speculative-decoding

You can accelerate LLM inference using speculative decoding techniques like Medusa multiple heads and lookahead decoding to parallelize token generation and significantly reduce latency for real-time applications.

Speculative decoding speeds up large language models by using a smaller, faster draft model to generate speculative candidate tokens, which a larger, more accurate model then verifies in parallel.

You can implement Medusa multiple heads using PyTorch and Transformers to predict future tokens in parallel via multiple decoding heads, enhancing inference speed without sacrificing output quality.

Yes, speculative decoding works with the Hugging Face Transformers library, requiring both Transformers and PyTorch dependencies to execute draft model verification and parallel token generation.

The best way to improve LLM throughput using Jacobi iteration is through lookahead decoding, which parallelizes the generation of n-grams to maximize inference speed and overall processing efficiency.

You should use lookahead decoding when you want to parallelize n-gram generation via Jacobi iteration without training a separate draft model, whereas speculative decoding requires a smaller model for candidate generation.