{"schema_version":"onlylabs.public_signal.v1","title":"Cohere Writing: Hardware Aware Dynamic Speculative Decoding","description":"Cohere writing signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/1236a629-21e1-4558-b327-89e90b2529d7","json_url":"https://onlylabs.fyi/signals/1236a629-21e1-4558-b327-89e90b2529d7/signal.json","generated_at":"2026-07-27T00:20:33.138Z","evidence_latest_fetched_at":"2026-07-10T20:02:23.333793+00:00","signal_first_seen_at":"2026-07-10T20:00:48.882973+00:00","org":{"slug":"cohere","name":"Cohere","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/cohere","dossier_json_url":"https://onlylabs.fyi/labs/cohere/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/1236a629-21e1-4558-b327-89e90b2529d7","signal_json":"https://onlylabs.fyi/signals/1236a629-21e1-4558-b327-89e90b2529d7/signal.json","source":"https://cohere.com/blog/hardware-aware-dynamic-speculative-decoding","lab_dossier":"https://onlylabs.fyi/labs/cohere","lab_dossier_json":"https://onlylabs.fyi/labs/cohere/dossier.json","analysis":"https://onlylabs.fyi/analysis/cohere","analysis_json":"https://onlylabs.fyi/analysis/cohere/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/cohere/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":"https://onlylabs.fyi/topics/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml","data_business":null},"answer_pack":{"answer":"Cohere published Hardware Aware Dynamic Speculative Decoding. 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LLM inference generates one token at a time. SD instead uses a smaller draft model to propose multiple tokens, which are then verified by the large target model in a single timestep. Because more than one token can be accepted per step, this speeds up generation. SD achieves its speedup by exploiting the tradeoff between the compute and memory bandwidth of a GPU. Batching multiple requests improves GPU utilization, but the operating regime depends on the batch size (BS): At small BS , inference is memory bandwidth-bound : the cost of loading LLM weights from HBM into SRAM dominates. At large BS , inference becomes compute-bound : the matrix multiplications of LLM weights against many request tokens dominate. The time spent in SD has two main components: Time in SD = Time to draft tokens (small draft model) + Time to verify draft tokens (large target model) Draft models are much cheaper to run. 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