{"author":{"name":"Yahav Biran","slug":"yahav-biran","article_count":1,"latest_published_at":"2026-04-16T15:22:33.61+00:00","profile_url":"https://platform.waiboom.ai/authors/yahav-biran","api_url":"https://platform.waiboom.ai/api/authors/yahav-biran"},"articles":[{"slug":"accelerating-decode-heavy-llm-inference-with-speculative-decoding-on-aws-trainiu","title":"Speculative Decoding on Trainium Cuts LLM Inference Latency 3x","url":"https://platform.waiboom.ai/article/2026/04/16/accelerating-decode-heavy-llm-inference-with-speculative-decoding-on-aws-trainiu","content_type":"aggregated_news","summary":"AWS and vLLM have demonstrated that speculative decoding on Trainium chips can accelerate token generation by up to 3x for decode-heavy LLM workloads. The technique uses a small draft model to propose multiple tokens at once, which a larger target model verifies in a single forward pass, reducing sequential decode steps and lowering per-token inference costs. The post provides benchmarks using Qwen3 models, practical tuning guidance for draft model selection and speculative token window sizing, and reproducible instructions for deployment on Kubernetes.","published_at":"2026-04-16T15:22:33.61+00:00","updated_at":"2026-04-22T00:59:04.768177+00:00","source":{"url":"https://aws.amazon.com/blogs/machine-learning/accelerating-decode-heavy-llm-inference-with-speculative-decoding-on-aws-trainium-and-vllm/","name":"AWS Machine Learning Blog"},"featured_image":{"url":"https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/03/26/ML-206391.png","alt":null},"categories":[{"name":"LLMs","slug":"llms"},{"name":"AI Hardware","slug":"ai-hardware"},{"name":"Infrastructure","slug":"infrastructure"}]}]}