Tavus, an AI video research company, announced integration of Meta's Llama 3.3 models to power its conversational video interface platform on April 2, 2025, enabling developers to build realistic, real-time conversational experiences with digital twins. Co-Founder and CEO Hassaan Raza explained that incorporating Llama models gives digital replicas both "eyes" and a "brain" through multi-image reasoning for visual content interpretation and context-aware response capabilities.
The platform leverages Llama models to create human-like digital interactions addressing conversational quality and visual question-answering challenges through lifelike responsiveness and coherence. Tavus replaced closed-source AI models with Llama for better conversational quality, faster response times, and flexible open-source design enabling on-premises deployment and testing for enhanced speed, data privacy, and interoperability.
Llama's 70B model processes approximately 2,000 tokens per second, with Tavus reporting significant efficiency and quality improvements through open-source community tooling enabling experimentation and customisation for faster iteration and better use case alignment. The company integrates Llama models for conversational AI delivering responsive, context-aware real-time interactions, tool calling for enhanced responsiveness and dynamic interactions, and multi-image reasoning enabling visual question answering with accurate responses based on video visual context.
Tavus successfully integrated Llama 8B and 70B Instruct versions with customisations including advanced prompt engineering with multi-level prompting for enhanced conversational depth. Infrastructure testing included both on-premise vLLM and hosted cloud solutions through partners Cerebras and Fireworks, with vector databases and embedded models for storage and query optimisation.
With Cerebras's Llama implementation, Tavus achieved 440% to 550% latency improvement over high-latency models and 25% to 50% performance edge over comparable GPT models. Raza noted Llama has been "one of the least complex and most reliable components in our AI stack" with strong community support and internal workflow interoperability.
Fine-tuned Llama models integrated with retrieval-augmented generation techniques allow clients to use proprietary data and retrieval sources, tailoring AI solutions to specific business needs.
Llama 3.2 and 3.3 multimodal capabilities and smaller models suitable for on-device and edge cases enable Tavus to explore new CVI platform functionalities including enhanced speech recognition, turn detection, and visual question answering. The open-source model approach eliminates dependency on closed-source providers while enabling extensive customisation and optimisation for specific conversational video applications.
The platform's real-time digital interaction capabilities previously required extensive engineering time and multiple models, with Llama integration making processes more efficient while ensuring quick, clear responses. The combination of open-source flexibility with enterprise-grade performance positions Tavus to compete effectively in the conversational AI market while offering clients greater control over data privacy and deployment options. The significant latency improvements and processing capabilities demonstrate the viability of open-source alternatives to proprietary conversational AI solutions.