Llama 4 Maverick 17B 128E Instruct FP8
Version: 2
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Key capabilities
About this model
For vision, Llama 4 models are also optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The Llama 4 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation.Key model capabilities
- Visual recognition
- Image reasoning
- Captioning
- Answering general questions about an image
- Synthetic data generation
- Distillation
- Multilingual text generation
- Code generation
- Natural language generation
Use cases
See Responsible AI for additional considerations for responsible use.Key use cases
Llama 4 is intended for commercial and research use in multiple languages. Instruction tuned models are intended for assistant-like chat and visual reasoning tasks, whereas pretrained models can be adapted for natural language generation. For vision, Llama 4 models are also optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The Llama 4 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 4 Community License allows for these use cases.Out of scope use cases
Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 4 Community License. Use in languages or capabilities beyond those explicitly referenced as supported in this model card.Pricing
Pricing is based on a number of factors, including deployment type and tokens used. See pricing details here.Technical specs
The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality.| Model Name | Training Data | Params | Input modalities | Output modalities | Context length | Token count | Knowledge cutoff |
|---|---|---|---|---|---|---|---|
| Llama 4 Scout (17Bx16E) | A mix of publicly available, licensed data and information from Meta's products and services. This includes publicly shared posts from Instagram and Facebook and people's interactions with Meta AI. Learn more in our Privacy Center . | 17B (Activated) 109B (Total) | Multilingual text and image | Multilingual text and code | 10M | ~40T | August 2024 |
| Llama 4 Maverick (17Bx128E) | 17B (Activated) 400B (Total) | Multilingual text and image | Multilingual text and code | 1M | ~22T | August 2024 |
Training cut-off date
August 2024Training time
Model pre-training utilized a cumulative of 7.38M GPU hours of computation on H100-80GB (TDP of 700W) type hardware. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.| Model Name | Training Time (GPU hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
|---|---|---|---|---|
| Llama 4 Scout | 5.0M | 700 | 1,354 | 0 |
| Llama 4 Maverick | 2.38M | 700 | 645 | 0 |
| Total | 7.38M | - | 1,999 | 0 |
Input formats
Multilingual text and imageOutput formats
Multilingual text and codeSupported languages
Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese.Sample JSON response
The provider has not supplied this information.Model architecture
The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality.Long context
Llama 4 Scout supports up to 10M context length and Llama 4 Maverick supports up to 1M context length.Optimizing model performance
The Llama 4 Scout model is released as BF16 weights, but can fit within a single H100 GPU with on-the-fly int4 quantization; the Llama 4 Maverick model is released as both BF16 and FP8 quantized weights. The FP8 quantized weights fit on a single H100 DGX host while still maintaining quality. Meta provides code for on-the-fly int4 quantization which minimizes performance degradation as well.Additional assets
For more technical information about generation parameters and recipes for how to use Llama 4 in applications, please go here .Training disclosure
Training, testing and validation
Llama 4 Scout was pretrained on 40 trillion tokens and Llama 4 Maverick was pretrained on 22 trillion tokens of multimodal data from a mix of publicly available, licensed data and information from Meta's products and services. This includes publicly shared posts from Instagram and Facebook and people's interactions with Meta AI.Distribution
Distribution channels
The provider has not supplied this information.More information
Instructions on how to provide feedback or comments on the model can be found in the Llama README . For more technical information about generation parameters and recipes for how to use Llama 4 in applications, please go here .Responsible AI considerations
Safety techniques
The primary objective of conducting safety fine-tuning is to offer developers a readily available, safe, and powerful model for various applications, reducing the workload needed to deploy safe AI systems. Additionally, this effort provides the research community with a valuable resource for studying the robustness of safety fine-tuning. Fine-tuning dataMeta employs a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. Meta has developed many large language model (LLM)-based classifiers that enable them to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals
Building on the work Meta started with our Llama 3 models, Meta put a great emphasis on driving down model refusals to benign prompts for Llama 4. Meta included both borderline and adversarial prompts in Meta's safety data strategy, and modified Meta's safety data responses to follow tone guidelines. Tone
Meta expanded their work on the refusal tone from Llama 3 so that the model sounds more natural. Meta targeted removing preachy and overly moralizing language, and Meta corrected formatting issues including the correct use of headers, lists, tables and more. To achieve this, Meta also targeted improvements to system prompt steerability and instruction following, meaning the model is more readily able to take on a specified tone. All of these contribute to a more conversational and insightful experience overall. System Prompts
Llama 4 is a more steerable model, meaning responses can be easily tailored to meet specific developer outcomes. Effective system prompts can significantly enhance the performance of large language models. In particular, Meta has seen that the use of a system prompt can be effective in reducing false refusals and templated or "preachy" language patterns common in LLMs. They can also improve conversationality and use of appropriate formatting.
Llama 4 system protections
Large language models, including Llama 4, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional guardrails as required. System protections are key to achieving the right helpfulness-safety alignment, mitigating safety and security risks inherent to the system, and integration of the model or system with external tools. Meta provides the community with system level protections - like Llama Guard, Prompt Guard and Code Shield - that developers should deploy with Llama models or other LLMs. All of Meta's reference implementation demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.Safety evaluations
As part of Meta's release approach, Meta followed a three-pronged strategy to manage risks:- Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama.
- Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
- Provide protections for the community to help prevent the misuse of our models.
Meta conducts recurring red teaming exercises with the goal of discovering risks via adversarial prompting and Meta uses the learnings to improve our benchmarks and safety tuning datasets. Meta partners early with subject-matter experts in critical risk areas to understand how models may lead to unintended harm for society. Based on these conversations, Meta derives a set of adversarial goals for the red team, such as extracting harmful information or reprogramming the model to act in potentially harmful ways. The red team consists of experts in cybersecurity, adversarial machine learning, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
Critical Risks
Meta spends additional focus on the following critical risk areas
1. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulnessTo assess risks related to proliferation of chemical and biological weapons for Llama 4, Meta applied expert-designed and other targeted evaluations designed to assess whether the use of Llama 4 could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. Meta also conducted additional red teaming and evaluations for violations of Meta's content policies related to this risk area. 2. Child Safety
Meta leverages pre-training methods like data filtering as a first step in mitigating Child Safety risk in our model. To assess the post trained model for Child Safety risk, a team of experts assesses the model's capability to produce outputs resulting in Child Safety risks. Meta uses this to inform additional model fine-tuning and in-depth red teaming exercises. Meta has also expanded their Child Safety evaluation benchmarks to cover Llama 4 capabilities like multi-image and multi-lingual. 3. Cyber attack enablement
Meta's cyber evaluations investigated whether Llama 4 is sufficiently capable to enable catastrophic threat scenario outcomes. Meta conducted threat modeling exercises to identify the specific model capabilities that would be necessary to automate operations or enhance human capabilities across key attack vectors both in terms of skill level and speed. Meta then identified and developed challenges against which to test for these capabilities in Llama 4 and peer models. Specifically, Meta focused on evaluating the capabilities of Llama 4 to automate cyberattacks, identify and exploit security vulnerabilities, and automate harmful workflows.
Known limitations
Llama 4 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 4's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 4 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Note:- Llama 4 has been trained on a broader collection of languages than the 12 supported languages (pre-training includes 200 total languages ). Developers may fine-tune Llama 4 models for languages beyond the 12 supported languages provided they comply with the Llama 4 Community License and the Acceptable Use Policy. Developers are responsible for ensuring that their use of Llama 4 in additional languages is done in a safe and responsible manner.
- Llama 4 has been tested for image understanding up to 5 input images. If leveraging additional image understanding capabilities beyond this, Developers are responsible for ensuring that their deployments are mitigated for risks and should perform additional testing and tuning tailored to their specific applications.
Acceptable use
Acceptable use policy
Intended Use Cases: Llama 4 is intended for commercial and research use in multiple languages. Instruction tuned models are intended for assistant-like chat and visual reasoning tasks, whereas pretrained models can be adapted for natural language generation. For vision, Llama 4 models are also optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The Llama 4 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 4 Community License allows for these use cases. Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 4 Community License. Use in languages or capabilities beyond those explicitly referenced as supported in this model card.Model Specifications
Context Length1000000
Quality Index0.78
LicenseCustom
Training DataAugust 2024
Last UpdatedDecember 2025
Input TypeText,Image
Output TypeText
ProviderMeta
Languages12 Languages
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