Llama 3.1 Nemotron 70B

Overview

NVIDIA's RLHF tune of Llama 3.1 70B that topped Arena Hard at 85.0 at release. Strong alignment and instruction-following on familiar Llama foundations.

When to pick this model

• Instruction-heavy chat assistants needing strong alignment
• Deployments already standardized on the Llama 3.1 family
• Workloads where human-preference alignment beats raw benchmarks
• NVIDIA-stack deployments leveraging NIM and TensorRT-LLM

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

• Arena Hard 85.0 — topped the leaderboard at release
• AlpacaEval 2 LC 57.6
• MT-Bench 8.98
• Strong RLHF on real human preference data

Limitations

• Llama 3.1 Community License with MAU clause
• Hugging Face gated access
• Now overtaken on reasoning by Qwen 2.5 72B and R1 distills
• ~42GB at Q4 — needs dual 24GB GPUs

Architecture & training

Architecture: Dense Llama 3.1 70B · intensive NVIDIA RLHF

Training: RLHF on human preferences.

Quick start

ollama run nemotron:70b

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