Llama 3.3 70B Instruct

Overview

Meta's Llama 3.3 70B — same quality tier as Llama 3.1 405B at one-sixth the size, thanks to improved post-training. Weights are gated on Hugging Face.

When to pick this model

• Self-hosted alternatives to GPT-4 and Claude APIs
• Long-context reasoning and code on multi-GPU servers
• Production workloads where 405B is too expensive to run
• Domain fine-tuning on a high-quality 70B base
• Enterprise deployments cleared under the Llama Community license

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

• Quality competitive with Llama 3.1 405B
• 128k context window
• Strong reasoning and code performance
• Major efficiency gain vs the 405B model

Limitations

• Hugging Face access is gated — must accept Meta's terms
• Llama Community license restricts use above 700M MAU
• No vision capabilities
• Still needs roughly 40GB VRAM at Q4

Architecture & training

Architecture: Dense · GQA · Llama 3.1 base

Training: Improved post-training vs Llama 3.1 70B.

Quick start

ollama run llama3.3:70b

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