# llama3

Meta Llama 3 is a family of large language models with pretrained and instruction-tuned 8B and 70B variants.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Run Large Language Models Locally on a Mac with Ollama

DevFeed: [Run Large Language Models Locally on a Mac with Ollama](<https://devfeed.tech/articles/ollama-29002.md>)

Original publisher: [Read original article](<https://nshipster.com/ollama/>)

Author: Mattt (mattt@nshipster.com)

Published: 2025-02-14T08:00:00Z

Content type: tutorial

Language: en

Sources: [NSHipster](<https://devfeed.tech/sources/nshipster.md>)

Topics: [Ollama](<https://devfeed.tech/topics/ollama.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [llama.cpp](<https://devfeed.tech/topics/llama-cpp.md>), [llama3](<https://devfeed.tech/topics/llama3.md>), [Containers](<https://devfeed.tech/topics/containers.md>), [Homebrew](<https://devfeed.tech/topics/homebrew.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [containers](<https://devfeed.tech/tags/containers.md>), [homebrew](<https://devfeed.tech/tags/homebrew.md>), [inference](<https://devfeed.tech/tags/inference.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llama-cpp](<https://devfeed.tech/tags/llama-cpp.md>), [llama3](<https://devfeed.tech/tags/llama3.md>), [llms](<https://devfeed.tech/tags/llms.md>), [miscellaneous](<https://devfeed.tech/tags/miscellaneous.md>), [oci](<https://devfeed.tech/tags/oci.md>), [ollama](<https://devfeed.tech/tags/ollama.md>)

### AI overview

This tutorial explains how to use Ollama to download, run, and manage large language models locally on a Mac. It covers installation, llama3.2, llama.cpp, Modelfiles, OCI-based model distribution, and the practical benefits of local inference, including privacy, cost, latency, control, and reliability.

### Source excerpt

While we wait for Apple Intelligence to arrive on our devices, something remarkable is already running on our Macs. Think of it as a locavore approach to artificial intelligence: homegrown, sustainable, and available year-round.

## Benchmarking Language Model Performance on 5th Gen Xeon at GCP

DevFeed: [Benchmarking Language Model Performance on 5th Gen Xeon at GCP](<https://devfeed.tech/articles/benchmarking-language-model-performance-on-5th-gen-xeon-at-gcp-7290.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/intel-gcp-c4>)

Author: Matrix Yao; Ke Ding; Ilyas Moutawwakil

Published: 2024-12-17T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [Google Cloud Platform (GCP)](<https://devfeed.tech/topics/google-cloud.md>), [intel](<https://devfeed.tech/topics/intel.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [llama3](<https://devfeed.tech/topics/llama3.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [architectures](<https://devfeed.tech/tags/architectures.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [backend](<https://devfeed.tech/tags/backend.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [compute](<https://devfeed.tech/tags/compute.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [gcp](<https://devfeed.tech/tags/gcp.md>), [google](<https://devfeed.tech/tags/google.md>), [google-cloud](<https://devfeed.tech/tags/google-cloud.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [intel](<https://devfeed.tech/tags/intel.md>), [llama3](<https://devfeed.tech/tags/llama3.md>), [llm](<https://devfeed.tech/tags/llm.md>), [performance](<https://devfeed.tech/tags/performance.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>)

### AI overview

This article benchmarks language-model performance on Google Cloud Compute Engine C4 and N2 instances powered by different generations of Intel Xeon processors. It compares text embedding and text generation for agentic AI workloads, focusing on the benefits of Intel Advanced Matrix Extensions (AMX) and CPU-based hosting of systems using small language models such as Meta's 1B and 3B Llama 3.2 models.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## "Llama 3.2 in Keras"

DevFeed: ["Llama 3.2 in Keras"](<https://devfeed.tech/articles/llama-3-2-in-keras-7302.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/keras-llama-32>)

Author: Martin Görner

Published: 2024-10-21T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Keras](<https://devfeed.tech/topics/keras.md>), [llama3](<https://devfeed.tech/topics/llama3.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [tokenization](<https://devfeed.tech/topics/tokenization.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [Tensorflow](<https://devfeed.tech/topics/tensorflow.md>), [Transformers](<https://devfeed.tech/topics/transformers.md>)

Tags: [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [keras](<https://devfeed.tech/tags/keras.md>), [llama3](<https://devfeed.tech/tags/llama3.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llms](<https://devfeed.tech/tags/llms.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [tensorflow](<https://devfeed.tech/tags/tensorflow.md>), [tokenization](<https://devfeed.tech/tags/tokenization.md>), [training](<https://devfeed.tech/tags/training.md>), [transformers](<https://devfeed.tech/tags/transformers.md>)

### AI overview

The article introduces Llama 3.2 support in Keras, explaining that Hugging Face checkpoints can be loaded directly and converted on the fly when needed. It also describes Keras and keras-hub support for multiple backends, pretrained models, tokenization, preprocessing, training, and fine-tuning.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Introducing The AWS AI Stack

DevFeed: [Introducing The AWS AI Stack](<https://devfeed.tech/articles/introducing-the-aws-ai-stack-14082.md>)

Original publisher: [Read original article](<https://www.serverless.com/blog/aws-ai-stack>)

Author: Serverless Team

Published: 2024-09-11T00:00:00Z

Content type: release

Language: en

Sources: [Serverless Blog](<https://devfeed.tech/sources/serverless-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Amazon Web Services](<https://devfeed.tech/topics/aws.md>), [Amazon Bedrock](<https://devfeed.tech/topics/amazon-bedrock.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [AWS Lambda](<https://devfeed.tech/topics/aws-lambda.md>), [Boilerplate](<https://devfeed.tech/topics/boilerplate.md>), [Claude](<https://devfeed.tech/topics/claude.md>), [React](<https://devfeed.tech/topics/react.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>), [Back end](<https://devfeed.tech/topics/backend.md>), [Front end](<https://devfeed.tech/topics/frontend.md>), [llama3](<https://devfeed.tech/topics/llama3.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [aws](<https://devfeed.tech/tags/aws.md>), [aws-lambda](<https://devfeed.tech/tags/aws-lambda.md>), [back-end](<https://devfeed.tech/tags/back-end.md>), [bedrock](<https://devfeed.tech/tags/bedrock.md>), [ci-cd](<https://devfeed.tech/tags/ci-cd.md>), [claude](<https://devfeed.tech/tags/claude.md>), [cloud-computing](<https://devfeed.tech/tags/cloud-computing.md>), [faas](<https://devfeed.tech/tags/faas.md>), [front-end](<https://devfeed.tech/tags/front-end.md>), [function-as-a-service](<https://devfeed.tech/tags/function-as-a-service.md>), [infrastructure-as-code](<https://devfeed.tech/tags/infrastructure-as-code.md>), [llama3](<https://devfeed.tech/tags/llama3.md>), [llms](<https://devfeed.tech/tags/llms.md>), [news](<https://devfeed.tech/tags/news.md>), [react](<https://devfeed.tech/tags/react.md>), [serverless](<https://devfeed.tech/tags/serverless.md>), [serverless-architecture](<https://devfeed.tech/tags/serverless-architecture.md>), [serverless-framework](<https://devfeed.tech/tags/serverless-framework.md>)

### AI overview

The AWS AI Stack is introduced as a full-stack, serverless boilerplate for building AI applications on AWS. It uses Bedrock LLMs including Claude 3.5 Sonnet and Llama3.1, with a React front end, AWS Lambda back end, and built-in CI/CD.

### Source excerpt

Full-stack, serverless, boilerplate for AI applications on AWS, featuring Bedrock LLMs like Claude 3.5 Sonnet and Llama3.1, a React front-end, AWS Lambda back-end, built-in CI/CD and more.