Best local AI model for email categorization | DevPulse — DevPulse

Recommended models

Three tiers based on your available RAM and quality needs.

Gemma 3 1BBudget Pick

Fast enough for real-time classification with minimal RAM. Perfect for always-on background tasks.

Q8_0 — 1.5 GB

Phi-4 Mini 3.8BBest Overall

Best quality-to-size ratio for classification. Handles nuanced categories and multi-label tagging.

Q4_K_M — 2.5 GB

Qwen 3.5 9BMaximum Quality

Overkill for simple categorization, but handles complex rules and multilingual emails.

Q4_K_M — 5.5 GB

Tips for email categorization

1 Pre-filter by sender, date, and subject before passing to the model — reduces token usage dramatically

2 Use structured output (JSON mode) to get consistent category labels

3 A 1B model with a good prompt often beats a 9B model with a lazy prompt for classification

4 Run classification on a schedule (every 5 minutes) rather than per-email to batch efficiently

Other Use Cases

Related Pages

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