Llama 3.1 70B

Overview

Meta's Llama 3.1 70B, the open-weight model that first felt like a credible GPT-4 alternative. Needs serious hardware — think dual 3090s or an A100.

When to pick this model

• On-prem deployments needing frontier-adjacent quality
• Long-context reasoning and document workloads up to 128k tokens
• Self-hosted alternatives to GPT-4 class APIs
• Multi-GPU inference servers already provisioned for 70B-class models
• Fine-tuning when you need a strong base for domain adaptation

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

• Benchmark-leading quality for open-weight 70B
• 128k context
• Strong reasoning and code generation
• Mature serving stack in vLLM, TGI, llama.cpp

Limitations

• ~40GB VRAM at Q4 — minimum two 24GB GPUs
• Llama Community license restricts use above 700M MAU
• Slower and pricier to serve than Llama 3.3 70B at similar quality

Architecture & training

Architecture: Dense Transformer · 80 layers · GQA

Training: 15T tokens, Meta multilingual corpus.

Quick start

ollama run llama3.1:70b

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