# peagle

Published articles for peagle.

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## Parallel All the Way Down: Beyond Single-Token Generation with Speculative Decoding

DevFeed: [Parallel All the Way Down: Beyond Single-Token Generation with Speculative Decoding](<https://devfeed.tech/articles/parallel-all-the-way-down-beyond-single-token-generation-with-speculative-decoding-79467.md>)

Original publisher: [Read original article](<https://vllm.ai/blog/2026-07-28-speculators-parallel-drafting>)

Author: Alexandre Marques, Megan Flynn, Helen Zhao, Krishna Teja Chitty Venkata, Chibueze Ukachi (Red Hat AI)

Published: 2026-07-28T00:00:00Z

Content type: article

Language: en

Sources: [vLLM Blog](<https://devfeed.tech/sources/vllm-blog.md>)

Topics: [tgi](<https://devfeed.tech/topics/tgi.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [distributed-training](<https://devfeed.tech/topics/distributed-training.md>), [LLM optimization](<https://devfeed.tech/topics/llm-optimization.md>), [GPU](<https://devfeed.tech/topics/gpu.md>)

Tags: [ai-engineering](<https://devfeed.tech/tags/ai-engineering.md>), [dflash](<https://devfeed.tech/tags/dflash.md>), [dspark](<https://devfeed.tech/tags/dspark.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-inference](<https://devfeed.tech/tags/llm-inference.md>), [llm-serving](<https://devfeed.tech/tags/llm-serving.md>), [model-serving](<https://devfeed.tech/tags/model-serving.md>), [open-source-ai](<https://devfeed.tech/tags/open-source-ai.md>), [parallel](<https://devfeed.tech/tags/parallel.md>), [peagle](<https://devfeed.tech/tags/peagle.md>), [speculative-decoding](<https://devfeed.tech/tags/speculative-decoding.md>), [speculators](<https://devfeed.tech/tags/speculators.md>), [vllm](<https://devfeed.tech/tags/vllm.md>), [vllm-blog](<https://devfeed.tech/tags/vllm-blog.md>)

### AI overview

Speculators and vLLM support three parallel drafting algorithms for speculative decoding in large language model serving: P-EAGLE, DFlash, and DSpark. The article explains how these methods generate draft tokens in parallel, describes their distinct training and inference approaches, and presents them as ways to reduce drafting latency and improve serving throughput. It notes that performance varies by model, task, and hardware, and that the article's benchmark plots were corrected after an erroneous environment setup.

### Source excerpt

Speculators and vLLM now support P-EAGLE, DFlash, and DSpark -- three parallel drafting algorithms that move beyond sequential token generation to deliver faster, simpler, and more scalable speculative decoding for LLM serving.