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Signed-off-by: Aidan Reilly <aireilly@redhat.com> Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
30 lines
2.6 KiB
Markdown
30 lines
2.6 KiB
Markdown
# Speculators
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[Speculators](https://docs.vllm.ai/projects/speculators/en/latest/) 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.
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Speculators provides the following key features:
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- **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.
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- **Draft model training support**: E2E training support of single and multi-layer draft models. Training is supported for both non-MoE and MoE models.
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- **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.
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- **Seamless vLLM Integration**: Built for direct deployment into vLLM, enabling low-latency, production-grade inference with minimal overhead.
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## Why use Speculators?
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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.
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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.
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Speculative decoding provides the following benefits:
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- **Reduced latency**: Generates tokens 2-3 times faster for interactive applications such as chatbots and code assistants, where response time directly impacts user experience
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- **Better GPU utilization**: Converts latency and memory-bound decoding in the large model into compute-bound parallel token verification, improving hardware utilization.
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- **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.
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- **Cost efficiency**: Serve more requests per GPU by reducing the time each request occupies the hardware
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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.
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## Resources
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- [Speculators examples](https://github.com/vllm-project/speculators/tree/main/examples)
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- [GitHub Repository](https://github.com/vllm-project/speculators)
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