Llama 3.2 3B

Overview

Meta's 3B instruct model with a full 128k context, tuned for laptops, mobile, and edge devices where memory and battery matter.

When to pick this model

• On-device assistants for laptops, phones, or tablets
• CPU-only inference where speed beats raw quality
• Long-context summarization on constrained hardware
• Latency-critical agent loops
• Local autocomplete or text classification

VRAM requirements by quantization

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Published benchmark scores

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

Strengths

• 128k context in a 3B parameter footprint
• Fast CPU inference
• Strong baseline for edge and mobile use cases
• Distilled from larger Llama models for better quality density

Limitations

• Noticeably weaker than 7B+ models on complex tasks
• No vision in this checkpoint
• Subject to Llama Community license terms

Architecture & training

Architecture: Dense Transformer · Llama 3.2 3B · lightweight architecture for edge

Training: Meta multilingual corpus + distillation from larger Llama models.

Quick start

ollama run llama3.2:3b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.