{"schema_version":"onlylabs.public_signal.v1","title":"Together AI Writing: FlashAttention-4: Algorithm and Kernel Pipelining Co-Design for Asymmetric Hardware Scaling","description":"Together AI writing signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/ff7e0eb2-9f45-4c2b-88a5-29f3e0ad6686","json_url":"https://onlylabs.fyi/signals/ff7e0eb2-9f45-4c2b-88a5-29f3e0ad6686/signal.json","generated_at":"2026-06-07T21:14:59.735035+00:00","org":{"slug":"together-ai","name":"Together AI","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/together-ai","dossier_json_url":"https://onlylabs.fyi/labs/together-ai/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/ff7e0eb2-9f45-4c2b-88a5-29f3e0ad6686","signal_json":"https://onlylabs.fyi/signals/ff7e0eb2-9f45-4c2b-88a5-29f3e0ad6686/signal.json","source":"https://www.together.ai/blog/flashattention-4","lab_dossier":"https://onlylabs.fyi/labs/together-ai","lab_dossier_json":"https://onlylabs.fyi/labs/together-ai/dossier.json","analysis":"https://onlylabs.fyi/analysis/together-ai","analysis_json":"https://onlylabs.fyi/analysis/together-ai/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/together-ai/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":"Together AI published FlashAttention-4: Algorithm and Kernel Pipelining Co-Design for Asymmetric Hardware Scaling. 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Links in this article Paper Code Presenting FlashAttention-4: [ Paper ] [ Code ] Modern accelerators like Blackwell GPUs continue the trend of asymmetric hardware scaling , where tensor core throughput grows..."},"evidence_pages":[{"url":"https://www.together.ai/blog/flashattention-4","final_url":"https://www.together.ai/blog/flashattention-4","title":"FlashAttention-4: Algorithm and Kernel Pipelining Co-Design for Asymmetric Hardware Scaling","http_status":200,"content_type":"text/html; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-07T21:14:59.735035+00:00","bytes":323328,"raw_path":"3aed471f86e7be108d7d255c3b131c95e2ba4abb1904b67869ef161866ed32b8.html","content_hash":"7595c952139a8851ca9e17313993c800bca28cba2bf43951597f4e6f7ddfea2c","excerpt_chars":1200,"truncated":true,"excerpt":"FlashAttention-4: Algorithm and Kernel Pipelining Co-Design for Asymmetric Hardware Scaling ⚡️ FlashAttention-4: up to 1.3× faster than cuDNN on NVIDIA Blackwell → Introducing Together AI's new look → 🔎 ATLAS: runtime-learning accelerators delivering up to 4x faster LLM inference → ⚡ Together GPU Clusters: self-service NVIDIA GPUs, now generally available → 📦 Batch Inference API: Process billions of tokens at 50% lower cost for most models → 🪛 Fine-Tuning Platform Upgrades: Larger Models, Longer Contexts → All blog posts Research Published 3/5/2026 FlashAttention-4: Algorithm and Kernel Pipelining Co-Design for Asymmetric Hardware Scaling Authors Ted Zadouri (Princeton University, Together AI), Markus Hoehnerbach (Meta), Jay Shah (Colfax Research), Timmy Liu (NVIDIA), Vijay Thakkar (Meta, Georgia Tech), Tri Dao (Princeton University, Together AI) Table of contents 40+ Models Chosen for Production...40+ Models Chosen for Production...40+ Models Chosen for Production... 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