AI Agent Models for Local Deployment: Gemma 4 12B, Holo 3.1, and Mellum 2 — June 2026 Guide

Three major models dropped in June 2026 — and they share a powerful theme: local AI agent deployment. Google's Gemma 4 12B, H Company's Holo 3.1 family, and JetBrains' Mellum 2 are all designed to run on your own hardware, not in the cloud. Here's what each brings to the table and which one you should use for your specific agent use case.

1. Google Gemma 4 12B: The Multimodal Local Agent

Google released Gemma 4 as an encoder-free multimodal model — meaning it processes images natively without a separate vision encoder. This is a first for its parameter class.

Architecture highlights:

Best use cases for agents:

Hardware reality: The 12B fits comfortably on consumer 16GB cards. With 4-bit quantization it runs on 8GB (RTX 4060, Apple M-series with 16GB unified memory).

Trade-off: No MoE means more active parameters per token = slower inference than Mellum 2. But you get true multimodality without a separate vision pipeline.

2. H Company Holo 3.1: The Computer-Use Agent Family

H Company (backed by Eric Schmidt's First Connect) launched Holo 3.1 as a family of computer-use agent models ranging from 0.8B to 35B parameters. These are specialized architectures for GUI automation.

Architecture highlights:

Best use cases for agents:

Hardware reality: The 3B runs on any modern CPU + 8GB RAM (no GPU needed). The 12B needs 8GB VRAM. The 35B needs 24GB VRAM (RTX 4090). The 0.8B runs on Android phones.

Trade-off: Holo 3.1 is purpose-built for computer use — not a general-purpose LLM. You wouldn't use it for creative writing or complex reasoning. It is, however, the best option for GUI automation agents today.

3. JetBrains Mellum 2: The Coding Agent Engine

JetBrains open-sourced Mellum 2 as a 12B Mixture-of-Experts coding model with only 2.5B active parameters per token. Trained on 10 trillion tokens of code and technical content.

Architecture highlights:

Best use cases for agents:

Hardware reality: Only 2.5B active parameters means this flies on consumer hardware. Runs at 50+ tokens/sec on a single RTX 4060 (8GB). Runs comfortably on Apple Silicon (MacBook Air M-series).

Trade-off: Strictly a code model. It won't answer general knowledge questions, generate marketing copy, or understand images. But for coding agents, it's arguably the best open-weight option available.

Comparison Table

Which Model Should You Choose?

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Choose Gemma 4 12B if:

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Choose Holo 3.1 if:

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Choose Mellum 2 if:

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The Hybrid Agent Stack

For a complete local agent system, consider running all three:

Combine them with a lightweight agent orchestrator (like CrewAI or LangGraph running locally) and you have a fully self-hosted agent workforce — zero cloud costs, zero data egress.

The Bigger Picture: Why Local Agent Models Matter Now

June 2026 marks a turning point. For the first time, there are practical, purpose-built agent models that run on consumer hardware:

• •Gemma 4 12B closes the multimodal gap for local deployment


• •Holo 3.1 proves that GUI automation agents can run entirely offline


• •Mellum 2 shows that MoE architectures make coding agents blazing fast on ordinary GPUs

For developers, startups, and enterprises in Asia — where data sovereignty laws (China's CSL, India's DPDP Act, Vietnam's PDPD) make cloud-only agent architectures risky — these models are a game changer. You can now deploy capable agent systems without sending data to any US or Chinese cloud provider.

Try These Models

All three models are open-weight and free to use. Here's where to get started:

• •Gemma 4 12B: `ollama pull gemma4:12b` — or download from [Hugging Face](https://huggingface.co/google)


• •Holo 3.1: Download from [Hugging Face — HCompany](https://huggingface.co/HCompany)


• •Mellum 2: `ollama pull mellum2:12b` — or grab from [JetBrains GitHub](https://github.com/JetBrains)

📖 See also: [Local AI Models vs Cloud: What's Best for Asian Businesses in 2026?](/blog/local-ai-models-vs-cloud-which-is-best-for-asia)

📖 See also: [Best AI Coding Assistants 2026: Comprehensive Comparison](/blog/best-ai-coding-assistants-2026-comparison)

📖 See also: [Building Multi-Agent Systems for Production](/blog/multi-agent-systems-production-2026)

— The Apifeny AI Team




Try Ollama (local models) free →  |  Try Hugging Face free →  |  Try GitHub free →  |  Try JetBrains IDEs free →