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Nvidia Neva 22B vs Microsoft kosmos-2: A Battle of Multimodal LLMs

Explore the capabilities of Nvidia’s Neva 22B and Microsoft’s Kosmos-2 multimodal LLM in event reporting, visual question answering, and more.
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In the rapidly evolving landscape of artificial intelligence, two tech giants, Nvidia and Microsoft, are making significant strides with their latest multimodal large language models (MLLMs). Nvidia’s Neva 22B and Microsoft’s Kosmos-2 represent the cutting-edge of AI technology, each offering unique features and capabilities. As businesses and researchers increasingly rely on AI for various applications, understanding these models’ strengths and differences is crucial.  This article investigates a comparative analysis of Nvidia’s Neva 22B and Microsoft’s Kosmos-2, focusing on their inference performance.

Table of content

  1. Overview of MultiModal LLMs
  2. Model architecture 
  3. Inference Performance Analysis
  4. Best Use Case of Each Model

Let’s start with understanding the functionality of multimodal LLMs.

Overview of MultiModal LLMs

Multimodal large language models (LLMs) represent a significant advancement in artificial intelligence, enabling the processing and understanding of diverse data types, including text, images, and more. Unlike traditional LLMs that focus solely on textual information, multimodal models integrate multiple data modalities, offering a more comprehensive and nuanced understanding of information.

Multimodal LLMs, such as Nvidia’s Neva 22B and Microsoft’s Kosmos-2, are designed to process and generate outputs from various data types simultaneously. This capability is crucial in applications where understanding the context from different sources is essential. For instance, in the healthcare sector, multimodal LLMs can analyze medical records, imaging data, and patient histories to provide more accurate diagnoses and treatment recommendations.

Current Trends in Multimodal AI

The AI landscape is witnessing a surge in the development and deployment of multimodal LLMs. Companies are leveraging these models to enhance user experiences, improve decision-making processes, and drive innovation across various industries. Key trends include:

  • Integration of Vision and Language: Models like Neva 22B and Kosmos-2 excel at combining visual and textual information, making them ideal for applications such as automated content creation, enhanced search engines, and advanced virtual assistants.
  • Enhanced Contextual Understanding: Multimodal LLMs provide richer context by drawing from diverse data sources, leading to more accurate and relevant outputs.
  • Improved Human-AI Interaction: By understanding and generating multimodal data, these models facilitate more natural and effective interactions between humans and AI systems.

Model architecture

Neva 22B is a multimodal large language model developed by Nvidia. It integrates textual and visual data, enhancing its capability to understand and generate content that encompasses multiple modalities.

Key Features:

  • Architecture: Transformer-based, optimized for multimodal tasks.
  • Input: Text and image data.
  • Output: Text with multimodal context.
  • Inference: Optimized for speed and accuracy, utilizing Nvidia’s latest hardware.

Kosmos-2 is a multimodal large language model created by Microsoft, designed to ground text to the visual world, enabling advanced reasoning about visual elements in images.

Key Features:

  • Architecture: Combines GPT and CLIP models.
  • Input: RGB images and text.
  • Output: Text, with capabilities for visual question answering and bounding box generation.
  • Inference: Uses Nvidia’s Triton inference server, optimized for various hardware configurations.

Inference Performance Analysis

To compare the inference performance of both the multimodal LLMs we would use the same image and question from the models to make a fair comparison. Below is the image and the question that we would be feeding as input for the MLLMs.

Input Image:

Input Question:

Describe what you see in this image.

Let’s see how these models would reply to the image and the question feed. Here are the inferences.

Neva 22B output:

Kosmos -2 image output:

Kosmos – 2 text output:

Analysis of the inference

Neva 22B:

  • Focuses on the food cart and the people gathered around it.
  • Mentions individuals holding umbrellas, possibly due to rain.
  • Highlights the presence of cars and the urban setting.

Kosmos-2:

  • Emphasizes a man walking down the sidewalk and general street activity.
  • Notes the presence of motorcycles parked on the street.
  • Describes the lively and bustling atmosphere with people walking and riding motorcycles.

Both models provide a detailed description of the street scene, capturing the bustling and lively nature of the environment. However, Neva 22B focuses more on the food cart and the people around it, while Kosmos-2 highlights the general street activity and the presence of motorcycles which are highlighted in the output image.

Best Use Case of Each Model

Neva 22B

Neva 22B excels in scenarios where detailed contextual understanding from multiple modalities is crucial. Its strength lies in providing comprehensive descriptions and understanding complex interactions in dynamic environments. This makes it ideal for applications in:

  • Urban Surveillance: Monitoring and analyzing street activities.
  • Event Reporting: Capturing and summarizing events in crowded settings.
  • Social Media Content Generation: Creating rich descriptions for images involving multiple subjects and activities.

Kosmos-2

Kosmos-2 is particularly effective in scenarios requiring the integration of textual and visual data for specific tasks. Its ability to provide accurate visual grounding and interaction makes it suitable for:

  • Visual Question Answering: Answering questions based on visual input.
  • Autonomous Driving Systems: Understanding and describing road scenes.
  • Retail and Inventory Management: Identifying and describing items in a retail setting.

Conclusion

Nvidia’s Neva 22B and Microsoft’s Kosmos-2 represent the forefront of multimodal large language models, each with its unique strengths. Neva 22B excels in providing detailed contextual understanding and is well-suited for urban surveillance, event reporting, and social media content generation. On the other hand, Kosmos-2 shines in visual question answering, autonomous driving systems, and retail inventory management, thanks to its strong visual grounding capabilities.

References

  1. Modelcard of Nvidia Neva 22B
  2. Modelcard of Microsoft Kosmos-2
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Sourabh Mehta

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