AI Models for Pixel 8a — What runs on 8GB
Specs checked against manufacturer and public documentation on .
What runs on the Pixel 8a
All 48 models at their recommended quant, on the 8GB 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 | ~51.2 tokens/s | ✓ Runs great |
| S | Qwen3 1.7B | 1.7B | Q8_0 | 2.6 GB | ~17.1 tokens/s | ✓ Runs great |
| S | Llama 3.2 1B | 1.2B | Q4_K_M | 1.5 GB | ~38.4 tokens/s | ✓ Runs great |
| S | Llama 3.2 3B | 3.2B | Q4_K_M | 2.9 GB | ~15.4 tokens/s | ✓ Runs great |
| S | Gemma 3 1B | 1B | Q4_K_M | 1.5 GB | ~38.4 tokens/s | ✓ Runs great |
| S | DeepSeek R1 Distill 1.5B | 1.8B | Q4_K_M | 1.9 GB | ~27.9 tokens/s | ✓ Runs great |
| S | SmolLM2 1.7B | 1.7B | Q4_K_M | 1.9 GB | ~27.9 tokens/s | ✓ Runs great |
| S | SmolLM3 3B | 3.1B | Q4_K_M | 2.8 GB | ~16.2 tokens/s | ✓ Runs great |
| S | Qwen 3.5 2B | 2B | Q4_K_M | 2.1 GB | ~23.6 tokens/s | ✓ Runs great |
| S | Ternary Bonsai 4B | 4B | PQ2_0 | 2 GB | ~27.9 tokens/s | ✓ Runs great |
| S | Ternary Bonsai 1.7B | 1.7B | PQ2_0 | 1.2 GB | ~61.5 tokens/s | ✓ Runs great |
| S | Ministral 3 3B | 3B | Q4_K_M | 3 GB | ~14.6 tokens/s | ✓ Runs great |
| S | Ternary Bonsai 8B | 8B | PQ2_0 | 3.5 GB | ~14 tokens/s | ✓ Runs great |
| S | Qwen3 4B | 4B | Q4_K_M | 3.5 GB | ~12.3 tokens/s | ✓ Runs great |
| S | Gemma 3 4B | 4.3B | Q4_K_M | 3.5 GB | ~12.3 tokens/s | ✓ Runs great |
| S | Phi-4 Mini 3.8B | 3.8B | Q4_K_M | 3.5 GB | ~12.3 tokens/s | ✓ Runs great |
| S | Qwen 3.5 4B | 4B | Q4_K_M | 3.7 GB | ~11.4 tokens/s | ✓ Runs great |
| S | Nemotron 3 Nano 4B | 4B | Q4_K_M | 3.8 GB | ~11 tokens/s | ✓ Runs great |
| S | Gemma 4 E2B | 2B | Q4_K_M | 4 GB | ~9.9 tokens/s | ✓ Runs great |
| B | Mistral 7B v0.3 | 7.2B | Q4_K_M | 5.7 GB | ~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 | Qwen 3.5 9B | 9B | Q4_K_M | 7.2 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 | 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 | 20.1 GB | — | ✕ Won't fit |
| F | Qwen 3.6 35B-A3B | 35B | Q4_K_M | 26.3 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 | 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 | 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 |
~ = bandwidth-based estimate · ✓ = measured on real hardware
Best model by use case
Top everyday assistant & writing pick here — ~51.2 tokens/s at Q8_0, using 1.3 of ~6GB.
Top code completion & explain-this pick here — ~12.3 tokens/s at Q4_K_M, using 3.5 of ~6GB.
Top math & step-by-step thinking pick here — ~27.9 tokens/s at Q4_K_M, using 1.9 of ~6GB.
FAQ
What is the biggest AI model the Pixel 8a can run?
Ternary Bonsai 8B (8B parameters) at PQ2_0 — it needs 3.5GB of the ~6GB usable on the 8GB Pixel 8a and runs at ~14 tokens/s.
How much of the Pixel 8a'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 Pixel 8a run Llama 3.1 8B?
Not at Q4_K_M: it needs 6.3GB but the Pixel 8a only has ~6GB usable. Try a smaller model like Qwen3 0.6B.
How fast is local AI on the Pixel 8a?
The Google Tensor G3 has 68.3GB/s of memory bandwidth, which is what decode speed scales with. Small models like Ternary Bonsai 1.7B reach ~61.5 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 Pixel 8a?
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 8GB of RAM.
Is 8GB of RAM enough for local AI?
20 of the 48 models we track fit on the Pixel 8a — 19 run great and 1 run with compromises. 28 models (mostly 12B+) don't fit at their recommended quant.