text model · Llama 3.2 Vision · Android
Can I run Llama 3.2 Vision 11B on Samsung Galaxy S25 Ultra (16GB, 1TB config only)?
Yes. Llama 3.2 Vision 11B runs on Samsung Galaxy S25 Ultra (16GB, 1TB config only) at Q4_K_M (~9 GB of ~12 GB usable).
Runs at Q4_K_M using ~9 GB of ~12 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. Samsung Galaxy S25 Ultra (16GB, 1TB config only) leaves ~3 GB of headroom.
- Q4_K_M needed
- ~9 GB
- Usable on device
- ~12 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~8 W
- Electricity / 1M tokens
- ~$0.06
- Pays for itself after
- ~3,680M tok
At ~$0.15/kWh and the estimated ~6 tok/s, a million generated tokens costs about $0.06 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,619 Samsung Galaxy S25 Ultra (16GB, 1TB config only) pays for itself after roughly 3,680 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.).
- Parameters
- 10.7B
- Q4_K_M size
- 7.36 GB
- Q8_0 size
- 11.49 GB
- Context
- 128k
- Ollama tag
- llama3.2-vision:11b
- Memory
- 16 GB ram
- Usable for weights
- ~12 GB
- Power draw
- ~8 W
- Best runtime
- llama.cpp (PocketPal) or MLC-LLM (Adreno GPU path)
You could also run
Run Llama 3.2 Vision 11B on other hardware
FAQ
Can Samsung Galaxy S25 Ultra (16GB, 1TB config only) run Llama 3.2 Vision 11B?
Yes. Llama 3.2 Vision 11B runs on Samsung Galaxy S25 Ultra (16GB, 1TB config only) at Q4_K_M (~9 GB of ~12 GB usable).
How much memory does Llama 3.2 Vision 11B need?
Samsung Galaxy S25 Ultra (16GB, 1TB config only) has room to spare. At Q4_K_M the weights are ~7.36 GB; with KV cache and runtime overhead, budget ~9 GB at a 4k context.
What is the best tool to run Llama 3.2 Vision 11B 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/llama-3.2-vision-11b/samsung-s25-ultra-16gb) Sources
- gadgetversus.com
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- gsmarena.com/samsung_galaxy_s25_ultra-12750.php
- gsmarena.com/samsung_galaxy_s25_ultra-13322.php
- huggingface.co/leafspark
- huggingface.co/meta-llama
- layla-network.ai
- ollama.com/library/llama3.2-vision
- ollama.com/library/llama3.2-vision/tags
- sammyfans.com
- sammyguru.com
- samsung.com
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.