AI Model Inference Communication Protocols

4 posts · Apr 2024 – Aug 2025

What this trail covers

Baseten's platform supports various communication protocols for AI model inference, including HTTP for broad compatibility and reliability, WebSockets for real-time bidirectional communication in interactive applications, and gRPC for high-performance, type-safe service-to-service communication. The choice of protocol impacts application architecture, performance, and complexity. This post details a specific implementation of real-time text-to-speech using WebSockets with the XTTS V2 model, demoing the trade-offs between HTTP, WebSockets, and gRPC for AI model inference.

All Baseten trails

Aug 2025HTTP vs. WebSockets vs. gRPC for AI model inferenceThis post elaborates on the trade-offs between HTTP, WebSockets, and gRPC for AI model inference. It details the characteristics of each protocol: HTTP for reliability and simplicity, WebSockets for real-time bidirectional communication, and gRPC for high-performance, type-safe service-to-service interactions. The post highlights how these protocols impact application architecture, performance, and complexity, providing guidance on choosing the appropriate protocol based on specific use cases.
Jul 2025Zero to real-time transcription: The complete Whisper V3 streaming tutorialThis post provides a concrete implementation tutorial for real-time speech transcription using the Whisper V3 model over WebSockets on Baseten. It details the necessary Python packages, code structure for audio capture and transmission, metadata configuration for Whisper and streaming parameters, and the concurrent handling of sending audio chunks and receiving transcription results. It also highlights the benefits of WebSockets over traditional HTTP for streaming applications and provides guidance on production deployment with autoscaling.
Dec 2024Generally Available: The fastest, most accurate and cost-efficient Whisper transcriptionThis post details significant optimizations to the Whisper transcription pipeline, enhancing its speed, accuracy, and cost-efficiency. Key contributions include: 1. Implementing a two-stage approach: audio chunking using Voice Activity Detection (VAD) and then transcribing chunks with Whisper. 2. Leveraging Baseten Chains to orchestrate the VAD and Whisper Chainlets, allowing independent scaling and custom hardware provisioning for each step. 3. Optimizing the Whisper runtime by switching from a TensorRT-LLM Python runtime to a C++ executor, yielding an 18% speed improvement. 4. Transitioning from dynamic batching to in-flight batching (continuous batching) for the Whisper inference, further reducing processing times. These optimizations result in a pipeline over 10x faster than OpenAI's vanilla Whisper, with improved accuracy and cost-effectiveness.
Apr 2024Streaming real-time text to speech with XTTS V2This post details the implementation of a streaming real-time text-to-speech (TTS) endpoint for the XTTS V2 model using Baseten's Truss framework. It highlights how to leverage the model's `inference_stream()` function to yield audio chunks as they are generated, enabling near real-time audio playback with a round-trip time to the first chunk as low as 200 milliseconds. The post provides code examples for loading the model, configuring inference for streaming, and consuming the audio stream on the client-side using `ffplay`.
Advertisement