- Artificial Intelligence
Gemma 4 by Google: Specs, Benchmarks, Model Sizes, and How to Run It Locally (2026 Guide)
Gemma 4 by Google: Specs, Benchmarks, Model Sizes, and How to Run It Locally (2026 Guide)
Gemma 4 by Google: Specs, Benchmarks, Model Sizes, and How to Run It Locally
ollama run gemma4. Since launch, Gemma 4 has seen rapid adoption alongside competing releases like Qwen 3.5 (April 2026), continued Llama 4 updates from Meta, and the rising wave of agentic AI frameworks. This guide compares all major open models and is updated regularly as new benchmarks and tools emerge.What if a model small enough to fit on your smartphone could outperform AI systems 20 times its size? That is no longer hypothetical. With Gemma 4, Google is pushing frontier-level AI into devices, laptops, workstations, and servers in a form developers can actually run, fine-tune, and deploy commercially. In a 2026 landscape defined by agentic AI, vibe coding, MCP tool ecosystems, and the push toward private on-device intelligence, Gemma 4 sits at the center of the open model conversation.
Why Gemma 4 Is a Big Deal
Since the first Gemma models launched, developers around the world have downloaded them over 400 million times and created more than 100,000 custom variants. That level of adoption tells you something important: people wanted open models that were practical, fast, and deployable beyond the cloud.
Gemma 4 is Google DeepMind's answer to that demand. It brings frontier-level intelligence into model families that can run on everything from a Raspberry Pi to a data-centre GPU, while remaining open enough for developers and businesses to actually build with. Built from the same research and technology behind Gemini 3, it is the most capable model family you can run on your own hardware.
The open model landscape has shifted rapidly since Gemma 4's launch. Alibaba's Qwen 3.5 family dropped weeks later with competitive scores, Meta continued expanding Llama 4 Scout's ecosystem, and Mistral pushed its own mid-size models. Yet Gemma 4 remains the only family that spans phones to servers under a fully permissive Apache 2.0 license with no MAU restrictions, a combination that none of its competitors match as of May 2026.
Meanwhile, the broader AI ecosystem has moved decisively toward agentic AI, where models don't just answer questions but autonomously call tools, make decisions, and execute multi-step workflows. Anthropic's Model Context Protocol (MCP) has emerged as a standard for connecting AI models to external tools and data sources. Google's own Agent-to-Agent (A2A) protocol is gaining traction for multi-agent coordination. And the "vibe coding" movement, where developers describe what they want in natural language and AI writes the code, has gone from novelty to mainstream workflow. Gemma 4 sits at the intersection of all three trends.
What Is Gemma 4?
Gemma 4 is Google's newest family of open AI models, released on April 2, 2026. The models are built from the same research foundations behind Gemini 3, but unlike Google's proprietary offerings, Gemma 4 is released openly for the community to use, modify, and deploy.
Google has published Gemma 4 under the Apache 2.0 license, which means developers and companies can use it commercially without restrictive licensing headaches. No monthly active user limits, no acceptable use policies, no special permissions needed. Whether you need a small model for a mobile app or a larger model for research and advanced tooling, the family has multiple sizes built for different hardware profiles.
This licensing distinction matters more in 2026 than ever before. As companies build AI agents that run continuously, process customer data, and integrate with internal tools via protocols like MCP, the licensing terms of the underlying model become a strategic decision. Apache 2.0 means no surprises at scale.
Gemma 4 Model Sizes and Hardware Requirements
Google designed Gemma 4 to span edge devices, laptops, consumer GPUs, and production servers. The family includes four options, each with a distinct deployment sweet spot.
| Model | Active Params | Best For | Context | Min RAM (Q4) |
|---|---|---|---|---|
| E2B | ~2.3B effective | Smartphones, IoT, Raspberry Pi | 128K tokens | ~1.5 GB |
| E4B | ~4.5B effective | Mobile apps, edge devices, laptops | 128K tokens | ~5 GB |
| 26B A4B MoE | 3.8B of 26B total | Consumer GPUs (RTX 3090/4090), Mac | 256K tokens | ~14–18 GB |
| 31B Dense | 30.7B (all active) | Maximum quality, research, fine-tuning | 256K tokens | ~20 GB |
The 26B model uses a Mixture of Experts (MoE) architecture with 128 small experts, activating only 8 per token plus one shared expert. Instead of activating the full model every time, it selectively turns on the most relevant expert pathways, delivering near-31B quality at dramatically lower compute cost.
The E2B and E4B use Per-Layer Embeddings (PLE), giving them the representational depth of a much larger model while keeping memory usage low enough for smartphones and Raspberry Pi boards.
Key Capabilities: Reasoning, Vision, Code, and Agents
Gemma 4 is not just another general-purpose text model. It combines reasoning, structured outputs, multimodal inputs, and long-context support in ways that make it genuinely useful for modern product development.
Gemma 4 Benchmarks: Real Numbers
| Benchmark | Gemma 4 31B | Gemma 3 27B | Category |
|---|---|---|---|
| MMLU Pro | 85.2% | — | General Knowledge |
| AIME 2026 | 89.2% | 20.8% | Math Competition |
| GPQA Diamond | 84.3% | 42.4% | Graduate-Level Reasoning |
| LiveCodeBench v6 | 80.0% | 29.1% | Coding |
| Codeforces ELO | 2150 | 110 | Competitive Programming |
| MMMU Pro | 76.9% | — | Vision Understanding |
| Arena AI ELO | 1452 (#3) | — | Human Preference |
The 26B MoE model ranks #6 on Arena AI with an ELO of 1441, while only activating roughly 3.8 billion parameters during inference, achieving 97% of the 31B's quality at approximately 8x less compute per inference step. That level of efficiency is why Google describes it as "intelligence per parameter."
Early reports show encouraging local inference speeds: the 31B model exceeds 10 tokens/sec on local GPU setups, the 26B MoE reaches 40+ tok/s, and E2B runs at 60+ tok/s on edge hardware.
Gemma 4 vs. Qwen 3.5 vs. Llama 4 vs. Others: Head-to-Head
| Dimension | Gemma 4 31B | Qwen 3.5 27B | Llama 4 Scout |
|---|---|---|---|
| MMLU Pro | 85.2% | 86.1% | — |
| GPQA Diamond | 84.3% | 85.5% | 74.3% |
| AIME 2026 (Math) | 89.2% | ~48.7%* | — |
| Codeforces ELO | 2150 | — | — |
| Arena AI ELO | 1452 (#3) | ~1404 | — |
| License | Apache 2.0 | Apache 2.0 | Meta License (700M MAU cap) |
| Context Window | 256K tokens | 128K tokens | 10M tokens |
| Smallest Model | E2B (2.3B) for phones | 0.8B | 109B total (no edge) |
| Audio Support | Yes (E2B/E4B) | Omni variant only | No |
When to pick Gemma 4: Best for math-heavy reasoning, edge/on-device deployment, competitive programming, agentic AI tool-use workflows, and when you need the widest hardware coverage (phones to servers) under a fully open license.
When to pick Qwen 3.5: Best for production coding workflows (SWE-bench leader at 72.4%), when you need the largest available model (397B), or for real-time speech output via Qwen 3.5-Omni.
When to pick Llama 4 Scout: When you need massive context windows (10M+ tokens) and can accept Meta's licensing restrictions.
* Qwen 3.5 AIME score is from AIME 2025; direct numerical comparison across benchmark versions is directional, not exact.
What About Mistral, DeepSeek, Phi-4, and Claude?
The open model space in 2026 is crowded. Beyond the three models compared above, developers are also evaluating Mistral's mid-size offerings for European data sovereignty use cases, DeepSeek V3 for cost-efficient Chinese-language tasks, Microsoft's Phi-4 for ultra-lightweight edge scenarios, and comparing open models against proprietary options like Anthropic's Claude 4 and OpenAI's GPT-4o.
However, none of these match Gemma 4's combination of benchmark scores, edge-to-server hardware coverage, multimodal support (text, image, video, audio), and Apache 2.0 licensing in a single model family. For teams that need one model family to standardize across their entire stack from mobile to server, Gemma 4 remains the most versatile choice.
When to pick Mistral or Phi-4: If your primary concern is European data residency (Mistral) or you need sub-1B parameter models for extremely constrained edge devices (Phi-4). These are niche scenarios where specialized models may be a better fit than Gemma 4's broader family.
When to pick Claude or GPT-4o instead: When maximum intelligence matters more than local deployment or cost control. Proprietary models like Claude 4 and GPT-4o still lead on complex multi-turn reasoning and nuanced instruction following. But they require internet access, incur per-token costs, and send your data to external servers. If privacy, cost at scale, or offline capability matter, Gemma 4 is the stronger choice.
Gemma 4 for Agentic AI and MCP Tool Ecosystems
The biggest shift in AI during 2026 is not a new model; it is a new paradigm. Agentic AI, where models don't just answer questions but autonomously plan tasks, call external tools, make decisions, and execute multi-step workflows, has moved from research concept to production reality.
Anthropic's Model Context Protocol (MCP) has quickly become the standard for connecting AI models to external data sources and tools. Think of MCP as a universal adapter: it lets any AI model interact with databases, APIs, file systems, calendars, CRMs, and more through a standardized interface. MCP servers are already available for Google Drive, Slack, GitHub, Jira, Salesforce, and hundreds of other services.
Google has responded with its own Agent-to-Agent (A2A) protocol, designed for multi-agent coordination where specialized AI agents hand off tasks to each other. Together, MCP and A2A are building the plumbing for a future where AI agents collaborate autonomously.
Where Gemma 4 fits in: Its native function calling, JSON structured output, configurable thinking modes, and 256K context window make it well-suited as the "brain" of agentic systems. Because it runs locally under Apache 2.0, you can deploy Gemma 4 agents on your own infrastructure without per-call API costs or data leaving your servers. For sensitive workflows in healthcare, finance, legal, and enterprise operations, this is a significant advantage over cloud-only proprietary models.
The combination of agentic capabilities and local deployment is what makes Gemma 4 particularly relevant right now. You're not just choosing a model; you're choosing a foundation for autonomous workflows that may run continuously and handle sensitive data at scale.
Gemma 4 and the Vibe Coding Revolution
The term "vibe coding" was coined to describe a new style of software development: instead of writing code line by line, you describe the intent ("build me a dashboard that shows real-time sales by region") and an AI model generates the implementation. What started as a playful concept has become a genuine productivity shift in 2026.
Tools like Cursor, Windsurf, Claude Code, GitHub Copilot, Bolt, Lovable, and Replit have made vibe coding accessible to millions of developers. But most of these tools rely on cloud-based proprietary models, which means your code, prompts, and context are sent to external servers.
Gemma 4 offers an alternative. With a Codeforces ELO of 2150 (expert competitive programmer level), 80% on LiveCodeBench v6, and the ability to run entirely on a single consumer GPU, it is one of the most capable coding models you can run locally. That means vibe coding with full privacy: your proprietary codebase, your internal APIs, your business logic, all staying on your machine.
For teams building internal tools, prototyping features, or working with sensitive code, a local Gemma 4 instance combined with an IDE extension or a tool like Continue.dev gives you the vibe coding experience without the data exposure risk.
How to Download and Run Gemma 4 Locally
ollama run gemma4. For more control, use llama.cpp. For a visual interface, use LM Studio. For production serving, use vLLM. All models have day-one support across Hugging Face, Kaggle, Ollama, LM Studio, NVIDIA NIM, and more.Easiest Method: Install with Ollama
Ollama handles model downloads, quantization, and GPU detection automatically. Install it, then run one command:
# Install Ollama (macOS / Linux)
curl -fsSL https://ollama.com/install.sh | sh
# Run the default 26B MoE model (recommended for most)
ollama run gemma4
# Or choose a specific size:
ollama run gemma4:e2b # Edge — phones, Raspberry Pi
ollama run gemma4:e4b # Edge — laptops, mobile apps
ollama run gemma4:26b # MoE — best speed/quality balance
ollama run gemma4:31b # Dense — maximum qualityQuick Start with LM Studio (GUI Option)
If you prefer a visual interface over the terminal, LM Studio offers one-click download and chat for all Gemma 4 variants. Download LM Studio from lmstudio.ai, search for "Gemma 4" in the model browser, select your preferred size and quantization, and start chatting. LM Studio auto-detects your GPU and applies optimal settings. It also exposes a local API endpoint, so you can integrate Gemma 4 into your own applications without touching the command line.
For Maximum Control: llama.cpp
If you need control over quantization, context length, or batch size:
# Build llama.cpp with GPU support
git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j
# Run the 26B MoE model
./llama.cpp/llama-cli \
-hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_XL \
--temp 1.0 --top-p 0.95 --top-k 64On Apple Silicon Macs, use -DGGML_CUDA=OFF. Metal support is enabled by default.
For Python Developers: Hugging Face Transformers
pip install transformers torch
# Load the model with model ID: google/gemma-4-31B-itTry Without Installing Anything
Explore larger Gemma 4 models in Google AI Studio. For on-device variants, check Google's AI Edge resources.
Download Model Weights
Day-one tool support: Gemma 4 works with Hugging Face Transformers, vLLM, llama.cpp, MLX (Apple Silicon), LM Studio, Ollama, NVIDIA NIM & NeMo, Unsloth, SGLang, Keras, Docker, Baseten, and more.
Can Gemma 4 Run on a Smartphone?
Yes. The E2B and E4B models were specifically built for on-device use. They can run offline on smartphones, Raspberry Pi boards, and embedded hardware like NVIDIA Jetson devices. In 4-bit mode, E2B fits in approximately 1.5 GB RAM and E4B in roughly 5 GB, feasible for modern mobile and edge scenarios.
That matters because it pushes AI features like local summarization, on-device assistants, private image analysis, and edge-side agentic flows into practical reach for product teams. Both E2B and E4B support native audio input, a capability that neither Llama 4 nor Qwen 3.5 offer at these sizes.
In the context of 2026's agentic AI trend, on-device models become even more valuable. Imagine an AI agent running locally on a warehouse scanner that reads barcodes, checks inventory via MCP, and triggers reorders without ever sending data to the cloud. Gemma 4 E2B and E4B make these scenarios realistic.
| Model | 4-bit RAM | 8-bit RAM | Full Precision |
|---|---|---|---|
| E2B | ~1.5 GB | ~3 GB | ~10 GB |
| E4B | ~5 GB | ~8 GB | ~15 GB |
| 26B MoE | ~14–18 GB | ~28 GB | ~52 GB |
| 31B Dense | ~20 GB | ~34 GB | ~62 GB |
Fine-Tuning Gemma 4 for Custom Tasks
The Apache 2.0 license allows unrestricted fine-tuning on proprietary data. Using QLoRA (Quantized LoRA) via tools like Unsloth, you can fine-tune the 31B model with as little as 16 GB VRAM, a single RTX 4090 or equivalent.
Fine-tuning is supported on Google Colab, Vertex AI, Hugging Face TRL, Unsloth, and consumer GPUs. Full fine-tuning (all parameters) requires approximately 80 GB VRAM for the 31B model. For most custom tasks such as domain-specific Q&A, specialized coding, custom instruction following, and agentic tool use, QLoRA is sufficient and far more accessible.
Unsloth also offers GRPO reinforcement learning and the ability to auto-create training datasets from PDFs, CSVs, and DOCX files, making it practical to fine-tune Gemma 4 for specific business domains.
A growing number of teams are fine-tuning Gemma 4 specifically for agentic tool use: training the model to reliably call the right MCP tools, parse structured responses, and handle multi-step workflows with minimal hallucination. This is one of the most active fine-tuning use cases in 2026.
Why Businesses and Developers Should Care
For teams looking to deploy AI at scale, Gemma 4's compatibility with major inference frameworks, MCP tool ecosystems, and cloud platforms makes the path from prototype to production cleaner than ever. Cygnus Alpha by Auriga IT can help integrate open models into real business workflows.
The Open AI Model Landscape in May 2026
The pace of AI releases in 2026 has been extraordinary. To understand where Gemma 4 fits, it helps to zoom out and see the full picture of what has happened since its launch.
Google I/O 2025 and Beyond: Google used I/O to announce the Gemini ecosystem expansion, including Gemini 3 for cloud, Project Astra for multimodal agents, and the Gemma open model family for developers. Gemma 4 is the direct continuation of that strategy, now with significantly stronger capabilities and broader hardware reach.
Anthropic's MCP Ecosystem: Anthropic's Model Context Protocol has become the de facto standard for connecting AI to tools. Originally launched as an open protocol, MCP now has server implementations for hundreds of services. Any model with function calling support, including Gemma 4, can participate in MCP ecosystems. This has created a new selection criterion for open models: not just "how smart is it" but "how well does it use tools."
The Rise of AI Coding Tools: Cursor, Windsurf, GitHub Copilot Workspace, and Claude Code have made AI-assisted coding the default workflow for a growing number of developers. Open models like Gemma 4 are increasingly being plugged into these tools as local backends for teams that want the productivity gains without the data exposure.
Enterprise AI Adoption: Companies are no longer asking "should we use AI?" but "which model, where, and under what terms?" The Apache 2.0 license, local deployment options, and agentic capabilities of models like Gemma 4 directly address the procurement, privacy, and compliance concerns that slowed enterprise adoption in previous years.
The Multimodal Push: Vision, audio, and video understanding are no longer bonus features. They are expected. Gemma 4's native multimodal support across all model sizes, with audio on E2B and E4B specifically, puts it ahead of most open alternatives for applications that need to process real-world inputs beyond text.
Frequently Asked Questions About Gemma 4
What is Gemma 4?
Gemma 4 is Google DeepMind's most capable family of open AI models, released April 2, 2026. Built from Gemini 3 research, it includes 4 model sizes (E2B, E4B, 26B MoE, 31B Dense) under the Apache 2.0 license for unrestricted commercial use.
Is Gemma 4 free to use commercially?
Yes. The Apache 2.0 license allows unlimited commercial use, modification, fine-tuning, and redistribution with no royalty payments, no MAU limits, and no restrictive use policies.
What are the Gemma 4 model sizes?
E2B (~2.3B effective, for phones), E4B (~4.5B effective, for edge), 26B A4B MoE (3.8B active of 26B total, for consumer GPUs), and 31B Dense (all parameters active, for maximum quality).
How does Gemma 4 compare to Qwen 3.5?
Within 1 to 2% on most reasoning benchmarks. Qwen 3.5 leads on MMLU Pro (86.1% vs 85.2%) and SWE-bench coding. Gemma 4 dominates on math (AIME 89.2%), competitive programming (Codeforces 2150), and human preference (Arena AI). Both use Apache 2.0.
How does Gemma 4 compare to Llama 4?
Gemma 4 31B outperforms Llama 4 Scout (109B total) on reasoning benchmarks like GPQA Diamond (84.3% vs 74.3%). Gemma 4 uses Apache 2.0 while Llama 4 has a 700M MAU restriction. Gemma 4 also covers edge deployment; Llama 4 has no small models.
Is Gemma 4 better than ChatGPT for local use?
For local, offline, private use, yes. ChatGPT requires internet access and sends data to OpenAI's servers. Gemma 4 runs entirely on your hardware with no data leaving your machine. For cloud-based tasks where maximum intelligence matters and privacy is not a concern, GPT-4o and Claude remain stronger on complex multi-turn reasoning. But for local deployment, Gemma 4's 26B MoE model is one of the best options available.
What is the best open source AI model in 2026?
As of May 2026, the top open models are Gemma 4 (best all-around family from edge to server), Qwen 3.5 (strongest for coding and largest available at 397B), and Llama 4 Scout (best for ultra-long context at 10M tokens). Gemma 4 and Qwen 3.5 both use Apache 2.0 licensing. The "best" depends on your specific use case, hardware, and licensing needs.
Can Gemma 4 be used for agentic AI workflows?
Yes. Gemma 4 has native support for function calling, JSON structured output, system instructions, and configurable thinking modes. Combined with its 256K context window and Apache 2.0 license, it is well-suited for building AI agents that autonomously call tools, make decisions, and execute multi-step workflows on your own infrastructure. It is compatible with MCP-style tool ecosystems.
What is MCP and does Gemma 4 support it?
MCP (Model Context Protocol) is an open standard by Anthropic that lets AI models interact with external tools and data sources through a standardized interface. Any model with function calling support can work with MCP, including Gemma 4. This means you can build agents that access databases, APIs, calendars, and more through a consistent protocol.
Can Gemma 4 be used for vibe coding?
Yes. With a Codeforces ELO of 2150 and 80% on LiveCodeBench v6, Gemma 4 is capable enough to power AI-assisted coding workflows. Run it locally with tools like Continue.dev, LM Studio, or Ollama's API to get vibe coding capabilities without sending your code to external servers.
What hardware do I need to run Gemma 4?
E2B: ~1.5 GB RAM. E4B: ~5 GB. 26B MoE at Q4: ~14 to 18 GB (fits on RTX 3090/4090 or Mac with 24GB unified memory). 31B Dense at Q4: ~20 GB. All models also run on CPU, though slower.
How do I run Gemma 4 with Ollama?
Install Ollama, then run: ollama run gemma4 for the 26B MoE, or ollama run gemma4:31b for maximum quality. Ollama handles downloading, quantization, and GPU detection automatically.
Can Gemma 4 run on a smartphone?
Yes. E2B and E4B are designed for on-device mobile deployment. E2B fits in ~1.5 GB RAM, runs on modern Android phones via Google AICore, operates completely offline, and supports native audio input.
What is the Gemma 4 context window?
E2B and E4B: 128K tokens. 26B MoE and 31B Dense: 256K tokens, sufficient for processing entire codebases, long documents, and extended conversations.
What benchmarks does Gemma 4 31B achieve?
MMLU Pro: 85.2%. AIME 2026: 89.2%. GPQA Diamond: 84.3%. LiveCodeBench v6: 80.0%. MMMU Pro (vision): 76.9%. Codeforces ELO: 2150. Arena AI: #3 with ELO 1452.
Does Gemma 4 support images, video, and audio?
All models support text + image input with variable resolution. E2B and E4B add native audio. Video is processed as frame sequences across all sizes. You can mix text and images freely in a single prompt.
Can I fine-tune Gemma 4?
Yes. Apache 2.0 allows unrestricted fine-tuning. Using QLoRA via Unsloth, the 31B can be fine-tuned with 16 GB VRAM. Full fine-tuning needs ~80 GB. Supported on Google Colab, Vertex AI, and consumer GPUs.
What is the difference between Gemma 4 and Gemini?
Gemini is Google's proprietary cloud model (API-accessible). Gemma 4 is the open-weight version from the same research, designed to run locally on your hardware with full data privacy. Gemini is more powerful; Gemma 4 is more flexible and private.
What languages does Gemma 4 support?
Over 140 languages natively, making it one of the most multilingual open-weight model families available for global applications.
Which Gemma 4 model should I use?
For most developers: the 26B MoE. It delivers 97% of the 31B's quality at ~8x less compute and fits on a single RTX 3090/4090. For phones: E2B. For laptops: E4B. For maximum quality with 24GB+ VRAM: 31B Dense.
The Bottom Line on Gemma 4
Gemma 4 represents a real shift in open AI. Models that previously needed large-scale infrastructure are now viable on smaller devices, local machines, and more affordable hardware profiles. That changes how teams can think about privacy, cost, latency, and product design.
The bigger takeaway is not just that Gemma 4 is good. It is that open AI is increasingly becoming practical, competitive, and deployable in real-world products. With Apache 2.0 licensing, frontier-level benchmarks, edge deployment, agentic AI capabilities, MCP tool compatibility, and broad ecosystem support from day one, Gemma 4 is the strongest open model family for developers who want to build without restrictions.
In a 2026 landscape defined by agentic workflows, vibe coding, and the push toward private on-device intelligence, having a model family that covers phones to servers under a truly open license is not just convenient. It is a strategic advantage.
At Auriga IT, we help businesses turn AI breakthroughs like Gemma 4 into working products and scalable systems. From building intelligent applications to deploying them on strong cloud infrastructure, we work with the latest tools so teams can move faster with less uncertainty.
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Whether you are exploring open models, building AI agents, deploying private on-device intelligence, or looking to integrate agentic workflows into your business, our team can help you turn the latest model advances into real outcomes.
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