What Makes Gemma 4 Different
Gemma 4 is not an incremental update. Three architectural innovations set it apart from every previous Google open model and most of its competitors.
First, the Per-Layer Embeddings (PLE) technique used in the E2B and E4B variants. Instead of sharing a single embedding table across all layers, PLE assigns specialized embeddings at each transformer layer. This squeezes significantly more capability out of small parameter counts, which is why the 4B-effective E4B punches well above its weight class on reasoning benchmarks.
Second, the Mixture-of-Experts (MoE) architecture in the 26B-A4B variant. With 128 experts and only 3.8 billion parameters active per token, this model delivers 26B-class knowledge with the speed and memory footprint of a 4B model. It is the densest expert configuration in any open-weight model to date.
Third, the Apache 2.0 license. Previous Gemma releases used a custom license with commercial restrictions. Gemma 4 drops all of that. You can use, modify, fine-tune, and redistribute every variant with no usage caps or monthly active user limits.
Multimodal by default: All four Gemma 4 variants accept text and image input natively. The E2B and E4B models also support audio input, making them the smallest multimodal models you can run locally on a Mac.
Which Gemma 4 Model Should You Run?
According to LLMCheck testing, the right Gemma 4 variant depends entirely on your hardware and workload. Here is how they compare:
| Variant | Architecture | RAM (INT4) | tok/s (M5 Max) | Modality | Best For |
|---|---|---|---|---|---|
| E2B | 2.3B active, PLE | ~1.5 GB | ~155 | Text + Image + Audio | Edge, mobile, autocomplete |
| E4B | 4B effective, PLE | ~3 GB | ~125 | Text + Image + Audio | General assistant, daily driver |
| 26B-A4B | MoE, 128 experts, 3.8B active | ~18 GB | ~48 | Text + Image | Reasoning, coding, agents |
| 31B Dense | 31B dense | ~20 GB | ~24 | Text + Image | Frontier quality, Arena #3 |
For most Mac users with 8-16 GB of RAM, the E4B is the sweet spot. It delivers strong general reasoning and multimodal capability at 125 tok/s while consuming just 3 GB of memory. If you have 32 GB or more, the 26B-A4B MoE variant offers a massive quality jump for only 18 GB of RAM, thanks to activating just 3.8B parameters per token.
Step-by-Step Setup with Ollama
Getting any Gemma 4 variant running on your Mac takes under three minutes. Ollama handles weight downloads, Metal GPU acceleration, and memory allocation automatically.
1. Install Ollama
Download from ollama.com or install via Homebrew:
brew install ollama2. Start the Ollama server
ollama serve3. Pull and run your chosen Gemma 4 variant
# Default E4B (recommended for most users)
ollama run gemma4
# Ultra-light E2B
ollama run gemma4:e2b
# MoE powerhouse (needs 18+ GB RAM)
ollama run gemma4:26b
# Dense flagship (needs 20+ GB RAM)
ollama run gemma4:31bOllama downloads the quantized weights automatically. The E4B is roughly a 2 GB download and launches in seconds. The 26B and 31B variants are larger (10-12 GB) and take a few minutes on typical broadband.
4. Verify Metal GPU acceleration
Open Activity Monitor and check that the ollama_llama_server process shows GPU usage. On Apple Silicon, Metal acceleration is enabled by default. If GPU reads 0%, restart with:
OLLAMA_METAL=1 ollama servePro tip: Gemma 4 supports 256K context natively. To unlock extended context, set OLLAMA_NUM_CTX=131072 before launching. This allocates additional memory for 128K tokens of context.
Benchmark Results
According to LLMCheck testing across Apple Silicon configurations, here is how Gemma 4 stacks up against the current top local models:
| Model | Active Params | Context | RAM (INT4) | tok/s (M5 Max) | Arena ELO |
|---|---|---|---|---|---|
| Gemma 4 31B Dense | 31B | 256K | ~20 GB | ~24 | 1452 (#3) |
| Gemma 4 26B-A4B | 3.8B | 256K | ~18 GB | ~48 | 1441 (#6) |
| Gemma 4 E4B | 4B | 256K | ~3 GB | ~125 | — |
| Qwen 3.5 9B | 9B | 262K | ~6 GB | ~78 | — |
| Phi-4 Mini (3.8B) | 3.8B | 128K | ~2.5 GB | ~140 | — |
The standout result is the 26B-A4B MoE variant. It activates only 3.8B parameters per token yet ranks #6 on Arena AI with an ELO of 1441, outperforming many dense models five to ten times its active size. The 31B Dense variant at Arena #3 competes directly with closed-source APIs while running entirely on your Mac.
MLX vs Ollama: Which Runner?
Gemma 4 launched with day-one MLX support, giving Mac users two excellent options for local inference. According to LLMCheck analysis, the choice comes down to your workflow:
- Ollama is the best choice for most users. One-command setup, automatic Metal acceleration, built-in API server for tool integration, and a large ecosystem of compatible UIs like Open WebUI and Enchanted. Use Ollama if you want a drop-in local ChatGPT replacement.
- MLX (via
mlx-lm) offers lower-level control and typically 5-15% faster inference on Apple Silicon thanks to tighter Metal integration. Choose MLX if you are doing research, fine-tuning, or building custom inference pipelines. Install withpip install mlx-lmand load Gemma 4 weights directly from Hugging Face.
For raw speed on Apple Silicon, MLX has a slight edge. For convenience and ecosystem support, Ollama wins. Both are free and open source.
Best Use Cases for Gemma 4 on Mac
The breadth of the Gemma 4 family means there is a variant for nearly every local AI workflow:
- On-device assistant (E4B): At 3 GB RAM and 125 tok/s, E4B is fast enough for real-time chat, summarization, and email drafting on any Mac. Multimodal input means you can paste screenshots directly.
- Coding copilot (26B-A4B): The MoE model's Arena #6 ranking and native function calling make it a strong local coding assistant. Pair it with Continue.dev or Cursor for IDE integration.
- Agentic workflows (26B-A4B / 31B): Native function calling support across all variants enables structured tool use. Build local AI agents that query databases, call APIs, and execute multi-step plans without any cloud dependency.
- Edge and mobile prototyping (E2B): At 1.5 GB and 155 tok/s, E2B is ideal for testing on-device AI features before deploying to iOS or embedded hardware.
- Private document analysis (31B Dense): Feed confidential legal, medical, or financial documents into the 256K context window of the frontier-quality 31B model. Zero data ever leaves your machine.