text model · Sarvam · iOS
Can I run Sarvam-105B on iPad Pro M4 (16GB, 1TB/2TB config)?
No. Sarvam-105B needs ~67.5 GB even at Q4_K_M, but iPad Pro M4 (16GB, 1TB/2TB config) only has ~12 GB usable.
Needs ~67.5 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 55.5 GB: Sarvam-105B needs roughly 67.5 GB at Q4_K_M and iPad Pro M4 (16GB, 1TB/2TB config) leaves only about 12 GB usable for a model. The lightest tracked hardware that runs Sarvam-105B is the Apple M4 Max (128GB) at 128 GB. See Sarvam-105B on Apple M4 Max (128GB).
- Q4_K_M needed
- ~67.5 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
- 105B (MoE, 10.3B active)
- Q4_K_M size
- 64.2 GB
- Context
- 128k
- 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
Run Sarvam-105B on other hardware
FAQ
Can iPad Pro M4 (16GB, 1TB/2TB config) run Sarvam-105B?
No. Sarvam-105B needs ~67.5 GB even at Q4_K_M, but iPad Pro M4 (16GB, 1TB/2TB config) only has ~12 GB usable.
How much memory does Sarvam-105B need?
iPad Pro M4 (16GB, 1TB/2TB config) does not have enough memory. At Q4_K_M the weights are ~64.2 GB; with KV cache and runtime overhead, budget ~67.5 GB at a 4k context. It is a Mixture-of-Experts model (105B total / 10.3B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Sarvam-105B 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.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/sarvam-105b/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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sarvam.ai · 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.