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Llama 4 Deployment Now Available on Bitdeer AI Cloud

Taylor Ye

• 5 min read
Banner image showing Llama 4 Maverick on Bitdeer AI Cloud, with a llama mascot and platform interface

The open-source AI landscape has seen a significant advancement with the release of Llama 4 by Meta on April 5. This latest iteration of Meta's powerful large language models (LLMs) brings substantial improvements in reasoning, instruction-following, and overall performance, marking the most notable upgrade in the Llama LLM family to date. Key highlights of the Llama 4 release include Llama 4 Maverick (~400B) and Llama 4 Scout (~109B), both leveraging a Mixture of Experts (MoE) architecture with 17 billion active parameters.

In this post, we’ll explore what’s new in Llama 4, how it compares to Llama 3, ideal use cases, and how you can start using it today through our Bitdeer  AI Cloud platform.

Meet Llama 4: Introducing Scout and Maverick

The newly released Llama 4 Scout and Llama 4 Maverick represent the most advanced models in the Llama ecosystem to date. Both are built using a MoE architecture, Meta’s first use of this design, which enables more efficient training and inference by activating only a subset of model parameters for each task.

These models are also open-weights, native multimodal models with unprecedented context length support, allowing them to process significantly longer inputs than previous Llama generations. By leveraging the MoE architecture, Llama 4 models can deliver higher performance under the same computational constraints compared to traditional dense models, making them both powerful and resource-efficient.

Mixture of Experts architecture diagram

Source: from Meta

Llama 4 Maverick: Optimized for Multimodal Intelligence and Efficiency

Llama 4 Maverick is one of the most advanced models in the Llama 4 ecosystem, designed to deliver high performance in both text and image understanding while maintaining exceptional efficiency. Built with a MoE architecture, featuring 128 experts and activating only a subset per task, it significantly reduces computation costs without compromising output quality.

As Meta’s flagship model for general assistants and chat applications, Maverick is particularly well-suited for multilingual, multimodal, and highly interactive AI experiences.

Key Highlights:

  • 17B active parameters, 128 experts, and 400B total parameters, 1M content length
  • MoE architecture activates only one routing expert and one shared expert per token for efficient inference
  • Outperforms Llama 3.3 70B in accuracy and efficiency under similar computational budgets
  • Delivers state-of-the-art image and text understanding in 12 languages
  • Capable of running on a single NVIDIA H100 DGX (=8 H100 GPUs) or in distributed inference environments
  • Ideal for tasks involving precise image interpretation, creative content generation, and multilingual conversation

Llama 4 Scout: Optimized for Long-Context Reasoning and Lightweight Deployment

Llama 4 Scout is a highly efficient model designed for tasks requiring long-context understanding, high-speed reasoning, and visual alignment. With 17 billion active parameters, 16 experts, and 109 billion total parameters, it offers a powerful balance of performance and efficiency. Notably, it supports an industry-leading 10 million-token context window, an 80x increase over Llama 3, making it ideal for working with large-scale documents, extensive codebases, and complex information flows.

Key Highlights

  • 17B active parameters, 16 experts, and 109B total parameters
  • Efficient MoE architecture for faster, lower-cost inference
  • Supports a default 256K context window, with roadmap expansion to 10 million tokens—80x longer than Llama 3
  • Significant improvements in reasoning, encoding, and long-context processing
  • High accuracy in image understanding with prompt-to-visual alignment
  • Runs on a single NVIDIA H100 GPU and supports scalable deployment

Why is it better than the previous Llama models

Llama 4 marks a major leap forward from its predecessors, delivering powerful upgrades in performance, efficiency, and versatility. The first two models (i.e. Llama 4 Scout and Llama 4 Maverick) in the series  introduce advanced multimodal capabilities (understanding both text and image prompts) and support industry-leading context windows, allowing them to process significantly more information at once compared to earlier versions.

Higher Performance at Better Value

  • Compared to Llama 3.3 70B, Llama 4 delivers high-quality results at lower cost. Built with a MoE architecture, the model activates only the most relevant components per task, improving efficiency in both training and inference while reducing computational overhead.

Improved Training and Language Support

  • Llama 4 utilizes a more stable and efficient training strategy, enhancing its content generation and comprehension. It now supports 12 languages, up from 8 in Llama 3, with pre-training data from over 200 languages, ensuring more accurate and natural outputs for global use cases.

Scaling Up Efficiency and Data

  • Llama 4 represents a major leap in training scale and efficiency. Meta trained the model using over 300 trillion tokens, more than double that of Llama 3, and introduced image and video data for the first time. This enhances the model’s multimodal capabilities and overall adaptability for complex tasks and diverse input types.
Llama 4 vs. Llama 3 feature comparison table

Business Application of Llama4

Llama 4 Maverick offers industry-leading performance in image and text understanding, making it especially well-suited for building intelligent, interactive, and multilingual AI applications. It enables enterprises to develop smart assistants equipped with both visual recognition and natural language generation capabilities.

Ideal Use Cases:

  • Customer Support Bots: Analyze uploaded images and respond accurately using contextual text understanding.
  • Complex Reasoning: Conduct structured analysis and decision-making across extensive codebases or knowledge repositories.
  • Enterprise AI Assistants: Answer employee questions by interpreting complex documents that combine visuals and text, aiding in internal decision-making.
  • Global User Engagement: Power high-quality AI assistants that understand visual inputs and communicate across languages.
  • Multimodal Applications: Improve response accuracy and user experience in scenarios requiring both image and language comprehension.

Llama 4 Scout excels in code understanding, advanced reasoning, long-context processing, and image interpretation. It is particularly suited for extracting key insights from large datasets, making it ideal for enterprise knowledge management and intelligent analytics.

Ideal Use Cases:

  • Document Summarization: Efficiently process and compress lengthy content such as technical manuals, contracts, or research reports.
  • Multilingual Creative Generation: Facilitate cross-language content creation and natural language interactions for marketing, media, and global communication.
  • Personalized Interactions: Generate responses tailored to user preferences and past behavior.
  • Enterprise Information Retrieval: Aggregate and extract information across platforms, including systems like SharePoint or internal documentation.
  • Intelligent Virtual Assistants: Support customer service and technical support scenarios requiring deep contextual understanding.

The following section provides a performance comparison of the two models. Enterprise users can evaluate them based on their specific use cases and model configurations to determine the most suitable solution.

Scout vs. Maverick architecture and deployment comparison

Get Started with Llama 4 on Bitdeer AI Studio

With Llama 4 Maverick now available on Bitdeer AI Cloud , you can immediately begin exploring its capabilities through a streamlined development experience. Whether you're experimenting with natural language tasks or integrating AI into production systems, our platform makes it easy to test, deploy, and scale with confidence. Powered by NVIDIA H100 DGX GPUs, Bitdeer AI Cloud ensures the high-performance compute needed to unlock the full potential of Llama 4 Maverick.

With support through both an intuitive web interface and API access, Bitdeer AI Cloud makes it simple to build, test, and deploy with Llama 4 Maverick in real time.

Getting Started with Llama 4 Maverick on Bitdeer AI Cloud:

  • Sign In to Bitdeer AI Cloud – Create an account or log in to gain access to our full suite of AI tools.
  • Explore the Models Library – Head to Console > Models and choose Llama 4 Maverick from the list of available models.
  • Interact via Web Interface – Input your queries directly into our user-friendly web UI to receive fast, high-quality outputs.
  • Integrate via API – Developers can connect seamlessly using our OpenAI-compatible RESTful APIs.
  • Customize Your Experience – Fine-tune performance settings to meet your specific project goals—whether it’s optimizing for speed, precision, or resource use.

By accessing Llama 4 Maverick through Bitdeer AI Cloud, users gain a streamlined, professional-grade environment for experimenting, building, and deploying powerful AI-driven solutions with ease.