Can GRM 3.2 Cliff 9B run on Redmi Note 14 Pro 5G?
YES — Runs, barely
Formula estimate
What this means
Our estimate says GRM 3.2 Cliff 9B should fit, but replies are likely to arrive slowly.
Download the Q4_K_M version, which is about 5.9 GB. We expect it to use about 7.5 GB of the roughly 9 GB available to a model on this phone.
At the estimated speed, a roughly 300-word answer may take about 3 minutes to finish.
This result is calculated from the phone and model specifications. It has not been measured on this exact phone and setup.
2tokens/s
estimated · slower than you read
7.5GB
needed at Q4_K_M
Q4_K_M
quant checked
needs 7.5 GBusable 9 GB
Dimensity 730012 GB RAMCPU / GPUAlso sold with 8 GB
Make it bearable
1
Drop context to 2K — saves ~0.4 GB of KV cache and a little speed.
2
Close every other app before loading — the 1.5 GB headroom is real; Android may kill the app otherwise.
3
Expect throttling after ~10 min of sustained generation on a phone chassis.
See how fast it feels
Estimated at 2 tokens/s — slower than you read. A ~300-word reply takes about 200 seconds on this phone.
Where the memory goes
| Component | Detail | GB |
|---|---|---|
| Model weights | Q4_K_M GGUF (5.9 GB) + mmap overhead | 6.2 |
| KV cache | 4K context window | 0.7 |
| Runtime | llama.cpp + app overhead | 0.6 |
| Total needed | at Q4_K_M, 4K context | 7.5 |
| Working budget | 12 GB RAM − Android system reserve | 9 |
| Headroom | remaining inside the working budget | 1.5 |
Pick your quant
| Quant | Download | Verdict | Speed |
|---|---|---|---|
| Q2_K | 4.1 GB | ! Runs, barely | ~2.8 tokens/s |
| Q3_K_M | 4.9 GB | ! Runs, barely | ~2.4 tokens/s |
| IQ4_XS | 5.2 GB | ! Runs, barely | ~2.2 tokens/s |
| Q4_0 | 5.5 GB | ! Runs, barely | ~2.1 tokens/s |
| Q4_K_S | 5.6 GB | ! Runs, barely | ~2.1 tokens/s |
| Q4_K_M BEST HERE | 5.9 GB | ! Runs, barely | ~2 tokens/s |
| Q5_K_M | 6.9 GB | ! Runs, barely | ~1.7 tokens/s |
| Q6_K | 7.7 GB | ✕ Won't fit | won't fit |
| Q8_0 | 9.5 GB | ✕ Won't fit | won't fit |
Get your first offline chat working
Recommended app: PocketPal. Follow the point-and-click steps below. The speed above is an estimate, not a measurement from this exact app and phone.
1
Install or update PocketPal from Google Play. It is free and does not require an account. Use a current version so its loader supports newer model architectures.
2
Open the exact model. In PocketPal, go to Models → + → Add from Hugging Face, then paste
bartowski/OrionLLM_GRM-3.2-Cliff-GGUF.3
Choose the Q4_K_M GGUF file. The download is about 5.9 GB, so use Wi-Fi and keep the app open. Choose the main GGUF weights, not a vision projector, mmproj, or other helper file.
4
Tap Download, then Load. Start with a 4K (4096-token) context. Keep the app’s default Android backend for the first run.
5
Send a simple first prompt. Try “Explain why the sky is blue in three sentences.” This page estimates about 2 tokens/s, but that number is not a PocketPal measurement unless it carries a ✓ Verified label.
6
Confirm it is really offline. After the first reply, turn on airplane mode and ask a second question. If it still answers, the model is running on your phone.
If it does not work
- Model not listed: update PocketPal and paste the exact repository
bartowski/OrionLLM_GRM-3.2-Cliff-GGUF. - App closes while loading: close other apps, restart the phone, and try 2K context. If it still closes, choose a smaller model.
- No offline reply: confirm that the Q4_K_M GGUF file is loaded in the chat rather than a remote model.
Related checks
More on Redmi Note 14 Pro 5G
GRM 3.2 Cliff 9B on other phones