AI Models for Redmi Note 17 — What runs on 8GB

5 great · 18 slow · 41 won't fit

Chip

Snapdragon 4 Gen 4

Memory bandwidth

17.1 GB/s

NPU

—

RAM options

6 / 8 GB

Usable for models

~6 GB

Year

2026

Specs checked against manufacturer and public documentation on 2026-07-22. China variant.

What runs on the Redmi Note 17

All 64 models at their recommended quant, on the 8GB configuration. Tap a model for the full report.

	Model	Params	Quant	Needs	Speed	Verdict
S	Ternary Bonsai 1.7B	1.7B	PQ2_0	1.2 GB	~15.4 tokens/s	✓ Runs great
S	Qwen3 0.6B	0.6B	Q8_0	1.3 GB	~12.8 tokens/s	✓ Runs great
S	OvisOCR2 0.8B	0.8B	Q4_K_M	1.3 GB	~12.8 tokens/s	✓ Runs great
S	Llama 3.2 1B	1.2B	Q4_K_M	1.5 GB	~9.6 tokens/s	✓ Runs great
S	Gemma 3 1B	1B	Q4_K_M	1.5 GB	~9.6 tokens/s	✓ Runs great
A	DeepSeek R1 Distill 1.5B	1.8B	Q4_K_M	1.9 GB	~7 tokens/s	! Runs, barely
A	SmolLM2 1.7B	1.7B	Q4_K_M	1.9 GB	~7 tokens/s	! Runs, barely
A	Ternary Bonsai 4B	4B	PQ2_0	2 GB	~7 tokens/s	! Runs, barely
A	Qwen 3.5 2B	2B	Q4_K_M	2.1 GB	~5.9 tokens/s	! Runs, barely
A	Qwen3 1.7B	1.7B	Q8_0	2.6 GB	~4.3 tokens/s	! Runs, barely
A	SmolLM3 3B	3.1B	Q4_K_M	2.8 GB	~4.1 tokens/s	! Runs, barely
A	Llama 3.2 3B	3.2B	Q4_K_M	2.9 GB	~3.8 tokens/s	! Runs, barely
A	Ministral 3 3B	3B	Q4_K_M	3 GB	~3.7 tokens/s	! Runs, barely
B	Ternary Bonsai 8B	8B	PQ2_0	3.5 GB	~3.5 tokens/s	! Runs, barely
B	Qwen3 4B	4B	Q4_K_M	3.5 GB	~3.1 tokens/s	! Runs, barely
B	Gemma 3 4B	4.3B	Q4_K_M	3.5 GB	~3.1 tokens/s	! Runs, barely
B	Phi-4 Mini 3.8B	3.8B	Q4_K_M	3.5 GB	~3.1 tokens/s	! Runs, barely
B	Qwen 3.5 4B	4B	Q4_K_M	3.7 GB	~2.9 tokens/s	! Runs, barely
B	Agents-A1 4B	4B	Q4_K_M	3.7 GB	~2.9 tokens/s	! Runs, barely
B	Gemma 4 E2B	2B	Q4_K_M	4 GB	~2.5 tokens/s	! Runs, barely
B	Nemotron 3 Nano 4B	4B	Q4_K_M	3.8 GB	~2.7 tokens/s	! Runs, barely
B	Fara 1.5 4B	4.5B	Q4_K_M	4 GB	~2.7 tokens/s	! Runs, barely
C	Mistral 7B v0.3	7.2B	Q4_K_M	5.7 GB	~1.7 tokens/s	! Runs, barely
F	DeepSeek R1 Distill 7B	7.6B	Q4_K_M	6.1 GB	—	✕ Won't fit
F	Gemma 4 E4B	4B	Q4_K_M	6.1 GB	—	✕ Won't fit
F	Qwen3 8B	8.2B	Q4_K_M	6.4 GB	—	✕ Won't fit
F	Llama 3.1 8B	8B	Q4_K_M	6.3 GB	—	✕ Won't fit
F	Ministral 8B	8B	Q4_K_M	6.3 GB	—	✕ Won't fit
F	Bonsai 27B (1-bit)	27B	Q1_0	6.5 GB	—	✕ Won't fit
F	Ministral 3 8B	8B	Q4_K_M	6.6 GB	—	✕ Won't fit
F	LFM2.5 8B-A1B	8B	Q4_K_M	6.6 GB	—	✕ Won't fit
F	Tini Cybersec 8B-A1B	8.5B	Q4_K_M	6.7 GB	—	✕ Won't fit
F	Qwen 3.5 9B	9B	Q4_K_M	7.2 GB	—	✕ Won't fit
F	Ornith 1.0 9B	9B	Q4_K_M	7.1 GB	—	✕ Won't fit
F	Qwythos 9B v2	9B	Q4_K_M	7.2 GB	—	✕ Won't fit
F	grug 9B (ProCreations)	9.4B	Q4_K_M	7.5 GB	—	✕ Won't fit
F	Fara 1.5 9B	9.4B	Q4_K_M	7.5 GB	—	✕ Won't fit
F	Gemma 4 12B	12B	Q4_0	8.8 GB	—	✕ Won't fit
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	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	Gemma 4 26B-A4B	26B	Q4_0	17.5 GB	—	✕ Won't fit
F	Nemotron 3 Nano 30B-A3B	30B	Q4_K_M	28.5 GB	—	✕ Won't fit
F	GPT-OSS 20B	21B	MXFP4	14.8 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

Ternary Bonsai 1.7B

Top everyday assistant & writing pick here — ~15.4 tokens/s at PQ2_0, using 1.2 of ~6GB.

Best for Coding

Qwen3 4B

Top code completion & explain-this pick here — ~3.1 tokens/s at Q4_K_M, using 3.5 of ~6GB.

Best for Reasoning

DeepSeek R1 Distill 1.5B

Top math & step-by-step thinking pick here — ~7 tokens/s at Q4_K_M, using 1.9 of ~6GB.

FAQ

What is the biggest AI model the Redmi Note 17 can run?

Ternary Bonsai 8B (8B parameters) at PQ2_0 — it needs 3.5GB of the ~6GB usable on the 8GB Redmi Note 17, at ~3.5 tokens/s.

How much of the Redmi Note 17's 8GB RAM can AI models actually use?

About 6GB. Android keeps roughly 2–4GB for the system and resident apps, so of the 8GB about 6GB is actually available to a model.

Can the Redmi Note 17 run Llama 3.1 8B?

Not at Q4_K_M: it needs 6.3GB but the Redmi Note 17 only has ~6GB usable. Try a smaller model like Ternary Bonsai 1.7B.

How fast is local AI on the Redmi Note 17?

The Snapdragon 4 Gen 4 has 17.1GB/s of memory bandwidth, which is what decode speed scales with. Small models like Ternary Bonsai 1.7B reach ~15.4 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 17?

Q4_K_M is the size/quality sweet spot for most models. For example, Ternary Bonsai 1.7B at PQ2_0 takes 1.2GB of memory here. Only drop to Q3 or IQ4 if a model just misses fitting; Q8 rarely pays off on 8GB of RAM.

Is 8GB of RAM enough for local AI?

23 of the 64 models we track fit on the Redmi Note 17 — 5 run great and 18 run with compromises. 41 models (mostly 12B+) don't fit at their recommended quant.

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