AINewsnow

Research: Gemma 4 E2B on a Pixel 8 Pro (Tensor G3) with llama.cpp

I'm building an app that runs a local model on-device, and spent a day measuring what actually helps on a Pixel 8 Pro. Posting the numbers in case they save someone else the time. Setup Phone: Pixel 8 Pro, Tensor G3. 9 cores: 4 small at 1.7 GHz (0–3), 4 medium at 2.37 GHz (4–7), 1 large at 2.91 GHz…

Read the full story at r/LocalLLM ↗

Timeline · 1 report

  1. 2026-10-14 01:04 · r/LocalLLM
    Research: Gemma 4 E2B on a Pixel 8 Pro (Tensor G3) with llama.cpp

More stories

  1. I clustered two old Android phones to run a 27B LLM locally. Bypassed RAM limits via C++ RPC split and Hadamard Ternary math (No overheating) — r/LocalLLM
  2. Qwen 27B at ~20 tok/s on a jailbroken PS5's GPU. I forked PS5LM and turned it into a native app — r/LocalLLM
  3. Why AnythingLLM is moving from Ollama to Lemonade and llama.cpp — r/LocalLLaMA
  4. Quartermaster 1.7 update now lets you run llama.cpp's new decision models (/v1/systemone) with a playground tab — r/LocalLLaMA
  5. An 80B MoE (Qwen3-Next, 48 GB GGUF) answering in a Chrome tab: llama.cpp's WebGPU backend, experts paged in from disk — r/LocalLLaMA
  6. Models keeps unloading from GPU — r/LocalLLM
  7. SonicBoom: 52 tok/s decode on a laptop RTX 3050 (llama.cpp: 45) with the same quantized kernels, the difference is a graph compiler. No Python. — r/LocalLLaMA
  8. train the best model you can on your mac in one hour — r/learnmachinelearning

Get the daily brief of stories like this at 6:30 every morning →