Llama 3.1 8B

Overview

Meta's Llama 3.1 8B, the open-weight benchmark of 2024. A 128k context, well-behaved instruction follower with the largest ecosystem in the open-source world.

When to pick this model

• General-purpose chat or assistant deployments on a single consumer GPU
• Long-context RAG up to 128k tokens
• Production workloads needing the most mature open-weight tooling
• Fine-tuning baselines for downstream tasks
• Drop-in replacement for Mistral 7B with longer context

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 window
• Strong instruction following and coding
• Enormous ecosystem of fine-tunes and integrations
• Solid quality-to-size ratio

Limitations

• Beaten by Qwen 3 8B on most 2025 benchmarks
• No vision in this checkpoint
• Llama Community license restricts use above 700M MAU

Architecture & training

Architecture: Dense Transformer · 32 layers · GQA · Llama 3.1 8B

Training: 15T multilingual tokens from Meta. Instruction-following fine-tuning.

Quick start

ollama run llama3.1:8b

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