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Reading list

6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Llama 3 vs Llama 3.1 : Which is Better for Your AI Products?

Hendrix Liu
Hendrix Liu 13 Aug, 2024
5 min read

Introduction

On July 23rd, 2024, Meta released its latest flagship model, Llama 3.1 405B, along with smaller variants: Llama 3.1 70B and Llama 3.1 8B. This release came just three months after the introduction of Llama 3. While Llama 3.1 405B outperforms GPT-4 and Claude 3 Opus in most benchmarks, making it the most powerful open-source model available, it may not be the optimal choice for many real-world applications due to its slow generation time and high Time to First Token (TTFT).

For developers looking to integrate these models into production or self-host them, Llama 3.1 70B emerges as a more practical alternative. But how does it compare to its predecessor, Llama 3 70B? Is it worth upgrading if you’re already using Llama 3 70B in production?

In this blog post, we’ll conduct a detailed comparison between Llama 3.1 70B and Llama 3 70B, examining their performance, efficiency, and suitability for various use cases. Our goal is to help you make an informed decision about which model best fits your needs.

Also Read: Meta Llama 3.1: Latest Open-Source AI Model Takes on GPT-4o mini

Llama 3 70B vs Llama 3.1 70B: Which is Better for Your AI Products?

Overview

  • Llama 3.1 70B: Best for tasks requiring extensive context, long-form content generation, and complex document analysis.
  • Llama 3 70B: Excels in speed, making it ideal for real-time interactions and quick response applications.
  • Benchmark Performance: Llama 3.1 70B outperforms Llama 3 70B in most benchmarks, particularly in mathematical reasoning.
  • Speed Trade-Off: Llama 3 70B is significantly faster, with lower latency and quicker token generation.

Table of Contents

Llama 3 70B vs Llama 3.1 70B

Basic Comparison

Here’s a basic comparison between the two models.

  Llama 3.1 70B Llama 3 70B
Parameters 70 billion 70 billion
Price-Input tokens-Output tokens $0.9 / 1M tokens$0.9 / 1M tokens $0.9 / 1M tokens$0.9 / 1M tokens
Context window 128K 8K
Max output tokens 4096 2048
Supported inputs Text Text
Function calling Yes Yes
Knowledge cutoff date December 2023 December 2023

Key Improvements in the New Model:

  1. Context Window: Llama 3.1 70B – 128K vs Llama 3 70B’s 8K (16-fold increase)
  2. Max Output Tokens: 4096 vs 2048 (doubled)

These significant improvements in context window and output capacity give Llama 3.1 70B a substantial edge in handling longer and more complex tasks, despite both models sharing the same parameter count, pricing, and knowledge cutoff date. The expanded capabilities make Llama 3.1 70B more versatile and powerful for a wide range of applications.

Benchmark Comparison

  Llama 3.1 70B Llama 3 70B
MMLU 86 82
GSM8K 95.1 93
MATH 68 50.4
HumanEval 80.5 81.7

Llama 3.1 70B outperforms its predecessor in most benchmarks, with notable improvements in

  • MMLU (+4 points): The benchmark covers 57 subjects across STEM, the humanities, the social sciences, and more. It ranges in difficulty from an elementary level to an advanced professional level, and it tests both world knowledge and problem-solving ability.
  • MATH (+17.6 points): MATH is a new dataset of 12,500 challenging competition mathematics problems.
  • GSM8K (+2.1 points): GSM8K is a dataset of 8.5K high-quality linguistically diverse grade school math word problems created by human problem writers. The dataset is segmented into 7.5K training and 1K test problems.
  • HumanEval (-1.2 points): This suggests a marginal decrease in coding performance. It consists of 164 original programming problems, assessing language comprehension, algorithms, and simple mathematics, with some comparable to simple software interview questions.

Overall, Llama 3.1 70B demonstrates superior performance, particularly in mathematical reasoning tasks, while maintaining comparable coding abilities.

Speed Comparison

We conducted tests using Keywords AI’s model playground to compare the speed performance of Llama 3 70B and Llama 3.1 70B.

Latency

Llama 3 70B vs Llama 3.1 70B: Latency test

Our tests, consisting of hundreds of requests for each model, revealed a significant difference in latency. Llama 3 70B demonstrated superior speed with an average latency of 4.75s, while Llama 3.1 70B averaged 13.85s. This nearly threefold difference in response time highlights Llama 3 70B’s advantage in scenarios requiring quick real-time responses, potentially making it a more suitable choice for time-sensitive applications despite Llama 3.1 70B’s improvements in other areas.

TTFT (Time to First Token)

Llama 3 70B vs Llama 3.1 70B: TTFT test

Our tests reveal a significant difference in TTFT performance. Llama 3 70B excels with a TTFT of 0.32s, while Llama 3.1 70B lags at 0.60s. This twofold speed advantage for Llama 3 70B could be crucial for applications requiring rapid response initiation, such as voice AI systems, where minimizing perceived delay is essential for user experience.

Throughput (Tokens per Second)

Throughput (tokens per second) comparison

Llama 3 70B demonstrates significantly higher throughput, processing 114 tokens per second compared to Llama 3.1 70B’s 50 tokens per second. This substantial difference in processing speed – more than double – underscores Llama 3 70B’s superior performance in generating text quickly, making it potentially more suitable for applications requiring rapid content generation or real-time interactions.

Performance Comparison

We conducted evaluation tests on the Keywords AI platform. The evaluation comprised three parts:

  • Coding Task: Both models successfully completed frontend and backend development tasks. Llama 3 70B often produced more concise solutions with better readability.
  • Document Processing: Both models achieved high accuracy (~95%) in processing documents ranging from 1 to 50 pages. Llama 3 70B demonstrated significantly faster processing speeds but was limited to documents under 8-10 pages due to its smaller context window. Llama 3.1 70B, while slower, could handle much longer documents.
  • Logical Reasoning: Llama 3.1 70B outperformed Llama 3 70B in this area, solving most problems more effectively and showing superior ability in identifying logical traps.

Model Recommendations

Llama 3.1 70B

  • Best for: Long-form content generation, complex document analysis, tasks requiring extensive context understanding, advanced logical reasoning, and applications that benefit from larger context windows and output capacities.
  • Not suitable for: Time-sensitive applications requiring rapid responses, real-time interactions where low latency is crucial, or projects with limited computational resources that cannot accommodate the model’s increased demands.

Llama 3 70B

  • Best for: Applications requiring quick response times, real-time interactions, efficient coding tasks, processing of shorter documents, and projects where computational efficiency is a priority.
  • Not suitable for: Tasks involving very long documents or complex contextual understanding beyond its 8K context window, advanced logical reasoning problems, or applications that require processing of extensive contextual information.

How to Choose the Best Open LLM?

Self-hosting open-source models has its own strengths, offering complete control and customization. However, it can be inconvenient for developers who want a simpler and more streamlined way to experiment with these models.

Consider using Keywords AI, a platform that allows you to access and test over 200 LLMs using a consistent format. With Keywords AI, you can try all the trending models with a simple API call or use the model playground to test them instantly.

You could easily choose LLMs you want to test, and then open the ‘Compare mode’ to compare different LLMs’ performance (latency, costs……) just like in the following screenshot.

Keywords AI playground

Conclusion

Choosing between Llama 3 70B and Llama 3.1 70B depends on your needs. Llama 3.1 70B is better for complex tasks with more context, while Llama 3 70B is faster for simpler jobs. Think about what matters most for your project – speed or power. You could test both models to see which works best for you on Keywords AI. This LLM-monitoring platform can call 200+ LLMs using the OpenAI format with one API key and get insights into your AI products. With just 2 lines of code, you can build better AI products with complete observability.

advancedAIcomparisonGuideLarge Language ModelsLlama 3Llama 3 70 Bllama 3.1Llama 3.1 70 BLLMsModels
Hendrix Liu
Hendrix Liu 13 Aug, 2024

Co-founder @ Keywords AI (YC W24), the best LLM monitoring platform. Visit: https://keywordsai.co With 2 lines of code, call 200+ LLMs using the same format and get complete LLM observability and user analytics.

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