LocalClaw/LLM/Gemma 4 26B A4B
Open-weight local LLM

Gemma 4 26B A4B

Gemma 4 MoE flagship-for-workstations: 26B total with ~4B active parameters. 256K context and excellent quality-per-watt for local inference. Apache 2.0.

32 GB power user 24 GB RAM Q4_K_M Advanced assistant
Run with LocalClaw Compare all models
Parameters
26B (A4B active)
Minimum RAM
24 GB
Model size
16 GB
Quantization
Q4_K_M

Can Gemma 4 26B A4B run locally?

Gemma 4 26B A4B belongs on 32 GB machines when you want stronger quality without jumping to server hardware.

Search for gemma-4-26b-a4b-it in LM Studio or another GGUF-compatible runtime.

google/gemma-4-26B-A4B-it
chatcodereasoningpowermultimodalgeneral

Install path

01
Check RAM fitMinimum 24 GB RAM. Start with the Q4_K_M quant.
02
Load the modelSearch gemma-4-26b-a4b-it in LM Studio.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Strengths

  • Excellent quality-per-watt
  • Large-model quality with reduced active compute
  • 256K context
  • Strong coding and reasoning

Limitations

  • Needs workstation-class RAM/VRAM for comfortable local inference

Best use cases

  • Advanced assistant
  • Agent workflows
  • Coding support
  • Research and analysis

Capability profile

speed
7
quality
9
coding
8
reasoning
9

Technical notes

Developer
Google DeepMind
License
Apache 2.0
Context window
262,144 tokens
Architecture
Mixture-of-Experts style Gemma 4 (26B total, ~4B active)

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.

Comfortable headroomMac mini M4 Pro 48GBMobile workstationMacBook Pro M4 Max 36GBPower-user picks32GB RAM guide

Similar models to compare

Gemma 4 12B 12BGemma 4 31B 31BQwen 3.5 (27B) 27BQwen 3.5 MoE (35B/3B active) 35B (3B active)

Where to go next