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How to deploy delayed-streams-modeling in different scenarios?

2025-08-23 950

Matrix of deployment modalities

Usage Scenarios Recommended Programs Key Configurations
research and development (R&D) PyTorch Implementation
  • Python 3.8+ environment
  • GPU acceleration is recommended
  • Download pre-trained models via Hugging Face
production environment Rust server
  • L40S GPU Recommended Batch 64 Streaming
  • H100 can support 400 concurrent
  • WebSocket interface protocol
Apple device MLX Framework
  • iPhone 16 Pro running 1B model
  • Supports real-time microphone input
  • Optimized Metal GPU acceleration

Example of a typical deployment process

PyTorch research environment deployment:

  1. Cloning GitHub repositories and installing dependencies
  2. Download kyutai/stt-1b-en_fr pre-training model
  3. fulfillmentpython -m moshi_mlx.run_inferenceinference

Rust Production Deployment Essentials:

  • pass (a bill or inspection etc)cargo installInstalling server components
  • Edit config.toml to adjust batch parameters
  • start using--releaseMode Guaranteed Performance

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