AI Tools Review

NVIDIA: Llama 3.1 Nemotron 70B Instruct

By Nvidia

Released: 2024-10-15

API
LLM
RAG
Nvidia
Paid
New

Nvidia Llama 3 1 Nemotron 70B Instruct, developed by NVIDIA, features 70B parameters and 131k-token context window. NVIDIA's Llama 3.1 Nemotron 70B is a language model designed for generating precise and useful responses. Leveraging [Llama 3.1 70B](/models/meta-llama/llama-3.1-70b-instruct) architecture and Reinforcement Learning from Human Feedback (RLHF), it excels in automatic alignment benchmarks. This model is tailored for applications requiring high accuracy in helpfulness and response generation, suitable for diverse user queries across multiple domains. Usage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/). Premium pricing at $1.2/1M tokens reflects its advanced capabilities.

Visit NVIDIA: Llama 3.1 Nemotron 70B Instruct

AI-Powered

Leverages advanced AI technology to deliver cutting-edge capabilities and results.

Fast & Efficient

Optimized performance ensures quick results without compromising on quality.

Purpose-Built

Specifically designed for llms tasks and workflows.

Nvidia Model Timeline

NVIDIA: Nemotron 3 Nano 30B A3B (free)

256k tokens context

NVIDIA: Nemotron 3 Nano 30B A3B

262k tokens context

NVIDIA: Nemotron Nano 12B 2 VL (free)

128k tokens context

NVIDIA: Nemotron Nano 12B 2 VL

131k tokens context

NVIDIA: Llama 3.3 Nemotron Super 49B V1.5

131k tokens context

NVIDIA: Nemotron Nano 9B V2 (free)

128k tokens context

NVIDIA: Nemotron Nano 9B V2

131k tokens context

NVIDIA: Llama 3.1 Nemotron Ultra 253B v1

131k tokens context

NVIDIA: Llama 3.1 Nemotron 70B InstructCurrent

131k tokens context

Specifications

pricing$1.20 / $1.20 (per 1M)
context Window131k tokens

AI Evaluation

4.8
Expert Rating
Text4.9/5
Coding3.5/5

Features a substantial 131k-token context capacity, balancing extensive document handling with efficient processing.

Pros

  • 131k token context window
  • Large-scale 70B architecture
  • Precise instruction following
  • Optimized for RAG workflows

Cons

  • Moderate API costs
  • API integration required