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Conversational AI Latency and Throughput

1 post · Mar 2025 – May 2025

What this trail covers

This post details advancements in conversational AI, focusing on reducing latency and improving throughput for natural voice interactions. Norby leverages Cerebras' inference capabilities to achieve significant reductions in conversational response latency, enhance language switching, and speed up conversation summary processing. The system also utilizes Cerebras for incremental and parallelized reasoning during conversational pauses, enabling dynamic integration of reasoning throughout the conversation. OpenCall further demonstrates these benefits by integrating Cerebras' LLM inference API to achieve a 90% reduction in overall latency for their AI contact center, leading to faster resolutions and improved customer satisfaction.

All Cerebras trails
  1. Humanizing Digital Technology with NorbyThis post details how Norby achieved an 87% decrease in conversational response latency, a 74% improvement in language switching, and an 88% reduction in conversation summary processing time by leveraging Cerebras' inference capabilities. It highlights the use of Cerebras for incremental and parallelized reasoning during conversational pauses, enabling dynamic integration of reasoning throughout the conversational flow, contrasting with conventional models that require complete logical cycles before responding. The post also mentions the use of Cerebras' open-source models for advanced turn detection and intention inference, performed in approximately 300 milliseconds.
  2. Building Real Time Digital Twin with Cerebras at Tavus - CerebrasTavus.io integrated Cerebras' inference engine into their Conversational Video Interface (CVI) pipeline to reduce LLM latency. They measured Time to First Token (TTFT) and Token Output Speed (TPS) as critical metrics for natural conversation flow. By using Cerebras, Tavus achieved a TTFT of 440ms and 2000 TPS for the Llama 3.1-8B model, resulting in a 66% reduction in TTFT (from 300ms to 100ms) and a 300% increase in TPS (from 1/30ms to 1/10ms). This led to an overall LLM latency reduction of 100-1000%, significantly improving the responsiveness and realism of their digital twin video experiences.
  3. Memo-ry: Simplifying Daily Tasks for People with Memory Loss - CerebrasThis post details the Memo-ry application, which simplifies daily tasks for individuals with memory loss by using Cerebras' fast inference technology to extract actionable tasks from speech in near real-time. It highlights the challenge of slow inference (over 300ms) in previous systems, which disrupted conversation flow and led to incomplete tasks. Memo-ry achieves sub-50ms latency, an 85% reduction, enabling seamless interaction. The architecture includes a frontend interface, Web Speech API for transcription, and the Cerebras inference API for task extraction. Future plans include voice feedback, smart home integration, mobile app development, caregiver collaboration, and AI personalization.
  4. Building Real-Time AI-Powered Contact Centers with Cerebras at OpenCall - CerebrasThis post details how OpenCall integrated Cerebras' LLM inference API into their AI contact center to address challenges with real-time processing and complex tool calls. By breaking down granular processing into atomic operations and leveraging Cerebras for fast and reliable inference, OpenCall achieved a 90% reduction in overall latency, transforming resolutions from minutes to seconds and improving customer satisfaction scores by 20%. The post highlights the state machine architecture and specialized inference chains used by OpenCall to ensure reliability and behavioral specificity in handling customer interactions.
  5. Simulating Human Behavior with Cerebras - CerebrasThis post introduces LlamaSim, a multi-LLM framework for simulating human behavior at scale, specifically for predicting event and condition outcomes like election results. It highlights the use of Cerebras for hosting Llama 3.1 8B, achieving over 2000 tok/s and significantly outperforming other model providers. LlamaSim uses Cerebras for generating synthetic voter identities and backstories, demonstrating a ~60% reduction in generation time compared to GPT-4o-mini. The framework also leverages Cerebras integration with Instructor for structured output generation and dynamically manages conversation context by handling the removal of older messages to maintain relevance within the model's context window.