> Source: https://meetrix.io/blogs/unveiling-the-language-revolution-llama-2-meta-ai/ > Markdown copy of that page. Cite the URL above, not this file. LLMs & APIs # Llama AI: Meta's Llama 2 Model, Its Use Cases and How to Run It [By Avindi Nisansala](https://meetrix.io/blogs/authors/avindi-nisansala/) • September 29, 2026 • 12 min read Revised by - ![Portrait of Hiruna Kumara, Senior DevOps Engineer at Meetrix](https://meetrix.io/blog-images/assets/authors/hiruna-kumara.webp)[Hiruna Kumara](https://meetrix.io/blogs/authors/hiruna-kumara/)Senior DevOps Engineer, Meetrix This article was reviewed and refreshed for accuracy. Last reviewed September 2026. When people search for "Llama AI" they usually mean one thing: Meta's family of large language models you can download and run yourself. Llama 2 is the release that made that practical for businesses. It was the first Llama you were allowed to use commercially, and a lot of self-hosted chatbots and internal tools from 2023 and 2024 still run on it. Here's what Llama 2 actually is, how it compares with Llama 3 and 4, what the license lets you do, where it still earns its place in a business, and how to get the 7B or 70B model running on AWS. ## What Is Meta AI Llama? Llama stands for Large Language Model Meta AI. Meta releases the model weights, not just an API, so you can run a Llama model on your own GPU server and keep prompts and data inside your own cloud account. That's the main difference from ChatGPT or Gemini, where the model only lives behind the vendor's API. Llama 2 came out on July 18, 2023, in three sizes (7B, 13B and 70B parameters), each with a chat-tuned version called Llama 2-Chat. Per the [Llama 2 paper](https://arxiv.org/abs/2307.09288), the base models were trained on 2 trillion tokens with a 4,096-token context window, and the chat versions were tuned with supervised fine-tuning and reinforcement learning from human feedback. Meta's current model pages live at [llama.com](https://www.llama.com/). ## The Llama AI Model Family: Where Llama 2 Fits Llama 2 is now two generations behind. Here's the whole line, so the version numbers make sense: | Generation | Released | Sizes | Context window | | --- | --- | --- | --- | | LLaMA (1) | February 2023 | 7B, 13B, 33B, 65B | 2K tokens | | Llama 2 | July 2023 | 7B, 13B, 70B | 4K tokens | | Llama 3 | April 2024 | 8B, 70B | 8K tokens | | Llama 3.1 | July 2024 | 8B, 70B, 405B | 128K tokens | | Llama 3.2 | September 2024 | 1B, 3B (text), 11B, 90B (vision) | 128K tokens | | Llama 3.3 | December 2024 | 70B | 128K tokens | | Llama 4 | April 2025 | Scout, Maverick (mixture of experts) | Up to 10M tokens (Scout) | The 4K context is the limit you feel first with Llama 2. It's enough for a chat turn or a support ticket, not for a 40-page contract. If you're starting fresh, our [Llama 3 developer guide](https://meetrix.io/blogs/llama3-developer-guide/) and [Llama 4 developer guide](https://meetrix.io/blogs/llama-4-developer-guide/) cover the newer models, and our roundup of the [best open source LLMs to self-host](https://meetrix.io/blogs/best-open-source-llms-self-hosted-2026/) compares Llama with Mistral, Qwen and the rest. ## Llama 2 Meetrix Edition: Run 7B or 70B on AWS Without the Setup Pain Running Llama 2 on your own infrastructure normally means wrestling with GPU drivers, model weights, and serving frameworks before you get a single token out. The Meetrix editions on the [Meetrix store](https://aws.amazon.com/marketplace/seller-profile?id=62b53dd8-45d0-4b50-a654-bd6993167486&ref=dtl_prodview-zrea7eq3c4jbe) in the AWS Marketplace skip all of that - pre-configured AMIs that boot straight into a working, OpenAI API compatible endpoint. Two sizes are available, and picking between them mostly comes down to your workload and instance budget: - **[Llama 2 7B AMI](https://aws.amazon.com/marketplace/pp/prodview-f6d2k5vasskwa):** The lighter option. It fits on a single-GPU instance, keeps hourly costs low, and handles chatbots, internal tools, and prototyping comfortably. - **[Llama 2 70B AMI](https://aws.amazon.com/marketplace/pp/prodview-3vkj4msxhjsq4):** The full-size model. It needs beefier GPU instances, but the jump in reasoning quality and response coherence is worth it for production and customer-facing workloads. What you get when the instance boots: the model already loaded, an OpenAI-compatible API endpoint, GPU drivers installed, and hourly billing through AWS Marketplace, so you pay only while the server runs. AWS scans Marketplace AMIs before they're listed, and our team answers if something breaks. The useful part is the API. An app written for OpenAI can point at your own Llama 2 server by changing its base URL, which our guide to [self-hosting an OpenAI-compatible API](https://meetrix.io/blogs/openai-compatible-api-self-hosted/) walks through. ## What the Llama 2 AI Model Does Well, and Where It Struggles Llama 2-Chat is good at the everyday language jobs: answering questions in a conversational tone, summarising, rewriting, drafting, and pulling structured fields out of messy text. The 70B model holds a conversation noticeably better than 7B and makes fewer confident mistakes. It's weaker at maths and code than anything from the Llama 3 line on, and the paper reports its pretraining data was around 90 percent English, so other languages are usable but patchy. Like every LLM it will state wrong facts fluently. For anything factual, pair it with retrieval over your own documents rather than trusting what it remembers. Hardware-wise, the 7B model needs roughly 14 GB of GPU memory at 16-bit precision, which fits a single NVIDIA T4 or A10G. The 70B model needs around 140 GB at 16-bit, so it means a multi-GPU instance or a quantized build. On a new AWS account both need a GPU vCPU quota first, which starts at 0; our [AWS vCPU quota guide](https://meetrix.io/blogs/increase-aws-vcpu-quota/) walks through the request. ## Is Llama 2 Open Source? The License in Plain Terms Not in the strict sense. Llama 2 ships under Meta's own [Llama 2 Community License](https://www.llama.com/llama2/license/): the weights are free to download, and you can use them commercially, fine-tune them and run them on your own servers. But there are conditions an open source license wouldn't have: - Products with more than 700 million monthly active users must get a separate license from Meta. - Use has to follow Meta's acceptable use policy. - You can't use Llama 2 or its outputs to improve other large language models, apart from Llama 2 and its derivatives. So "open weights" is the accurate term. For almost every company the practical answer is the same: yes, you can build a commercial product on it without paying Meta anything. The weights are on [Hugging Face](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) once you accept the license. ## Llama 2 Use Cases for Business Five places Llama 2 still earns its keep. The 7B model covers most of them. The 70B is worth its extra GPU cost where the answer goes straight to a customer. ### Research and NLP experiments Researchers mostly use Llama 2 as a baseline. The weights are public and the paper documents how it was trained, so a result on Llama 2 is one other people can reproduce. It's also handy for labelling text, sentiment tagging and pulling entities out of documents at a volume where paying per API call adds up. One trap: the license bans using Llama 2's output to improve any other large language model. Generating synthetic training data for a Llama 2 fine-tune is fine. Generating it to train a different model family isn't. The [Meetrix Llama 70B](https://aws.amazon.com/marketplace/pp/prodview-3vkj4msxhjsq4) AMI exposes an OpenAI-compatible API, so existing research scripts and NLP tooling can point at it with a base URL change. ### Customer support chatbots Llama 2-Chat handles FAQ-style support well, as long as it answers from your own help docs through retrieval. Left to its memory, it will make up refund policies with total confidence. Keep a clear handover to a human for anything account-specific. For a first-line bot, [Meetrix Llama 7B](https://aws.amazon.com/marketplace/pp/prodview-f6d2k5vasskwa) is enough and runs on a single modest GPU, so the AWS bill stays small. In finance or healthcare, where a wrong answer costs more than a GPU, step up to 70B. ### Developer documentation Drafting docstrings, README sections and changelog entries from a diff is a good fit. Writing the code itself isn't: Llama 2 is noticeably weak at code, and Code Llama or anything from Llama 3 onward does it better. The 4K context also means one file or one diff at a time, not a whole repository. ### Education and e-learning Quiz questions from a lesson, feedback on short written answers, the same explanation rewritten at three reading levels. A teacher still reviews what goes out. Running it on your own server keeps student work off third-party APIs, which matters under GDPR and most school data policies. ### Marketing and social media drafts First drafts, mostly: social posts, product descriptions, five variations of a headline for an A/B test. Paste two or three past posts into the prompt and it picks up the brand voice reasonably well. Somebody still edits before anything is published. [Meetrix Llama 70B](https://aws.amazon.com/marketplace/pp/prodview-3vkj4msxhjsq4) gets the tone closer on the first try. [Meetrix Llama 7B](https://aws.amazon.com/marketplace/pp/prodview-f6d2k5vasskwa) is the cheaper pick for bulk drafts, and it's up within minutes of launching the AMI. ## Should You Still Run Llama 2? If you have a pipeline that was built and tested on Llama 2, keep it. It's a well-understood model, and swapping models means re-testing every prompt. The [Llama 2 7B AMI](https://aws.amazon.com/marketplace/pp/prodview-f6d2k5vasskwa) suits cost-sensitive and internal workloads, and the [Llama 2 70B AMI](https://aws.amazon.com/marketplace/pp/prodview-3vkj4msxhjsq4) suits work where output quality comes first. Starting from scratch? Look at Llama 3.1 8B first. It runs on the same GPU as Llama 2 7B, reads documents 32 times longer, and handles code and other languages better. If you have the bigger GPUs, Llama 4 is the step after that. Llama 2 isn't a bad choice. It just isn't the default any more. ## Frequently Asked Questions What is Llama AI? Llama is Meta's family of large language models. The name comes from Large Language Model Meta AI. The weights are downloadable, so you can run the models on your own servers. The line runs from the first LLaMA in 2023 to Llama 4 in 2025. When was Llama 2 released? July 18, 2023, in 7B, 13B and 70B sizes, each with a chat-tuned version. Unlike the first LLaMA, which was research-only, Llama 2 allowed commercial use from day one. Is Llama 2 free for commercial use? Yes, under the Llama 2 Community License. There's no fee, but products with more than 700 million monthly active users need a separate license from Meta, and Meta's acceptable use policy applies. What are the main Llama 2 use cases for business? Support chatbots, summarising documents and tickets, drafting marketing or internal copy, classification, and question answering over company documents with retrieval. The 7B model handles most of these; 70B is for customer-facing answers where quality matters. Should I use Llama 2 or a newer Llama model? For a new project, a newer model. Llama 3.1 8B is stronger than Llama 2 7B at a similar size and reads 128K tokens instead of 4K. Llama 2 still makes sense when an existing pipeline was built and tested around it. What GPU does Llama 2 need? The 7B model at 16-bit precision needs roughly 14 GB of GPU memory, so a single 16 GB or 24 GB card works. The 70B model needs about 140 GB at 16-bit, which means several GPUs or a quantized build. ## Keep Reading [ ### How to Install Llama 2 on AWS via Pre-configured AMI in a Single Click Deploy Meta's Llama 2 on AWS in a single click using Meetrix's pre-configured AMI - fast, secure, and cost-efficient AI infrastructure without the manual setup. By Kelum Sampath • Meetrix.io ](https://meetrix.io/blogs/how-to-install-llama-2/) [ ### Llama 2 - Developer Guide Discover how to install and configure Llama 2 on AWS with our comprehensive developer guide. From initial setup to advanced configurations, this guide equips you with everything you need to successfully deploy Llama 2 and maximize its potential for your applications. By Dinesh Chathuranga • Meetrix.io ](https://meetrix.io/blogs/llama2-developer-guide/) ### Additional Resources - References - Complete Llama 2 Setup Guide: [Video Tutorial](https://youtu.be/yg8A8I_E7LE?si=i5HoPnasZIM65xQZ) - Llama 2 GitHub Repository: [Link to GitHub](https://github.com/meta-llama/llama) Meetrix Store Coturn TURN/STUN for WebRTC, no per-minute relay fees [Deploy it](https://meetrix.io/store/coturn/) Meetrix Store New Deploy what this guide covers, pre-configured. - [Coturn TURN/STUN for WebRTC, no per-minute relay fees](https://meetrix.io/store/coturn/) - [Jitsi Meet Self-hosted video calls for 50 to 500 users](https://meetrix.io/store/jitsi-meet/) - [RustDesk Remote desktop AMI, a TeamViewer alternative](https://meetrix.io/store/rustdesk/) - [OpenVPN Encrypted remote access, no per-user fees](https://meetrix.io/store/openvpn/) - [Plane Issues, cycles and roadmaps, a Jira alternative](https://meetrix.io/store/plane/) - [Mailcow Business email on your own domain](https://meetrix.io/store/mailcow/) [Browse all products](https://meetrix.io/store/)