text model · GLM · Android
Can I run GLM-4.6 on Generic Android Phone (8GB RAM)?
No. GLM-4.6 needs ~221.3 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
Needs ~221.3 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 216.8 GB: GLM-4.6 needs roughly 221.3 GB at Q4_K_M and Generic Android Phone (8GB RAM) leaves only about 4.5 GB usable for a model. No single tracked device has enough memory; GLM-4.6 needs a multi-GPU or high-memory rig.
- Q4_K_M needed
- ~221.3 GB
- Usable on device
- ~4.5 GB
- Device memory
- 8 GB
Which quant fits
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.).
- Parameters
- 357B (MoE, 32B active)
- Q4_K_M size
- 216 GB
- Q8_0 size
- 379 GB
- Context
- 200k
- Memory
- 8 GB ram
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp (PocketPal or SmolChat)
What you can run instead
FAQ
Can Generic Android Phone (8GB RAM) run GLM-4.6?
No. GLM-4.6 needs ~221.3 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
How much memory does GLM-4.6 need?
Generic Android Phone (8GB RAM) does not have enough memory. At Q4_K_M the weights are ~216 GB; with KV cache and runtime overhead, budget ~221.3 GB at a 4k context. It is a Mixture-of-Experts model (357B total / 32B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run GLM-4.6 on Android?
On Android, PocketPal AI (Polished app, download GGUF and run offline.) is the go-to option. NPU acceleration is limited and chip-specific; most apps run on CPU. Expect 1B-4B class.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/glm-4.6/android-generic-8gb) 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.