Gemma 4

Canonical version: Gemma 4.

Gemma 4 is the fourth generation of Google's Gemma open-weight model family. Released April 2, 2026, it is purpose-built for advanced reasoning and agentic workflows. Licensed under Apache 2.0.

Model variants

The "E" prefix stands for "effective parameters" using Per-Layer Embeddings (PLE), maximizing efficiency for on-device use. The 26B MoE variant must load all 26B parameters into memory despite only activating 3.8B per token.

Architecture

Hybrid attention mechanism interleaving local sliding window attention with full global attention. Sliding window sizes: 512 tokens (E2B/E4B), 1024 tokens (26B A4B, 31B). Vocabulary size: 262K tokens across all variants.

Vision encoders: ~150M parameters (E2B/E4B), ~550M parameters (26B A4B, 31B). Variable image resolution via configurable token budgets (70, 140, 280, 560, 1120). Audio encoders (~300M params) on E2B/E4B only; supports ASR and speech-to-translated-text up to 30 seconds. Video support via frame sequences, up to 60 seconds.

Key features

• AI Multimodal: text, image, audio (small models), video across all variants
• Built-in reasoning: configurable thinking mode via <|think|> token for step-by-step reasoning
• Native function calling: structured tool use for agentic workflows
• System role support: native system role (new in Gemma 4)
• Multilingual: 35+ languages out-of-box, trained on 140+ languages
• Long context: 128K (small) to 256K (medium) token windows

What's new vs Gemma 3

• Audio modality on small models (E2B, E4B)
• Built-in reasoning / thinking mode
• Native system role support
• 256K context window (up from 128K max)
• Mixture-of-Experts variant (26B A4B)
• Per-Layer Embeddings (PLE) for efficient small models
• Significantly improved coding and math benchmarks

Benchmarks (instruction-tuned)

Memory requirements

Multi-Token Prediction (MTP) drafters — May 2026 update

On 2026-05-05 Google released a companion line of small autoregressive drafter models for the Gemma 4 family, alongside a Multi-Token Prediction (MTP) head. These enable Speculative Decoding at inference time — the drafter predicts several tokens ahead, the target Gemma 4 model verifies them in parallel, and accepted tokens roll out without waiting for token-by-token decoding.

Reported speedups: up to 3× without quality degradation. On Apple Silicon with mixture-of-experts variants and batch sizes 4–8, ~2.2× decoding speedups.

The drafters introduce three architectural enhancements that distinguish them from generic speculative-decoding setups:

• Target activations sharing. The drafter consumes the final-layer activations of the target model (concatenated with its embeddings) on round 1, then reuses its own activations on subsequent rounds.
• KV cache sharing. The drafter cross-attends to the target model's KV cache instead of building its own — no redundant prompt re-processing.
• Efficient embedder. The LM Head uses sparse decoding via clustered token lookup; the drafter only computes logits for the most likely cluster, not the full 262K-token vocabulary.

For the broader concept (not Gemma-specific), see AI Multi-Token Prediction Drafters.

Available under Apache 2.0 on Hugging Face and Kaggle; supported in Transformers, MLX, vLLM, SGLang, and Ollama.

Run locally

Via Ollama:

ollama run gemma4
ollama run gemma4:e4b
ollama run gemma4:27b

Access

• Google AI Studio (free)
• Google AI Edge Gallery (mobile app)
• Vertex AI
• Hugging Face: google/gemma-4-27b-it, google/gemma-4-e4b-it
• Ollama: gemma4
• ...

References

• Google blog: https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/
• Multi-Token Prediction announcement (2026-05-05): https://blog.google/innovation-and-ai/technology/developers-tools/multi-token-prediction-gemma-4/
• Drafter explainer (Google Gemma on X): https://x.com/googlegemma/status/2051694045869879749
• Model card: https://ai.google.dev/gemma/docs/core/model_card_4
• Gemma docs: https://ai.google.dev/gemma/docs/core

Related

• Gemma
• Gemini
• Google AI Edge Gallery
• Large Language Models (LLMs)
• Dense AI Models
• Sparse AI Models
• AI Mixture of Experts (MoE)
• AI Multimodal
• Ollama
• Google AI Studio
• Agentic Era
• AI Multi-Token Prediction Drafters
• Speculative Decoding
• AI Inference
• MLX
• vLLM
• SGLang
• Transformers

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