On April 5, 2025, American tech giant Meta unveiled its latest artificial intelligence model series, Llama 4. This series marks a significant breakthrough in the field of AI, particularly in multimodal capabilities and the use of Mixture of Experts (MoE) architecture.

Model Versions and Architectural Features

The Llama 4 series currently consists of two versions: Scout and Maverick. Both versions utilize the Mixture of Experts (MoE) architecture, which divides the model into multiple "expert" sub-models focused on specific tasks, thereby significantly improving training and inference efficiency. Specifically:

● Llama 4 Scout: It has 17 billion active parameters and 16 expert sub-models, with a total parameter count of 109 billion. It supports a context window of up to 10 million tokens, setting a new record for open-source models. Scout can run on a single NVIDIA H100 GPU and is suitable for multimodal inference and mobile deployment.

● Llama 4 Maverick: It also has 17 billion active parameters but increases the number of expert sub-models to 128, with a total parameter count of 400 billion. It performs exceptionally well in multiple benchmark tests, outperforming GPT-4o and Gemini 2.0 Flash. It is comparable to DeepSeek v3 in inference and coding, but with less than half the parameters. Maverick requires multi-card collaboration and is suitable for complex code generation and long-text processing.

In addition, Meta is training an even larger model named Llama 4 Behemoth, which has a total parameter count of 2 trillion, 288 billion active parameters, and 64 experts. Behemoth is considered one of the most powerful and intelligent LLMs developed by Meta to date.

Multimodal Capabilities and Technical Advantages

The Llama 4 series is the first open-source MoE model to natively support multimodal capabilities. It can process and integrate various types of data, including text, video, images, and audio, and seamlessly convert content between these formats. This multimodal capability is achieved through early fusion technology, which integrates text and visual tokens into a unified model backbone. For example, Llama 4 can extract semantic information directly from video frames using the MetaCLIP visual encoder.

Furthermore, the Llama 4 series introduces new training techniques, such as MetaP, which reliably sets model hyperparameters to ensure high FLOPs utilization. During training, Llama 4 uses a data mixture of over 30 trillion tokens, covering diverse text, image, and video datasets.

Performance and Industry Impact

The Llama 4 series has demonstrated exceptional performance in multiple benchmark tests. For example, Llama 4 Maverick ranks first in challenging prompts, coding, mathematics, and creative writing, with an ELO score of 1417, surpassing Meta's previous Llama-3-405B. Additionally, Llama 4 Scout performs well in image understanding tasks, achieving accuracy rates of 83.4% and 89.4% in ChartQA and DocVQA benchmarks, respectively.

The launch of the Llama 4 series has had a profound impact on the industry. It has democratized multimodal models by enabling the 17B version to run on a single GPU. Moreover, the open-source nature of Llama 4 provides developers and researchers with more opportunities for customization and research.

Future Outlook

The launch of the Llama 4 series is just the beginning of Meta's journey in the AI field. With further training and optimization of Llama 4 Behemoth, its performance is expected to improve even more. Meta plans to maintain the Llama series' leading position in the open-source AI field through continuous technological innovation and expansion.

Conclusion

The Llama 4 series represents a significant breakthrough for Meta in the field of artificial intelligence. Its Mixture of Experts architecture and multimodal capabilities bring new possibilities to the open-source AI community. Despite some usage restrictions, such as the ban on EU users due to AI and data privacy regulations, the performance and application scope improvements of Llama 4 are still worth paying attention to. In the future, with the release and optimization of more versions, Llama 4 is expected to have a greater impact in the AI field.

Conevo IC modules

At Conevo Electronics, we specialize in delivering dependable and superior IC / integrated circuit solutions to clients globally. We are committed to to assist customers in swiftly acquiring the essential IC components and materials for their projects through our user-friendly website. Below, we highlight some of the latest popular IC models.

1. The AD8663ARZ-REEL7 is a high-performance, low-noise, precision operational amplifier designed for applications requiring high accuracy and stability, featuring rail-to-rail output swing and excellent DC specifications in a compact reel package.

2. The STM32F071RBT6TR is a high-performance 32-bit mcu from STMicroelectronics, featuring an ARM Cortex-M0 core that operates at up to 48 MHz. It includes 128 KB of flash memory, 16 KB of SRAM with hardware parity protection, and a wide range of peripherals such as a 12-bit ADC, a 12-bit DAC with two channels, and multiple communication interfaces including I2C, SPI, USART, and HDMI CEC.

3. The ADF4355BCPZ-RL7 is a high-performance, wideband frequency synthesizer with an integrated VCO, designed for applications requiring low phase noise and high frequency resolution. It supports RF output frequencies ranging from 54 MHz to 6.8 GHz, features fractional-N and integer-N synthesis capabilities, and offers programmable output power levels and a mute function.

Website: www.conevoelec.com

Email: info@conevoelec.com