Best Local LLM Models for Q2 2026

The open-source model landscape has changed dramatically in just three months. Qwen 3 brought MoE to the masses, Gemma 4 set new quality benchmarks under 10GB, and Llama 4 Scout broke the context window ceiling. Here’s how they compare for local deployment — and which one you should pick.

flowchart TD
    START["What is your primary task?"] --> CODE{"Code generation?"}
    START --> OFFICE{"Office assistant\n(emails, docs, Q&A)?"}
    START --> REASON{"Complex reasoning\nor math?"}
    START --> DOCS{"Massive documents\n(contracts, research)?"}
    START --> QUALITY{"Maximum quality\n(no hardware limits)?"}

    CODE -->|Yes| CODER["Qwen 2.5 Coder 7B\n4.7 GB VRAM — 27 tok/s"]
    OFFICE --> LANG{"Need multilingual\n(Spanish, etc.)?"}
    LANG -->|Yes| QWEN["Qwen 3 8B\n4.9 GB VRAM — 22 tok/s"]
    LANG -->|No| GEMMA["Gemma 4 E4B\n5.8 GB VRAM — 20 tok/s"]
    REASON -->|Yes| PHI["Phi-4 14B\n8.5 GB VRAM — 15 tok/s"]
    DOCS -->|Yes| LLAMA["Llama 4 Scout 109B\n35 GB VRAM — 10M context"]
    QUALITY -->|Yes| DS["DeepSeek V3.2 671B\n~22 GB VRAM — Near-GPT-4"]

    style START fill:#DBEAFE,stroke:#2563EB,color:#000
    style CODER fill:#D1FAE5,stroke:#059669,color:#000
    style QWEN fill:#D1FAE5,stroke:#059669,color:#000
    style GEMMA fill:#D1FAE5,stroke:#059669,color:#000
    style PHI fill:#FEF3C7,stroke:#F5A623,color:#000
    style LLAMA fill:#FECACA,stroke:#B91C1C,color:#000
    style DS fill:#FECACA,stroke:#B91C1C,color:#000

The Contenders

Model	Params	VRAM (Q4)	Speed (M4)	Strength
Qwen 3 8B	8B	4.9 GB	~22 tok/s	Best multilingual (40+ languages)
Gemma 4 E4B	9.6B	5.8 GB	~20 tok/s	Best quality under 10GB
Phi-4	14B	8.5 GB	~15 tok/s	Best reasoning/math
Llama 4 Scout	109B (17B active)	35 GB	~8 tok/s	10M token context window
DeepSeek V3.2	671B (37B active)	~22 GB	~12 tok/s	Near-GPT-4 reasoning
Qwen 2.5 Coder 7B	7.6B	4.7 GB	~27 tok/s	Best code generation

All available via ollama pull [model]. All run on a Mac Mini M4 (24GB).

Our Pick by Use Case

For a Spanish SME office assistant

Winner: Qwen 3 8B

Why: native Spanish support (40+ languages), runs comfortably on 24GB hardware at 22 tok/s, Apache 2.0 license for commercial use. Handles email drafting, customer Q&A, document summaries, and internal queries without breaking a sweat.

ollama pull qwen3:8b

For code generation and technical work

Winner: Qwen 2.5 Coder 7B

Why: purpose-built for code, fits in 4.7GB, runs at 27 tok/s. Supports Python, JavaScript, TypeScript, SQL, and 20+ languages. Outperforms models twice its size on coding benchmarks.

ollama pull qwen2.5-coder:7b

For complex reasoning and analysis

Winner: Phi-4 (14B)

Why: Microsoft’s Phi-4 punches far above its weight — 84.8% on MATH benchmark, beating many 70B models. Needs 16GB RAM but delivers exceptional reasoning for strategy documents, legal analysis, and financial modeling.

ollama pull phi4

For maximum quality (when you have 48GB+)

Winner: DeepSeek V3.2

Why: MoE architecture activates only 37B of 671B parameters per token. Near-frontier quality at fraction of the compute. Best for complex research, multi-step analysis, and content where quality matters more than speed.

For massive documents (contracts, research papers)

Winner: Llama 4 Scout

Why: 10 million token context window — the largest ever. Can process entire legal codebooks, research paper collections, or multi-year financial records in a single prompt. Needs 48GB+ RAM.

Hardware Requirements at a Glance

Your Hardware	Best Model	What You Can Do
8GB RAM (Jetson Orin Nano)	Qwen 2.5 3B	Basic Q&A, classification
24GB RAM (Mac Mini M4)	Qwen 3 8B or Gemma 4 E4B	Full office assistant
48GB RAM (Mac Mini M4 Pro)	Phi-4 14B or DeepSeek V3.2	Complex reasoning
128GB RAM (M5 Ultra / AGX Thor)	Llama 4 Scout 109B	Enterprise-grade

Quick-Start Tip

If you’re deploying your first local model, start with Ollama — it handles downloading, quantization, and serving in a single command. Install it from ollama.com, then run ollama pull qwen3:8b. Within five minutes you’ll have a production-ready model answering queries on localhost:11434. From there, connect it to n8n for workflow automation or build a simple RAG pipeline for your internal documents.

The Bottom Line

For 90% of SME use cases, Qwen 3 8B on a Mac Mini M4 is the sweet spot. It costs EUR 920 once (hardware) + EUR 0/month (inference) vs EUR 200-2,000/month for equivalent cloud API usage.

The gap between local and cloud models has effectively closed for business tasks. Save your money — run it locally.

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Related reading

• Cloud vs Local AI: Real Cost Analysis for Spanish SMEs in 2026
• DeepSeek R1: The Best Open-Source Reasoning Model You Can Run Locally
• Local AI Readiness Checklist: Is Your Business Ready to Run AI On-Premise?

Related resources

• 50 AI Models catalog — browse all models with VRAM and install commands
• Hardware catalog — 17 devices from EUR 200 to cloud GPU
• Software stack — Ollama, MLX, and the tools we use
• ROI Calculator — compare local vs cloud costs for your usage
• Contact — need help choosing and deploying?

Sources: Ollama Library · Open LLM Leaderboard