AI Models for vivo S2 — What runs on 8GB
Specs checked against manufacturer and public documentation on . India.
What runs on the vivo S2
All 89 models at their recommended quant, on the 8GB configuration. Select any row for the full report.
| Model | Params | Quant | Needs | Speed | Verdict |
|---|---|---|---|---|---|
| Ternary Bonsai 1.7B | 1.7B | Q2_0 | 1.2 GB | ~15.4 tokens/s | ✓ Runs great |
| Qwen3 0.6B | 0.6B | Q8_0 | 1.3 GB | ~12.8 tokens/s | ✓ Runs great |
| OvisOCR2 0.8B | 0.8B | Q4_K_M | 1.3 GB | ~12.8 tokens/s | ✓ Runs great |
| Llama 3.2 1B | 1.2B | Q4_K_M | 1.5 GB | ~9.6 tokens/s | ✓ Runs great |
| Gemma 3 1B | 1B | Q4_K_M | 1.5 GB | ~9.6 tokens/s | ✓ Runs great |
| DeepSeek R1 Distill 1.5B | 1.8B | Q4_K_M | 1.9 GB | ~7 tokens/s | ! Runs, barely |
| SmolLM2 1.7B | 1.7B | Q4_K_M | 1.9 GB | ~7 tokens/s | ! Runs, barely |
| Ternary Bonsai 4B | 4B | Q2_0 | 2 GB | ~7 tokens/s | ! Runs, barely |
| Qwen 3.5 2B | 2B | Q4_K_M | 2.1 GB | ~5.9 tokens/s | ! Runs, barely |
| LFM2.5 2.6B | 2.7B | Q4_K_M | 2.6 GB | ~4.5 tokens/s | ! Runs, barely |
| LFM2.5-VL 3B | 3.1B | Q4_K_M | 2.6 GB | ~4.5 tokens/s | ! Runs, barely |
| Qwen3 1.7B | 1.7B | Q8_0 | 2.6 GB | ~4.3 tokens/s | ! Runs, barely |
| SmolLM3 3B | 3.1B | Q4_K_M | 2.8 GB | ~4.1 tokens/s | ! Runs, barely |
| G9v3 3B | 3B | Q4_K_M | 2.8 GB | ~4.1 tokens/s | ! Runs, barely |
| Llama 3.2 3B | 3.2B | Q4_K_M | 2.9 GB | ~3.8 tokens/s | ! Runs, barely |
| Ministral 3 3B | 3B | Q4_K_M | 3 GB | ~3.7 tokens/s | ! Runs, barely |
| Ternary Bonsai 8B | 8B | Q2_0 | 3.5 GB | ~3.5 tokens/s | ! Runs, barely |
| Granite 4.2 3B | 3B | Q4_K_M | 3.1 GB | ~3.5 tokens/s | ! Runs, barely |
| Qwen3 4B | 4B | Q4_K_M | 3.5 GB | ~3.1 tokens/s | ! Runs, barely |
| Gemma 3 4B | 4.3B | Q4_K_M | 3.5 GB | ~3.1 tokens/s | ! Runs, barely |
| Phi-4 Mini 3.8B | 3.8B | Q4_K_M | 3.5 GB | ~3.1 tokens/s | ! Runs, barely |
| Qwen 3.5 4B | 4B | Q4_K_M | 3.7 GB | ~2.9 tokens/s | ! Runs, barely |
| Agents-A1 4B | 4B | Q4_K_M | 3.7 GB | ~2.9 tokens/s | ! Runs, barely |
| Nanbeige 4.2 3B | 4.2B | Q4_K_M | 3.7 GB | ~2.9 tokens/s | ! Runs, barely |
| Gemma 4 E2B | 2B | Q4_K_M | 4 GB | ~2.5 tokens/s | ! Runs, barely |
| Nemotron 3 Nano 4B | 4B | Q4_K_M | 3.8 GB | ~2.7 tokens/s | ! Runs, barely |
| AREX Turbo 4B | 4.5B | Q4_K_M | 4 GB | ~2.7 tokens/s | ! Runs, barely |
| Fara 1.5 4B | 4.5B | Q4_K_M | 4 GB | ~2.7 tokens/s | ! Runs, barely |
| Bonsai 27B (1-bit) | 27B | Q1_0 | 4.8 GB | ~2 tokens/s | ! Runs, barely |
| Mistral 7B v0.3 | 7.2B | Q4_K_M | 5.7 GB | ~1.7 tokens/s | ! Runs, barely |
| DeepSeek R1 Distill 7B | 7.6B | Q4_K_M | 6.1 GB | — | ✕ Won't fit |
| Gemma 4 E4B | 4B | Q4_K_M | 6.1 GB | — | ✕ Won't fit |
| Ling 3.0 Tiny | 7.9B | Q4_K_M | 6.3 GB | — | ✕ Won't fit |
| Qwen3 8B | 8.2B | Q4_K_M | 6.4 GB | — | ✕ Won't fit |
| Llama 3.1 8B | 8B | Q4_K_M | 6.3 GB | — | ✕ Won't fit |
| Ministral 8B | 8B | Q4_K_M | 6.3 GB | — | ✕ Won't fit |
| Ministral 3 8B | 8B | Q4_K_M | 6.6 GB | — | ✕ Won't fit |
| LFM2.5 8B-A1B | 8B | Q4_K_M | 6.6 GB | — | ✕ Won't fit |
| Tini Cybersec 8B-A1B | 8.5B | Q4_K_M | 6.7 GB | — | ✕ Won't fit |
| Granite 4.2 8B | 8B | Q4_K_M | 6.7 GB | — | ✕ Won't fit |
| Qwen 3.5 9B | 9B | Q4_K_M | 7.2 GB | — | ✕ Won't fit |
| Ornith 1.0 9B | 9B | Q4_K_M | 7.1 GB | — | ✕ Won't fit |
| Qwythos 9B v2 | 9B | Q4_K_M | 7.2 GB | — | ✕ Won't fit |
| GRM 3.2 Cliff 9B | 9.4B | Q4_K_M | 7.5 GB | — | ✕ Won't fit |
| grug 9B (ProCreations) | 9.4B | Q4_K_M | 7.5 GB | — | ✕ Won't fit |
| Fara 1.5 9B | 9.4B | Q4_K_M | 7.5 GB | — | ✕ Won't fit |
| UI-Mate 9B | 9B | Q4_K_M | 7.4 GB | — | ✕ Won't fit |
| Ternary Bonsai 27B | 27B | Q2_0 | 8.4 GB | — | ✕ Won't fit |
| Gemma 4 12B | 12B | Q4_0 | 8.8 GB | — | ✕ Won't fit |
| 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 |
| 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 |
| Gemma 4 26B-A4B | 26B | Q4_0 | 17.5 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 |
| GPT-OSS 20B | 21B | MXFP4 | 14.8 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 |
| Ling 3.0 Flash | 124B | Q4_K_M | 82.3 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
Top everyday assistant & writing pick here — ~15.4 tokens/s at Q2_0, using 1.2 of ~6GB.
Top code completion & explain-this pick here — ~4.1 tokens/s at Q4_K_M, using 2.8 of ~6GB.
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 vivo S2 can run?
Bonsai 27B (1-bit) (27B parameters) at Q1_0 — it needs 4.8GB of the ~6GB usable on the 8GB vivo S2, at ~2 tokens/s.
How much of the vivo S2'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 vivo S2 run Llama 3.1 8B?
Not at Q4_K_M: it needs 6.3GB but the vivo S2 only has ~6GB usable. Try a smaller model like Ternary Bonsai 1.7B.
How fast is local AI on the vivo S2?
The Dimensity 7360-Turbo 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 vivo S2?
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 89 models we track fit on the vivo S2 — 5 run great and 25 run with compromises. 59 models (mostly 12B+) don't fit at their recommended quant.