Most AI model launches give you one model and one decision: use it or don’t. Gemma 4 gives you five models and asks a different question: what hardware do you have?
That might sound like marketing, but it’s actually the most useful thing about this family. Each member is architecturally different — not just a scaled-up copy of the same thing. The edge models use Per-Layer Embeddings. The 12B threw out vision and audio encoders entirely. The 26B routes tokens through a mixture of 128 experts. The 31B just throws all 30.7 billion parameters at every token. Same family, different engineering philosophies, different trade-offs.
Let’s walk through them.
E2B — The Pocket AI (~1 GB RAM)
The smallest Gemma 4. Two billion parameters, quantized down to fit in about 1 GB of RAM. It handles text, images, and live audio — all on-device, all offline. Scores 37.5% on AIME 2026, which is competitive math reasoning on something that could run on a Raspberry Pi. The secret sauce is Per-Layer Embeddings (PLE), which gives each decoder layer its own dedicated embedding to maximize intelligence without bloating parameter count. You won’t mistake it for a desktop model, but for quick translations, photo questions, or voice queries on a budget phone, it’s genuinely useful.
E4B — The Phone Powerhouse (4–6 GB RAM)
The E4B is what happens when you give a phone-optimized model enough parameters to actually think. It scores 42.5% on AIME 2026 — more than doubling Gemma 3’s much larger 27B model. It handles text, images, and audio natively, has a 128K context window, and includes a configurable thinking mode for multi-step reasoning. If you have a modern flagship phone with 8+ GB of RAM, this is the model that makes “I’ll just ask my phone — offline” a serious option instead of a party trick.
12B Unified — The Privacy-First Laptop Agent Powerhouse (~7 GB VRAM with QAT)
This is where Gemma 4 gets exciting for most people. Released June 3, 2026, the 12B Unified does something no other model its size does: it handles text, images, and audio in a single decoder-only transformer with no separate encoders. Raw image patches and audio waveforms go straight into the embedding space through lightweight linear layers. Simpler architecture, lower latency, easier fine-tuning.
The numbers: 77.2% MMLU Pro, 77.5% AIME 2026, 72.0% LiveCodeBench, 78.8% GPQA Diamond. Google says it approaches the 26B MoE “at less than half the total memory footprint.” With the official QAT (Quantization-Aware Training) variant released June 5, the Q4_0 version needs roughly 6.7 GB of VRAM. Pair that with Multi-Token Prediction for speculative decoding, and community benchmarks show 100–130+ tokens per second on a 12 GB GPU like the RTX 4070 Super. It even runs on laptops with 16 GB unified memory — no dedicated GPU required.
If you want to run autonomous AI agents, local voice assistants, or private coding workflows directly on your laptop without cloud dependency or privacy risks, this is your model.
26B MoE — The Efficiency Expert (15–18 GB VRAM quantized)
The 26B contains 26 billion total parameters, but here’s the trick: only 3.8 billion activate per token. A learned router selects 2 of 128 expert sub-networks for each token, delivering near-31B quality at dramatically lower compute cost. Think of it as having a building full of specialists and only calling the two you need for each question.
It supports text, images, and video (not just audio like the smaller models), has a 256K context window, and ranks #6 among open models on Arena AI. The trade-off is VRAM — you need 15–18 GB quantized, which means an RTX 4090, an RTX 5060 Ti 16GB, or a Mac with 32 GB+ unified memory. If you have the hardware and want the best intelligence-per-watt ratio, this is your model.
31B Dense — The Uncompromising Giant (16–20 GB VRAM quantized)
No routing, no mixture of experts, no shortcuts. The 31B Dense fires all 30.7 billion parameters on every single token. It’s the quality ceiling of the Gemma 4 family — ranking #3 among all open models on Arena AI and scoring 89.2% on AIME 2026. Same modalities as the 26B (text, images, video), same 256K context window, but with maximum reasoning depth on every response.
The cost is compute. BF16 needs ~71 GB of VRAM (enterprise GPU territory). But quantized to INT4, it fits in 16–20 GB — manageable on a high-end consumer GPU. If you have the hardware and precision matters more than speed, this is the open model that gets closest to frontier cloud performance.
Which one should you pick?
Here’s the honest cheat sheet:
- Phone, offline, quick tasks → E4B (or E2B for very constrained devices)
- Laptop, 8–12 GB GPU → 12B Unified with QAT
- Laptop, 16 GB unified memory, no GPU → 12B Unified with QAT (slower but works)
- Workstation, RTX 4090 / 32 GB Mac → 26B MoE (best quality-per-watt)
- Server or high-end workstation → 31B Dense (maximum quality)
All five share the Apache 2.0 license, support 140+ languages, and work with Ollama, llama.cpp, LM Studio, vLLM, and Google’s AI Edge toolkit. The family disagrees on architecture — but agrees on philosophy: serious AI that runs on your hardware.
