aicanrun

AI Models for iQOO Z11 Lite — What runs on 8GB

5 great · 25 slow · 65 won't fit
Chip
Dimensity 6300
Memory bandwidth
17.1 GB/s
NPU
—
RAM options
4 / 6 / 8 GB
Usable for models
~6 GB
Year
2026

Specs checked against manufacturer and public documentation on . India offers 4/6GB RAM; Russia offers up to 8GB.

What runs on the iQOO Z11 Lite

All 95 models at their recommended quant, on the 8GB configuration. Select any row for the full report.

ModelParamsQuantNeedsSpeedVerdict
Ternary Bonsai 1.7B1.7BQ2_01.2 GB~15.4 tokens/s✓ Runs great
Qwen3 0.6B0.6BQ8_01.3 GB~12.8 tokens/s✓ Runs great
OvisOCR2 0.8B0.8BQ4_K_M1.3 GB~12.8 tokens/s✓ Runs great
Llama 3.2 1B1.2BQ4_K_M1.5 GB~9.6 tokens/s✓ Runs great
Gemma 3 1B1BQ4_K_M1.5 GB~9.6 tokens/s✓ Runs great
DeepSeek R1 Distill 1.5B1.8BQ4_K_M1.9 GB~7 tokens/s! Runs, barely
SmolLM2 1.7B1.7BQ4_K_M1.9 GB~7 tokens/s! Runs, barely
Ternary Bonsai 4B4BQ2_02 GB~7 tokens/s! Runs, barely
Qwen 3.5 2B2BQ4_K_M2.1 GB~5.9 tokens/s! Runs, barely
LFM2.5 2.6B2.7BQ4_K_M2.6 GB~4.5 tokens/s! Runs, barely
LFM2.5-VL 3B3.1BQ4_K_M2.6 GB~4.5 tokens/s! Runs, barely
Qwen3 1.7B1.7BQ8_02.6 GB~4.3 tokens/s! Runs, barely
SmolLM3 3B3.1BQ4_K_M2.8 GB~4.1 tokens/s! Runs, barely
G9v3 3B3BQ4_K_M2.8 GB~4.1 tokens/s! Runs, barely
Llama 3.2 3B3.2BQ4_K_M2.9 GB~3.8 tokens/s! Runs, barely
Ministral 3 3B3BQ4_K_M3 GB~3.7 tokens/s! Runs, barely
Ternary Bonsai 8B8BQ2_03.5 GB~3.5 tokens/s! Runs, barely
Granite 4.2 3B3BQ4_K_M3.1 GB~3.5 tokens/s! Runs, barely
Qwen3 4B4BQ4_K_M3.5 GB~3.1 tokens/s! Runs, barely
Gemma 3 4B4.3BQ4_K_M3.5 GB~3.1 tokens/s! Runs, barely
Phi-4 Mini 3.8B3.8BQ4_K_M3.5 GB~3.1 tokens/s! Runs, barely
Qwen 3.5 4B4BQ4_K_M3.7 GB~2.9 tokens/s! Runs, barely
Agents-A1 4B4BQ4_K_M3.7 GB~2.9 tokens/s! Runs, barely
Nanbeige 4.2 3B4.2BQ4_K_M3.7 GB~2.9 tokens/s! Runs, barely
Gemma 4 E2B2BQ4_K_M4 GB~2.5 tokens/s! Runs, barely
Nemotron 3 Nano 4B4BQ4_K_M3.8 GB~2.7 tokens/s! Runs, barely
AREX Turbo 4B4.5BQ4_K_M4 GB~2.7 tokens/s! Runs, barely
Fara 1.5 4B4.5BQ4_K_M4 GB~2.7 tokens/s! Runs, barely
Bonsai 27B (1-bit)27BQ1_04.8 GB~2 tokens/s! Runs, barely
Mistral 7B v0.37.2BQ4_K_M5.7 GB~1.7 tokens/s! Runs, barely
DeepSeek R1 Distill 7B7.6BQ4_K_M6.1 GB—✕ Won't fit
Gemma 4 E4B4BQ4_K_M6.1 GB—✕ Won't fit
Ling 3.0 Tiny7.9BQ4_K_M6.3 GB—✕ Won't fit
Qwen3 8B8.2BQ4_K_M6.4 GB—✕ Won't fit
Llama 3.1 8B8BQ4_K_M6.3 GB—✕ Won't fit
Ministral 8B8BQ4_K_M6.3 GB—✕ Won't fit
Ministral 3 8B8BQ4_K_M6.6 GB—✕ Won't fit
LFM2.5 8B-A1B8BQ4_K_M6.6 GB—✕ Won't fit
Tini Cybersec 8B-A1B8.5BQ4_K_M6.7 GB—✕ Won't fit
Granite 4.2 8B8BQ4_K_M6.7 GB—✕ Won't fit
Qwen 3.5 9B9BQ4_K_M7.2 GB—✕ Won't fit
Ornith 1.0 9B9BQ4_K_M7.1 GB—✕ Won't fit
Qwythos 9B v29BQ4_K_M7.2 GB—✕ Won't fit
GRM 3.2 Cliff 9B9.4BQ4_K_M7.5 GB—✕ Won't fit
grug 9B (ProCreations)9.4BQ4_K_M7.5 GB—✕ Won't fit
Fara 1.5 9B9.4BQ4_K_M7.5 GB—✕ Won't fit
UI-Mate 9B9BQ4_K_M7.4 GB—✕ Won't fit
Ternary Bonsai 27B27BQ2_08.4 GB—✕ Won't fit
Gemma 4 12B12BQ4_08.8 GB—✕ Won't fit
Gemma 3 12B12.2BQ4_K_M9.1 GB—✕ Won't fit
Grug 12B12BQ4_K_M9.5 GB—✕ Won't fit
Ministral 3 14B14BQ4_K_M10.2 GB—✕ Won't fit
Qwen3 14B14.8BQ4_K_M11.1 GB—✕ Won't fit
Phi-4 14B14.7BQ4_K_M11.2 GB—✕ Won't fit
Qwen3 30B A3B30.5BQ4_K_M22.3 GB—✕ Won't fit
Qwen 3.5 27B27BQ4_K_M20 GB—✕ Won't fit
Qwen 3.5 35B-A3B35BQ4_K_M26.2 GB—✕ Won't fit
Qwen 3.6 27B27BQ4_K_M18.5 GB—✕ Won't fit
Qwen 3.6 35B-A3B35BQ4_K_M23.9 GB—✕ Won't fit
Gemma 4 26B-A4B26BQ4_017.5 GB—✕ Won't fit
Nemotron 3 Nano 30B-A3B30BQ4_K_M28.5 GB—✕ Won't fit
Nemotron 3 Nano Omni 30B-A3B31BQ4_K_M26.3 GB—✕ Won't fit
Nemotron 3.5 Lightning 30B-A3B30BQ8_035.9 GB—✕ Won't fit
GPT-OSS 20B21BMXFP414.8 GB—✕ Won't fit
Ornith 1.0 35B-A3B35BQ4_K_M25.3 GB—✕ Won't fit
XYZ-Aquila mini 35B-A3B35BQ4_K_M25.5 GB—✕ Won't fit
Salience 1.5 Flash30BQ4_K_M22.3 GB—✕ Won't fit
Fara 1.5 27B27BQ4_K_M20.9 GB—✕ Won't fit
KAT-Coder V2.5 Dev35BQ4_K_M25.5 GB—✕ Won't fit
BigBang V1 36B-A3B36BQ4_K_M26.1 GB—✕ Won't fit
Muse Glimmer 30B29.6BK_QUANT_17GB20.3 GB—✕ Won't fit
Qwen3.8 27B27BQ4_K_M20.8 GB—✕ Won't fit
Qwen3.8 2.4T-A95B2400BIQ4_XS1377.4 GB—✕ Won't fit
UI-Mate 27B27BQ4_K_M20.9 GB—✕ Won't fit
GLM-4.5V106BQ4_K_M74.8 GB—✕ Won't fit
GLM-4.5-Air106BQ4_K_M74.8 GB—✕ Won't fit
Granite 4.2 30B30BQ4_K_M21.3 GB—✕ Won't fit
Apodex 1.1 Mini35BQ4_K_M26 GB—✕ Won't fit
Ling 3.0 Flash124BQ4_K_M82.3 GB—✕ Won't fit
Ling 3.0 Flash Fin124BQ4_K_M91 GB—✕ Won't fit
Qwen3.8-Flash-Next125BIQ4_XS107.7 GB—✕ Won't fit
GLM-5.3744BQ4_K_XL543.3 GB—✕ Won't fit
GLM-5.3-Flash320BQ4_K_XL232.7 GB—✕ Won't fit
Llama 3.3 70B70BQ4_K_M50.1 GB—✕ Won't fit
Qwen3 32B32.8BQ4_K_M23.7 GB—✕ Won't fit
Ornith 1.0 397B397BQ4_K_M285.6 GB—✕ Won't fit
Gemma 4 31B31BQ4_K_M22 GB—✕ Won't fit
Hunyuan 3 (Hy3)298.8BQ4_K_M212.8 GB—✕ Won't fit
DeepSeek V4 Flash 0731284BQ4_K_XL163.6 GB—✕ Won't fit
DeepSeek V4 Flash Vision Exp284BQ4_K_XL183.3 GB—✕ Won't fit
Inkling952.4BQ8_0966.9 GB—✕ Won't fit
Inkling Small276BQ4_K_M190.5 GB—✕ Won't fit
LongCat Flash Chat561.9BIQ1_S159.6 GB—✕ Won't fit
Laguna XS 2.133BQ4_K_M24.2 GB—✕ Won't fit
Laguna S 2.1118BQ4_K_M109.7 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 Q2_0, using 1.2 of ~6GB.

Best for Coding
G9v3 3B

Top code completion & explain-this pick here — ~4.1 tokens/s at Q4_K_M, using 2.8 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 iQOO Z11 Lite can run?

Bonsai 27B (1-bit) (27B parameters) at Q1_0 — it needs 4.8GB of the ~6GB usable on the 8GB iQOO Z11 Lite, at ~2 tokens/s.

How much of the iQOO Z11 Lite'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 iQOO Z11 Lite run Llama 3.1 8B?

Not at Q4_K_M: it needs 6.3GB but the iQOO Z11 Lite only has ~6GB usable. Try a smaller model like Ternary Bonsai 1.7B.

How fast is local AI on the iQOO Z11 Lite?

The Dimensity 6300 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 iQOO Z11 Lite?

Q4_K_M is the size/quality sweet spot for most models. For example, Ternary Bonsai 1.7B at Q2_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?

30 of the 95 models we track fit on the iQOO Z11 Lite — 5 run great and 25 run with compromises. 65 models (mostly 12B+) don't fit at their recommended quant.