Llama is a series of open source large language models released by Meta. It is currently the most widely used open weight AI basic model. Llama 3.1 provides three parameter sizes of 8B/70B/405B, supports 128K Token context, and 8 languages. It can be used for commercial purposes under the Meta Llama license (free under 700M MAU), and has been downloaded more than 300 million times on HuggingFace.

Llama — Meta open source large language model series

Core parameters and statistics

Parameters	Details
Latest major version	Llama 3.3 70B / Llama 3.2 (including visual version) / Llama 3.1 405B
Parameter scale options	1B, 3B, 8B, 11B (visual), 70B, 90B (visual), 405B
Maximum context window	128K Token (Llama 3.1/3.2 series)
Multi-language support	8 languages (English, Chinese, French, German, Italian, Portuguese, Spanish, Indian)
License	Meta Llama License (<700M MAU, free for commercial use, application required for more than 700M MAU)
Release channels	HuggingFace, llama.meta.com, major cloud service providers
HuggingFace downloads	Over 300 million times (cumulative in 2024)
Variant models	Code Llama (code), Llama Guard (security filtering), Llama 3.2 Vision
Quantization support	GGUF (llama.cpp), GPTQ, AWQ and other quantization formats
Inference framework	llama.cpp, vLLM, HuggingFace Transformers, Ollama, etc.

The Llama series is one of the most important forces driving the democratization of open source AI - by opening up large-scale pre-trained model weights, Meta enables global developers to run, fine-tune and deploy highly competitive large language models locally on their own hardware without relying on API calls, fundamentally changing the development landscape of AI applications.

User and market recognition

The Llama series is currently the most downloaded and widely used open source LLM family. It has been downloaded more than 300 million times on HuggingFace and is a veritable "open source AI infrastructure". The release rhythm from Llama 2 to Llama 3 proves Meta’s continued investment in the field of open source LLM. Each new version release triggers extensive benchmarking discussions and application practice sharing in the technical community.

In terms of commercial applications, the Llama series is one of the preferred open source basic models for enterprises to build private AI systems. It is especially favored by financial, medical and government institutions with strict data privacy requirements - open weight means that it can be deployed completely offline and the data does not leave the corporate intranet. In the AI ​​development community, an extremely rich ecosystem has formed around Llama, including quantification tools (llama.cpp), fine-tuning frameworks (Axolotl, LLaMA-Factory), conversational interfaces (Open WebUI) and inference engines (vLLM, Ollama). This ecological positive feedback further strengthens Llama's dominant position.

Cost advantage

Method	Price	Main applicable scenarios	Features
Local deployment (llama.cpp)	Hardware cost (one-time)	Personal, research, privacy applications	Completely free, data does not leave the local area
Cloud reasoning (Together AI)	$0.20/million Token (8B)	Quick access to AI applications	Pay-as-you-go billing, no need to manage hardware
Cloud reasoning (Groq)	$0.05/million Token (8B)	Low latency and high frequency calls	Ultra-low latency, suitable for real-time applications
Self-hosted (vLLM + GPU)	GPU hardware/cloud costs	Enterprise high-concurrency production deployments	Maximum flexibility, fully customizable

Compared with GPT-4 Turbo ($10/million Tokens): Llama 3.1 70B performs close to or equivalent in multiple benchmark tests, while the cost of calling through Together AI is only $0.88/million Tokens, which is about 90% lower in price; local deployment requires only one-time hardware investment and no ongoing API costs.

Main functions

• Llama 3.1 8B/70B/405B multiple scale options: from lightweight 8B (can run on consumer-grade GPU) to flagship 405B (comprehensive performance benchmarked against GPT-4o), meeting different computing power budgets and performance needs, users can choose the optimal cost-effective scale according to the scenario.
• 128K Token long context processing: Llama 3.1 series expands the context window to 128K Token, supporting the processing of ultra-long documents, code base analysis and multiple rounds of in-depth conversations, allowing Llama to enter the long context application market that only a few LLMs previously supported.
• Multi-language capabilities (8 languages): Llama 3.1 natively supports Chinese, French, German, Italian, Portuguese, Spanish and Hindi in addition to English, enabling multi-language conversation and text processing without the need for a translation middle layer.
• Code Llama (code-specific variant): Llama variant optimized for code understanding, generation and debugging tasks. It supports multiple mainstream programming languages ​​such as Python, Java, C++, TypeScript, etc., and is an important choice for open source code large models.
• Llama Guard (security filtering model): A content security classification model trained based on the Llama architecture. It is used to detect and filter harmful content in AI conversations, helping developers build AI applications that comply with security standards. It can be integrated as an independent component.
• Llama 3.2 Vision (multimodal version): A multimodal Llama variant (11B and 90B) that supports image understanding, can handle mixed input of images and text, and perform visual tasks such as image description, visual question answering, and document image understanding.
• Open weights can be fine-tuned: The complete model weights can be downloaded, supporting full parameter fine-tuning (Full Fine-tuning) or parameter efficient fine-tuning (LoRA, QLoRA) on own data, injecting domain knowledge into the model to create exclusive industry models.
• Quantized version support (low power devices): 7B/8B quantized models can be run at acceptable speeds on Apple Silicon Macs (such as M2 MacBook Pro) or consumer GPUs via the GGUF quantized format (llama.cpp),

Greatly expands the range of hardware that can run Llama.

Model and version evolution

Version	Release Date	Description
Llama 1 (7B/13B/33B/65B)	2023-02	The first Llama series, for research use only, no commercial use allowed
Llama 2 (7B/13B/34B/70B)	2023-07-18	Open commercial license for the first time, release Chat dialogue optimized version, milestone release
Code Llama	2023-08-24	Code-specific model based on Llama 2, supporting code completion and generation
Llama 2 Long	2023-09	Extend the Llama 2 context to 32K, oriented to long document scenarios
Llama 3 (8B/70B)	2024-04-18	The overall performance is greatly improved, 8K context, surpassing the Gemma/Mistral model with the same parameter scale
Llama 3.1 (8B/70B/405B)	2024-07-23	Flagship version, 128K context, 405B benchmarked GPT-4o, multi-language support, fully commercial
Llama 3.2 (1B/3B/11B/90B)	2024-09-25	Added lightweight 1B/3B end-side model and 11B/90B visual language model
Llama 3.3 70B	~2024-12	70B performance has been greatly improved, close to the 405B level, the most cost-effective open source instruction model

Technical advantages

Deployment flexibility brought by open weight: The core value of Llama lies in "open weight" - unlike closed source models that provide API calls, Llama allows users to download the complete model file and run it on any hardware, with no limit on the number of calls, no data transmission risk, and no API dependency risk. This feature is particularly important for companies that are sensitive to data security and is the technical foundation for building a fully autonomous and controllable AI system.

Powerful open source ecosystem: The most complete tool chain in the current open source LLM ecosystem has been formed around Llama: llama.cpp implements vLLM for local inference on consumer-grade hardware, implements production-grade high-throughput inference, Ollama provides a one-click local deployment experience, LLaMA-Factory and Axolotl provide convenient fine-tuning tools, and Open WebUI provides a beautiful conversational interface. This ecological positive feedback loop enables Llama's usability to continue to outperform similar open source models.

Meta-level training resources and security investment: The Llama series is the core research result of hundreds of researchers at the Meta AI Research Institute (FAIR). The scale of training data (Llama 3.1 uses 15 trillion Tokens) and investment in security alignment research far exceed that of most open source projects, making Llama competitive close to top closed-source models in terms of security and comprehensive capabilities.

The ultimate cost-effectiveness of Llama 3.3 70B: Llama 3.3 70B compresses most of the instruction following capabilities of the 405B model to 70B parameters, greatly lowering the deployment threshold of high-quality open source LLM - the 70B quantized version can run efficiently on a single H100 or 2×A100, saving more than 80% of GPU resources compared to 405B, and is the current open source LLM The pinnacle of value for money.

How to use

Entrance	Description
HuggingFace	Download model weights at https://huggingface.co/meta-llama (access permission required)
Ollama (the simplest local deployment)	Install Ollama: ollama pull llama3.1:8b, one command to complete local deployment
Meta official website application	Visit https://llama.meta.com/llama-downloads to submit a commercial use application
Cloud API	Accessed through service provider APIs such as Together AI, Groq, AWS Bedrock, etc.
vLLM production inference	Deploy high-throughput production inference services using the vLLM framework

Typical usage steps (Ollama local rapid deployment Llama 3.1 8B):

1. Visit https://ollama.com to download and install Ollama (supports macOS/Linux/Windows).
2. Run in the terminal: ollama run llama3.1 (automatically download 8B model, about 4.7GB).
3. After the download is completed, start the conversation directly in the terminal, or integrate it into the application through the local API (http://localhost:11434).
4. A larger model is required: ollama run llama3.1:70b (requires at least 40GB video memory or supports Apple M2 Max/Ultra).
5. Fine-tuning is required: Go to HuggingFace to apply for access, download the original weights, and use LLaMA-Factory or Axolotl framework for LoRA fine-tuning.

Product Pricing

The Llama series model weights are completely free and open, but different usage scenarios have different cost structures:

• Model weight download: completely free. You can download it freely after applying on the HuggingFace or Meta official website. It is free for commercial use with less than 7 million monthly active users (MAU < 700M). If it exceeds, you need to apply for a special license from Meta.
• Local Deployment (llama.cpp/Ollama): Only hardware cost, no ongoing fees, RTX 3090 (24GB) can run 13B quantized model smoothly, Apple M2 MacBook Pro can run 7B/8B quantized model.
• Cloud Inference (Third Party Service): Accessed through Together AI (from $0.20/million Tokens), Groq (from $0.05/million Tokens), AWS Bedrock, etc., pay per usage.
• Self-hosted vLLM: GPU server or cloud GPU instance cost, production-grade 8B models typically require an A10G or RTX 4090-class GPU, pricing varies based on concurrency and cloud provider used.
• Meta official API (Meta AI): Meta provides Llama capabilities through its own products, and some API functions are open to partners.

Application scenarios

1. Enterprise privatized AI assistant deployment: In data security-sensitive industries such as finance, medical care, and law, Llama 3.1 70B is deployed on the enterprise intranet as an internal knowledge base Q&A, file analysis, and code assistance tool. The data does not leave the enterprise network at all, solving data compliance issues when using cloud APIs such as ChatGPT/Claude.

2. Open source LLM research and benchmarking: AI researchers use Llama as a research benchmark model, based on which they conduct academic research on new fine-tuning methods, alignment techniques, and inference optimization. Open weights enable experimental reproducibility and research community collaboration, and are the core open infrastructure in the NLP research field.

3. Domain-specific model fine-tuning: Enterprises and research institutions conduct supervisory fine-tuning and instruction alignment based on Llama 3.1 8B/70B, and inject professional domain knowledge such as medical knowledge, legal provisions, and enterprise data into the model to build highly specialized domain AI assistants at a relatively low cost.

4. Code generation and development assistance: Use Code Llama or Llama variants fine-tuned with code data to locally deploy code completion, code explanation and bug repair assistants to protect code intellectual property from being transmitted through cloud services such as GitHub Copilot, which is suitable for software companies with sensitive intellectual property rights.

5. AI application prototype and education: Developers and students can use Ollama + Llama to quickly build LLM experiment environments locally. The feature of no API call cost makes a large number of interactive experiments possible. It is an ideal low-threshold platform for learning and teaching LLM application development.

Applicable people

• Enterprises with strict data privacy requirements: Financial, medical, and government institutions that need to deploy AI capabilities locally/intranet and cannot send data to third-party APIs.
• AI Researchers: NLP researchers who need a fine-tunable, reproducible, and accessible base model with internal weights for academic research.
• AI application developers: AI product developers who want to get rid of API dependencies, reduce inference costs, or need models to be fully autonomous and controllable.
• Students and AI Learners: Free open weights make world-class LLM accessible to any learner with a GPU and are an important open resource for AI education.
• Unsuitable scenarios: Non-technical users who have strong demand for "out-of-the-box operation without any settings" (it is easier to use ChatGPT/Claude directly); need the latest knowledge (Llama has a training deadline, which is not as real-time as the cloud API knowledge); severely limited computing power (quantified 8B model runs extremely slowly on the CPU); scenarios that require top-notch GPT-4 level capabilities but do not have enough GPU to run the 405B model (cloud API is more practical).

Summary and Outlook

The continued release of the Llama series has been one of the most important drivers of the open source AI movement in recent years. From the research exclusive use of Llama 1, to the commercial opening of Llama 2, to the technological maturity of Llama 3.1 405B, which challenges GPT-4 head-on, Meta has built the world's largest ecosystem of open source LLM users and contributors with an open strategy, forming a community moat that is difficult to replicate with commercial closed-source models.

The release of Llama 3.3 70B further verifies the feasibility of the technical direction of "more efficiently distilling large model capabilities into small models", indicating that open source LLM will continue to narrow the gap with closed source models in terms of cost performance.

Follow-up focus: The release time and performance of the Llama 4 series (expected to adopt the hybrid expert architecture MoE), the further enhancement of multi-modal capabilities (an extension of the Llama 3.2 Vision roadmap), the optimized deployment of Llama end-side models (1B/3B) on mobile devices, and how Meta maintains commercial competitiveness while maintaining openness will be the core highlights of the next phase of the Llama ecosystem.

Related tools: , replicate

Version Info

• Llama 3.1 (8B/70B/405B) ：Llama 3.1 is the most important milestone version released by Meta to date. It launched the 405B flagship model for the first time (comprehensive performance benchmarking GPT-4o and Claude 3.5 Sonnet), the context window was expanded to 128K Tokens, 8 new multi-language support was added, all scale models are open for commercial use (less than 700 million monthly active users), and the number of downloads on the first day of HuggingFace set a historical record for open source models. （2024-07-23）
• Llama 2（7B/13B/70B） ：Llama 2 was officially released, with a commercial use license for the first time, providing four parameter sizes of 7B/13B/34B/70B, and simultaneously releasing a variant version of Chat (Llama 2 Chat) optimized for dialogue. It became the most downloaded open source LLM at that time, which greatly promoted the prosperity of the open source AI application development ecosystem, and the open source AI community entered an accelerated development stage. （2023-07-18）
• Llama 3.3 70B ：Llama 3.3 70B was released. While maintaining the 70B parameter scale, it improved the instruction following ability to a level close to Llama 3.1 405B, further optimizing the performance/price ratio; its multi-language capabilities were significantly improved, and its mathematics and code generation benchmark scores improved significantly. It has become an open source instruction model with the best overall cost performance and is widely used to replace Llama 3.1 405B to reduce deployment costs. （~2024-12）