text model · Llama · Android
Can I run Llama 3.3 70B on Generic Android Phone (12GB RAM)?
No. Llama 3.3 70B needs ~45.3 GB even at Q4_K_M, but Generic Android Phone (12GB RAM) only has ~8.5 GB usable.
Needs ~45.3 GB even at Q4_K_M, but only ~8.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 36.8 GB: Llama 3.3 70B needs roughly 45.3 GB at Q4_K_M and Generic Android Phone (12GB RAM) leaves only about 8.5 GB usable for a model. The lightest tracked hardware that runs Llama 3.3 70B is the Apple M4 Max (64GB) at 64 GB. See Llama 3.3 70B on Apple M4 Max (64GB).
- Q4_K_M needed
- ~45.3 GB
- Usable on device
- ~8.5 GB
- Device memory
- 12 GB
Which quant fits
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.).
- Parameters
- 70B
- Q4_K_M size
- 42.52 GB
- Q8_0 size
- 74.98 GB
- Context
- 128k
- Ollama tag
- llama3.3:70b
- Memory
- 12 GB ram
- Usable for weights
- ~8.5 GB
- Best runtime
- llama.cpp (PocketPal) or MLC-LLM
What you can run instead
Run Llama 3.3 70B on other hardware
FAQ
Can Generic Android Phone (12GB RAM) run Llama 3.3 70B?
No. Llama 3.3 70B needs ~45.3 GB even at Q4_K_M, but Generic Android Phone (12GB RAM) only has ~8.5 GB usable.
How much memory does Llama 3.3 70B need?
Generic Android Phone (12GB RAM) does not have enough memory. At Q4_K_M the weights are ~42.52 GB; with KV cache and runtime overhead, budget ~45.3 GB at a 4k context.
What is the best tool to run Llama 3.3 70B on Android?
On Android, PocketPal AI (Polished app, download GGUF and run offline.) is the go-to option. NPU acceleration is limited and chip-specific; most apps run on CPU. Expect 1B-4B class.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/llama-3.3-70b/android-generic-12gb) Sources
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github.com · 5 sources
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gorilla.cs.berkeley.edu · 1 source
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huggingface.co · 2 sources
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layla-network.ai · 1 source
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lmarena.ai · 1 source
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ollama.com · 3 sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.