text model · Kimi · iOS
Can I run Kimi K2.7 Code on iPad Pro M4 (16GB, 1TB/2TB config)?
No. Kimi K2.7 Code needs ~592.1 GB even at Q4_K_M, but iPad Pro M4 (16GB, 1TB/2TB config) only has ~12 GB usable.
Needs ~592.1 GB even at Q4_K_M, but only ~12 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 580.1 GB: Kimi K2.7 Code needs roughly 592.1 GB at Q4_K_M and iPad Pro M4 (16GB, 1TB/2TB config) leaves only about 12 GB usable for a model. No single tracked device has enough memory; Kimi K2.7 Code needs a multi-GPU or high-memory rig.
- Q4_K_M needed
- ~592.1 GB
- Usable on device
- ~12 GB
- Device memory
- 16 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
- 1000B (MoE, 32B active)
- Q4_K_M size
- 583.71 GB
- Context
- 256k
- Memory
- 16 GB unified
- Usable for weights
- ~12 GB
- Power draw
- ~14 W
- Best runtime
- MLX (via Python or Swift; mlx-lm package)
What you can run instead
FAQ
Can iPad Pro M4 (16GB, 1TB/2TB config) run Kimi K2.7 Code?
No. Kimi K2.7 Code needs ~592.1 GB even at Q4_K_M, but iPad Pro M4 (16GB, 1TB/2TB config) only has ~12 GB usable.
How much memory does Kimi K2.7 Code need?
iPad Pro M4 (16GB, 1TB/2TB config) does not have enough memory. At Q4_K_M the weights are ~583.71 GB; with KV cache and runtime overhead, budget ~592.1 GB at a 4k context. It is a Mixture-of-Experts model (1000B total / 32B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Kimi K2.7 Code 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/kimi-k2.7-code/ipad-pro-m4-16gb) Sources
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apple.com · 2 sources
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developer.apple.com · 1 source
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enclaveai.app · 1 source
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github.com · 3 sources
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gsmarena.com · 1 source
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huggingface.co · 2 sources
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layla-network.ai · 1 source
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phonearena.com · 1 source
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privatellm.app · 1 source
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support.apple.com · 2 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.