{"schema_version":"onlylabs.public_signal.v1","title":"Cerebras Writing: How We Deploy Trillion Parameter Frontier Models On Cerebras","description":"Cerebras writing signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/c93fed8d-c375-4f53-a65a-489c25ddd25b","json_url":"https://onlylabs.fyi/signals/c93fed8d-c375-4f53-a65a-489c25ddd25b/signal.json","generated_at":"2026-10-24T21:10:33.821Z","evidence_latest_fetched_at":"2026-10-22T20:02:39.69564+00:00","signal_first_seen_at":"2026-10-22T20:00:52.836875+00:00","org":{"slug":"cerebras","name":"Cerebras","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/cerebras","dossier_json_url":"https://onlylabs.fyi/labs/cerebras/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/c93fed8d-c375-4f53-a65a-489c25ddd25b","signal_json":"https://onlylabs.fyi/signals/c93fed8d-c375-4f53-a65a-489c25ddd25b/signal.json","source":"https://www.cerebras.ai/blog/how-we-deploy-trillion-parameter-frontier-models-on-cerebras","lab_dossier":"https://onlylabs.fyi/labs/cerebras","lab_dossier_json":"https://onlylabs.fyi/labs/cerebras/dossier.json","analysis":"https://onlylabs.fyi/analysis/cerebras","analysis_json":"https://onlylabs.fyi/analysis/cerebras/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/cerebras/evidence.json","category":"https://onlylabs.fyi/neoclouds","category_json":"https://onlylabs.fyi/neoclouds.json","category_feed":"https://onlylabs.fyi/neoclouds/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neocloud","topic":"https://onlylabs.fyi/topics/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json?category=neocloud","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml?category=neocloud","data_business":null},"answer_pack":{"answer":"Cerebras published How We Deploy Trillion Parameter Frontier Models On Cerebras. 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A single Cerebras WSE-3T wafer has 44 GB of on-chip memory, and trillion-parameter frontier models need terabytes for their weights alone. In this post, we’ll explain how we split a large frontier model across multiple WSE-3T wafers, to run multi-trillion parameter models at 1000-2000+ tokens per second. We’ll use the open source model Kimi K3, with ~3 trillion parameters, as an illustrative example. By the end, you'll understand: How we fit the full model into on-chip memory How we map the model across multiple chips How we handle a high # of concurrent requests Breaking it down with Kimi K3 Kimi K3 is an open source, 3T parameter model with 69 linear-attention layers and 24 dense layers. It scores an impressive 88.3% on Terminal-Bench 2.1 (vs. GPT-5.6 Sol's 88.8%) and 91.2% on BrowseComp (vs. GPT-5.6 Sol's 90.4%). 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