text model · Llama · iOS
Can I run Llama 3.3 70B on iPhone 17?
No. Llama 3.3 70B needs ~45.3 GB even at Q4_K_M, but iPhone 17 only has ~4.5 GB usable.
Needs ~45.3 GB even at Q4_K_M, but only ~4.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 40.8 GB: Llama 3.3 70B needs roughly 45.3 GB at Q4_K_M and iPhone 17 leaves only about 4.5 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
- ~4.5 GB
- Device memory
- 8 GB
Which quant fits
How to run it
On iOS use Apple Foundation Models (Built into iOS 26, ~3B on-device model, zero download, fully private.).
- Parameters
- 70B
- Q4_K_M size
- 42.52 GB
- Q8_0 size
- 74.98 GB
- Context
- 128k
- Ollama tag
- llama3.3:70b
- Memory
- 8 GB unified
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp + Metal (via PocketPal or Off Grid app)
What you can run instead
Run Llama 3.3 70B on other hardware
FAQ
Can iPhone 17 run Llama 3.3 70B?
No. Llama 3.3 70B needs ~45.3 GB even at Q4_K_M, but iPhone 17 only has ~4.5 GB usable.
How much memory does Llama 3.3 70B need?
iPhone 17 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 iOS?
On iPhone and iPad, Apple Foundation Models (Built into iOS 26, ~3B on-device model, zero download, fully private.) is the standard choice. Phones realistically run 1B-4B class models. Anything larger thermally throttles or OOMs.
Embed this
[](https://localmodel.run/can-i-run/llama-3.3-70b/iphone-17) Sources
- apple.com
- developer.apple.com
- en.wikipedia.org/wiki/Apple_A19
- en.wikipedia.org/wiki/IPhone_17
- enclaveai.app
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mainframecomputer
- gorilla.cs.berkeley.edu
- huggingface.co
- layla-network.ai
- lmarena.ai
- macrumors.com
- ollama.com/library/llama3.3
- ollama.com/library/llama3.3/tags
- privatellm.app
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.