vLLM-Project/Speculators

Speculators is a library for accelerating LLM inference through speculative decoding, providing efficient draft model training that integrates seamlessly with vLLM to reduce latency and improve throughput.

Speculators provides the following key features:

Offline training data generation using vLLM: Enable the generation of hidden states using vLLM. Data samples are saved to disk and can be used for draft model training.
Draft model training support: E2E training support of single and multi-layer draft models. Training is supported for both non-MoE and MoE models.
Standardized, extensible format: Provides a Hugging Face-compatible format for defining speculative models, with tools to convert from external research repositories into a standard speculators format for easy adoption.
Seamless vLLM Integration: Built for direct deployment into vLLM, enabling low-latency, production-grade inference with minimal overhead.

Why use Speculators?

Large language models generate text one token at a time, which creates a fundamental bottleneck: each token requires a full forward pass through the model, leaving GPU compute underutilized while waiting for memory-bound operations. Speculative decoding addresses this by using a smaller, faster "draft" model (often times, just a single transformer layer) to predict multiple tokens ahead, and then verifying tokens in parallel with the primary model.

Speculative decoding provides the following benefits:

Reduced latency: Generates tokens 2-3 times faster for interactive applications such as chatbots and code assistants, where response time directly impacts user experience
Better GPU utilization: Converts latency and memory-bound decoding in the large model into compute-bound parallel token verification, improving hardware utilization.
No quality loss: Speculative decoding does not approximate the target model. Accepted tokens are exactly those the target model would have produced under the same sampling configuration; rejected draft tokens are discarded and regenerated by the target model.
Cost efficiency: Serve more requests per GPU by reducing the time each request occupies the hardware

Speculators is particularly valuable for latency-sensitive applications where users are waiting for responses in real-time, such as conversational AI, interactive coding assistants, and streaming text generation.