Speculative Decoding

Basic idea

Speculative decoding speeds up text generation by using a small, fast model to draft several tokens ahead, then having the larger model quickly verify or adjust them. This way, the big model doesn't need to predict every token step-by-step, reducing latency while keeping output quality.

What you'll accomplish

You'll explore speculative decoding using TensorRT-LLM on NVIDIA Spark using the traditional Draft-Target approach. These examples demonstrate how to accelerate large language model inference while maintaining output quality.

What to know before starting

• Experience with Docker and containerized applications
• Understanding of speculative decoding concepts
• Familiarity with TensorRT-LLM serving and API endpoints
• Knowledge of GPU memory management for large language models

Prerequisites

• NVIDIA Spark device with sufficient GPU memory available

• Docker with GPU support enabled

  Bash

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  docker run --gpus all nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev nvidia-smi
  

• HuggingFace authentication configured (if needed for model downloads)

  Bash

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  huggingface-cli login
  

• Network connectivity for model downloads

Time & risk

• Duration: 10-20 minutes for setup, additional time for model downloads (varies by network speed)
• Risks: GPU memory exhaustion with large models, container registry access issues, network timeouts during downloads
• Rollback: Stop Docker containers and optionally clean up downloaded model cache.