audio model · whisper · iOS
Can I run Whisper large-v3 on iPhone 16?
Yes. Whisper large-v3 runs on iPhone 16 at int8 (~2.5 GB of ~4.5 GB usable).
Runs at int8 using ~2.5 GB of ~4.5 GB usable.
- Peak memory
- ~2.5 GB
- Usable on device
- ~4.5 GB
- Device memory
- 8 GB
- Quant
- int8
How to run it
Use whisper.cpp or faster-whisper at int8. It is light enough to run on CPU; a GPU just makes it faster.
- Type
- Speech to text
- Parameters
- 1.55B
- Peak memory
- ~2.5 GB at int8
- License
- MIT
- Memory
- 8 GB unified
- Usable for weights
- ~4.5 GB
- Power draw
- ~11 W
- Best runtime
- llama.cpp + Metal (via PocketPal or Off Grid app)
You could also run
Run Whisper large-v3 on other hardware
FAQ
Can iPhone 16 run Whisper large-v3?
Yes. Whisper large-v3 runs on iPhone 16 at int8 (~2.5 GB of ~4.5 GB usable).
How much memory does Whisper large-v3 need?
iPhone 16 has room to spare. At int8 the realistic peak is ~2.5 GB of memory.
What do I use to run Whisper large-v3 locally?
Whisper large-v3 runs in whisper.cpp or faster-whisper (among others). It runs on CPU, so no GPU is required.
Sources
- apple.com
- developer.apple.com
- en.wikipedia.org/wiki/Apple_A18
- en.wikipedia.org/wiki/IPhone_16
- enclaveai.app
- github.com/a-ghorbani
- github.com/ggml-org
- github.com/google-ai-edge
- github.com/mainframecomputer
- github.com/openai
- github.com/SYSTRAN
- gsmarena.com
- huggingface.co
- layla-network.ai
- macrumors.com
- privatellm.app
VRAM figures are sourced peak-usage anchors at the noted quant, validated 2026-08-03. See methodology.