7.6B	Q4_K_M	6.1 GB	~2.5 tokens/s	! Runs, barely
Qwen3 8B	8.2B	Q4_K_M	6.4 GB	~2.3 tokens/s	! Runs, barely
Llama 3.1 8B	8B	Q4_K_M	6.3 GB	~2.4 tokens/s	! Runs, barely
Ministral 8B	8B	Q4_K_M	6.3 GB	~2.4 tokens/s	! Runs, barely
Gemma 4 E4B	4B	Q4_K_M	6.1 GB	~2.3 tokens/s	! Runs, barely
Ministral 3 8B	8B	Q4_K_M	6.6 GB	~2.2 tokens/s	! Runs, barely
Granite 4.2 8B	8B	Q4_K_M	6.7 GB	~2.2 tokens/s	! Runs, barely
Ornith 1.0 9B	9B	Q4_K_M	7.1 GB	~2.1 tokens/s	! Runs, barely
Qwen 3.5 9B	9B	Q4_K_M	7.2 GB	~2 tokens/s	! Runs, barely
Qwythos 9B v2	9B	Q4_K_M	7.2 GB	~2 tokens/s	! Runs, barely
GRM 3.2 Cliff 9B	9.4B	Q4_K_M	7.5 GB	~2 tokens/s	! Runs, barely
grug 9B (ProCreations)	9.4B	Q4_K_M	7.5 GB	~2 tokens/s	! Runs, barely
Fara 1.5 9B	9.4B	Q4_K_M	7.5 GB	~2 tokens/s	! Runs, barely
UI-Mate 9B	9B	Q4_K_M	7.4 GB	~2 tokens/s	! Runs, barely
Ternary Bonsai 27B	27B	Q2_0	8.4 GB	~1.6 tokens/s	! Runs, barely
Gemma 4 12B	12B	Q4_0	8.8 GB	~1.6 tokens/s	! Runs, barely
Gemma 3 12B	12.2B	Q4_K_M	9.1 GB	—	✕ Won't fit
Grug 12B	12B	Q4_K_M	9.5 GB	—	✕ Won't fit
Ministral 3 14B	14B	Q4_K_M	10.2 GB	—	✕ Won't fit
Qwen3 14B	14.8B	Q4_K_M	11.1 GB	—	✕ Won't fit
Phi-4 14B	14.7B	Q4_K_M	11.2 GB	—	✕ Won't fit
GPT-OSS 20B	21B	MXFP4	14.8 GB	—	✕ Won't fit
Gemma 4 26B-A4B	26B	Q4_0	17.5 GB	—	✕ Won't fit
Qwen3 30B A3B	30.5B	Q4_K_M	22.3 GB	—	✕ Won't fit
Qwen 3.5 27B	27B	Q4_K_M	20 GB	—	✕ Won't fit
Qwen 3.5 35B-A3B	35B	Q4_K_M	26.2 GB	—	✕ Won't fit
Qwen 3.6 27B	27B	Q4_K_M	18.5 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
Salience 1.5 Flash	30B	Q4_K_M	22.3 GB	—	✕ Won't fit
Fara 1.5 27B	27B	Q4_K_M	20.9 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
Muse Glimmer 30B	29.6B	K_QUANT_17GB	20.3 GB	—	✕ Won't fit
Qwen3.8 27B	27B	Q4_K_M	20.8 GB	—	✕ Won't fit
Qwen3.8 2.4T-A95B	2400B	IQ4_XS	1377.4 GB	—	✕ Won't fit
UI-Mate 27B	27B	Q4_K_M	20.9 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
Granite 4.2 30B	30B	Q4_K_M	21.3 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
Qwen3 32B	32.8B	Q4_K_M	23.7 GB	—	✕ Won't fit
Ornith 1.0 397B	397B	Q4_K_M	285.6 GB	—	✕ Won't fit
Gemma 4 31B	31B	Q4_K_M	22 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

Best for Chat

Qwen3 0.6B

Top everyday assistant & writing pick here — ~19.2 tokens/s at Q8_0, using 1.3 of ~9GB.

Best for Coding

Tini Cybersec 8B-A1B

Top code completion & explain-this pick here — ~18.8 tokens/s at Q4_K_M, using 6.7 of ~9GB.

Best for Reasoning

Tini Cybersec 8B-A1B

Top math & step-by-step thinking pick here — ~18.8 tokens/s at Q4_K_M, using 6.7 of ~9GB.

FAQ

What is the biggest AI model the Redmi Note 15 Pro 5G can run?

Bonsai 27B (1-bit) (27B parameters) at Q1_0 — it needs 4.8GB of the ~9GB usable on the 12GB Redmi Note 15 Pro 5G, at ~3 tokens/s.

How much of the Redmi Note 15 Pro 5G'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 Redmi Note 15 Pro 5G run Llama 3.1 8B?

Yes — at Q4_K_M it needs 6.3GB of the ~9GB usable and runs at ~2.4 tokens/s.

How fast is local AI on the Redmi Note 15 Pro 5G?

The Dimensity 7400 Ultra has 25.6GB/s of memory bandwidth, which is what decode speed scales with. Small models like Ternary Bonsai 1.7B reach ~23 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 Redmi Note 15 Pro 5G?

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?

49 of the 93 models we track fit on the Redmi Note 15 Pro 5G — 12 run great and 37 run with compromises. 44 models (mostly 12B+) don't fit at their recommended quant.

Other Xiaomi phones

Xiaomi 15 Pro Xiaomi 14 Redmi Note 13 Pro POCO X6 Pro Xiaomi 17 Xiaomi 17 Pro Redmi Note 14 Pro 5G Redmi Note 15 5G Redmi Note 14 Pro+ 5G Redmi Note 15 Pro+ 5G Xiaomi 15T Xiaomi 15T Pro POCO X7 Pro Xiaomi MIX Fold 2 Xiaomi MIX Fold 3 Xiaomi MIX Fold 4 Xiaomi MIX Flip Xiaomi MIX Flip 2 Redmi Note 17