Visual language models (VLMs) present a new divergence in approaches to achieving state-of-the-art performance. The two complete opposites of VLM models exemplify this split: Pixtral Large, with its impressive 124B parameters and compute-heavy approach, versus TIGER-Lab's Mantis-Idefics, with just 8B parameters, a champion of efficiency.
Both generative AI models can analyze images and documents with greater efficiency than any other currently known solution. This capability enables us to tackle a variety of tasks, such as asking questions related to images, processing documents for comparison, and much more.
However, the big question is: which is more suitable and reasonable for enterprise-wide adoption of LLMs? In this article, we will compare the technical architecture and performance of the two models to help determine which one you should use.
Before diving into a technical comparison of the two models, we will briefly explain what these so-called visual language models are.
Visual language models (VLMs) are generative AI models capable of reading, understanding, and analyzing information in images and documents, including charts, webpages, mathematical reasoning, and much more.These capabilities enable identifying errors, categorizing findings, answering questions about the information in these files, and even automating many human labor tasks.
For example, you can use these models to build a chatbot to ask and retrieve information about technical drawings (and automate tasks related to these), understand and compare insurance documents, analyze X-ray images to identify cancer, and more. As a result, these models are highly beneficial across many industries, such as healthcare, insurance, manufacturing, and beyond.
Core Architecture: 124B parameters
300GB+ GPU RAM requirement Enterprise-grade GPU clusters necessary ~37.5GB per billion parameters
Long context window (128K tokens)
Technical Innovations:
Novel ROPE-2D implementation Block-diagonal attention masksNative resolution processing Gating in FFN layers
Full Chain-of-Thought (CoT) reasoning implementation
Core Architecture: 8B parameters
Standard GPU requirements Consumer hardware compatible~1-2GB per billion parameters
8K context window (LLaMA3 backbone)
Technical Innovations:
Efficient instruction tuning pipeline Multi-image specialization Four-skill focus optimization Mantis-Instruct dataset utilization
Note: Mantis-Idefics2 does not specify using CoT - inference time compute and result embedding centrality may play in its favor here
Resource Utilization:
Pixtral: ~37.5GB per billion parameters Mantis: ~1-2GB per billion parameters
Deployment Options:∏
Pixtral: Specialized data centers Mantis: Standard GPU infrastructure
1. Model Generalization
2. Research Release Questions
Is Pixtral Large identical to Mistral's production model?
Are there unreleased optimizations?
Could the 300GB requirement be pre-optimization?
3. Production Realities
Production models often employ:
Potential limitations in:
1. Architectural Choices
2. Computational Strategy
1. Efficiency Focus
2. Training Innovation
721K carefully curated examples
Four-skill specialization:
1. Pixtral Large
2. Mantis-Idefics2
The efficiency-to-performance ratio reveals:
1. Architectural Evolution
2. Training Methodologies
1. Production Reality
2. Future Development
The comparison between Pixtral Large and Mantis-Idefics2 is more than just a technical face-off – it represents a philosophical divide in AI systems development that is critical for the future. While Pixtral Large boasts a comprehensive architecture capable of handling a wide range of general tasks, Mantis-Idefics2 demonstrates that clever training and efficient designs can achieve remarkable results with significantly fewer resources.
But the question we posed at the start remains: which model is right for your enterprise? There is no single correct answer. Each project is unique, and the model should be chosen based on the specifics of the task, which can range from small and narrow to large and general. Perhaps a hybrid approach could be the answer for many, allowing decisions on when each model is most appropriate. Our team can help you to decide.
The main lessons learned today are:
As we move forward, the industry would benefit from more comprehensive real-world testing, production metrics, and a deeper understanding of the trade-offs between generalization and efficiency. This is what we at ConfidentialMind are focusing on. We are constantly navigating the battle between compute-heavy and cost-efficient architectures, striving to deliver solutions where our customers win by balancing the best of both worlds. Only through such an approach will enterprise-wide adoption be possible - via an AI system that works out of the box, is efficient, and remains robust.