text model · Mistral · iOS
Can I run Mixtral 8x7B on iPhone 15 Pro?
No. Mixtral 8x7B needs ~28.9 GB even at Q4_K_M, but iPhone 15 Pro only has ~4.5 GB usable.
Needs ~28.9 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 24.4 GB: Mixtral 8x7B needs roughly 28.9 GB at Q4_K_M and iPhone 15 Pro leaves only about 4.5 GB usable for a model. The lightest tracked hardware that runs Mixtral 8x7B is the Nvidia GeForce RTX 5090 (32GB) at 32 GB. See Mixtral 8x7B on Nvidia GeForce RTX 5090 (32GB).
- Q4_K_M needed
- ~28.9 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
- 46.7B (MoE, 12.9B active)
- Q4_K_M size
- 26.49 GB
- Q8_0 size
- 46.22 GB
- Context
- 32k
- Ollama tag
- mixtral:8x7b
- Memory
- 8 GB unified
- Usable for weights
- ~4.5 GB
- Power draw
- ~14 W
- Best runtime
- llama.cpp + Metal (via PocketPal or Off Grid app)
What you can run instead
Run Mixtral 8x7B on other hardware
FAQ
Can iPhone 15 Pro run Mixtral 8x7B?
No. Mixtral 8x7B needs ~28.9 GB even at Q4_K_M, but iPhone 15 Pro only has ~4.5 GB usable.
How much memory does Mixtral 8x7B need?
iPhone 15 Pro does not have enough memory. At Q4_K_M the weights are ~26.49 GB; with KV cache and runtime overhead, budget ~28.9 GB at a 4k context. It is a Mixture-of-Experts model (46.7B total / 12.9B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Mixtral 8x7B 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
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/mixtral-8x7b/iphone-15-pro) Sources
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cpu-monkey.com · 1 source
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developer.apple.com · 1 source
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enclaveai.app · 1 source
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forums.macrumors.com · 1 source
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github.com · 3 sources
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gsmarena.com · 1 source
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helloexpress.net · 1 source
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huggingface.co · 2 sources
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layla-network.ai · 1 source
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ollama.com · 2 sources
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privatellm.app · 1 source
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support.apple.com · 1 source
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techinsights.com · 1 source
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wccftech.com · 1 source
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.