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Gemma 4

Gemma 4

Released March 31, 2026 by Google DeepMind. Apache 2.0 licensed. Multimodal (text + image, audio on small models).

Model Sizes

Model Type Effective Params Context Modalities
E2B Dense 2.3B (5.1B w/ embeddings) 128K Text, Image, Audio
E4B Dense 4.5B (8B w/ embeddings) 128K Text, Image, Audio
26B A4B MoE 3.8B active / 25.2B total 256K Text, Image
31B Dense 30.7B 256K Text, Image

The 26B A4B is the standout — a MoE model that runs almost as fast as a 4B model despite 26B total params. The E2B/E4B use Per-Layer Embeddings for on-device efficiency.

Local Running Options

  1. Ollama — ollama run google/gemma-4 (all sizes). Easiest one-command setup.
  2. llama.cpp — GGUF quantized versions available on Hugging Face. Good for CPU/GPU hybrid inference.
  3. vLLM — For higher-throughput server deployment. Supports the native HF safetensor weights.
  4. LM Studio — GUI-based, supports GGUF formats. Good for desktop use.
  5. Hugging Face Transformers — Direct Python API. Full precision or QLoRA fine-tuning.

Hardware Requirements (rough)

  • E2B (2.3B eff.) — Runs on phones, any modern laptop (4-8 GB RAM)
  • E4B (4.5B eff.) — 8-16 GB RAM, most 2024+ MacBooks
  • 26B A4B — 16-24 GB VRAM (single GPU), or CPU with enough RAM
  • 31B — 24-48 GB VRAM (A100/H100 recommended), or multi-GPU

The 26B A4B is generally considered the sweet spot for local use — frontier-level benchmarks (82.6 MMLU Pro, 88.3 AIME) with ~4B active parameter compute cost.

All models are on Hugging Face under google/gemma-4-*.

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