LocalClaw/LLM/Gemma 3n (8B)
Open-weight local LLM

Gemma 3n (8B)

Google on-device powerhouse with vision. Designed for phones/tablets/laptops but punches far above its weight. Per-layer memory management for constrained devices. Apache 2.0.

Laptop ready 8 GB RAM Q4_K_M On-device AI assistant
Run with LocalClaw Compare all models
Parameters
8B
Minimum RAM
8 GB
Model size
5 GB
Quantization
Q4_K_M

Can Gemma 3n (8B) run locally?

Gemma 3n (8B) is a good fit for normal laptops and compact desktops with 8 GB RAM or more.

Search for gemma-3n-e8b-it in LM Studio or another GGUF-compatible runtime.

google/gemma-3n-E8B-it-GGUF
chatvisionstandardgeneral

Install path

01
Check RAM fitMinimum 8 GB RAM. Start with the Q4_K_M quant.
02
Load the modelSearch gemma-3n-e8b-it in LM Studio.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Strengths

  • Built-in vision capabilities
  • Optimized for on-device deployment
  • Per-layer memory management for constrained devices
  • Strong quality-to-size ratio
  • Runs on phones, tablets, and laptops

Limitations

  • Gemma license restrictions
  • Not the best for server-side deployment
  • Vision capabilities less powerful than dedicated VLMs

Best use cases

  • On-device AI assistant
  • Mobile vision apps
  • Edge computing
  • Multimodal chat on laptops
  • Embedded AI systems

Capability profile

speed
7
quality
7
coding
6
reasoning
7

Technical notes

Developer
Google DeepMind
License
Gemma License
Context window
32,768 tokens
Architecture
Transformer (decoder-only) with per-layer memory management for constrained devices

This model fits these next steps

Hardware fit is based on LocalClaw's RAM tier, model size and quantization metadata. Always leave memory headroom for your OS and runtime.

Entry laptop fitMacBook Air 8GBMore headroomMac mini M4 16GBAll compatible picks8GB RAM guide

Similar models to compare

Gemma 3 (12B) 12BGemma 3n (4B) 4BLlama 3.2 Vision (11B) 11BQwen 2.5 VL (7B) 7B

Where to go next