n27/ gemma-4-26B-A4B-it-UD-Q4_K_M-32k

1,894 2 months ago

Best local model for standard desktop setups, suitable for coding and agent use.

tools thinking 
ollama run n27/gemma-4-26B-A4B-it-UD-Q4_K_M-32k

Applications

Claude Code
Claude Code ollama launch claude --model n27/gemma-4-26B-A4B-it-UD-Q4_K_M-32k
Codex App
Codex App ollama launch codex-app --model n27/gemma-4-26B-A4B-it-UD-Q4_K_M-32k
OpenClaw
OpenClaw ollama launch openclaw --model n27/gemma-4-26B-A4B-it-UD-Q4_K_M-32k
Hermes Agent
Hermes Agent ollama launch hermes --model n27/gemma-4-26B-A4B-it-UD-Q4_K_M-32k
Codex
Codex ollama launch codex --model n27/gemma-4-26B-A4B-it-UD-Q4_K_M-32k
OpenCode
OpenCode ollama launch opencode --model n27/gemma-4-26B-A4B-it-UD-Q4_K_M-32k

Models

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1 model

gemma-4-26B-A4B-it-UD-Q4_K_M-32k:latest

17GB · 256K context window · Text · 2 months ago

Readme

model.png

🧠 n27/gemma-4-26B-A4B-it-UD-Q4_K_M-32k

Best local model for standard desktop setups, suitable for coding and agent use.

📦 Overview

gemma-4-26B-A4B-it-UD-Q4_K_M-32k is a quantized, instruction-tuned large language model optimized for local inference on consumer hardware.

This variant is pre-configured with a 32K context window for Ollama, ensuring stable performance on standard desktop setups.

It offers an excellent balance between: - ⚡ Performance - 🧠 Reasoning capability - 💻 Code generation - 🤖 Agent workflows

🚀 Features

  • 🧩 Instruction-tuned (IT) – ready for chat and task execution
  • 💻 Strong coding abilities – works well with dev tools and agents
  • 🧠 Good reasoning performance for its size
  • 🪶 Quantized (Q4_K_M) – optimized for desktop GPUs / RAM
  • 📏 Context pre-configured to 32K (Ollama)
  • 🔧 Compatible with multiple local AI toolchains

📊 Model Details

Property Value
Model Name gemma-4-26B-A4B-it-UD-Q4_K_M-32k
Size ~17 GB
Context Length 32K (configured)
Base Capability Up to 256K (model-dependent)
Input Type Text
Quantization Q4_K_M

🖥️ Hardware Requirements

Minimum (will run, but limited performance)

  • GPU: 4 GB VRAM
  • RAM: 16 GB
  • ⚠️ Expect slow generation and possible limitations on long context

Recommended (comfortable usage)

  • GPU: 16 GB VRAM
  • RAM: 32 GB
  • ✅ Good balance between speed and stability

Optimal (best experience)

  • GPU: 24 GB VRAM
  • RAM: 32 GB+
  • 🚀 Smooth performance, better handling of long context and agents

⚙️ Usage

🖥️ Run with Ollama

ollama run n27/gemma-4-26B-A4B-it-UD-Q4_K_M-32k

🧪 Recommended Use Cases

  • 💻 Local coding assistant
  • 🤖 Autonomous agents / tool use
  • 📄 Document analysis (medium to long context)
  • 🧠 Reasoning-heavy tasks
  • 🛠️ Developer workflows

💡 Notes

  • Context is intentionally limited to 32K for better stability and memory usage in Ollama
  • The underlying model may support larger context, but this build is optimized for real-world desktop usage
  • Works best with GPU acceleration, but can run on CPU with reduced performance!