⚡️ 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 Instant 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 →

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Kernels
Key research and product announcements at the AI Native Conf

Kernels
FlashAttention-4: Algorithm and Kernel Pipelining Co-Design for Asymmetric Hardware Scaling

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)

Inference
Cache-aware prefill–decode disaggregation (CPD) for up to 40% faster long-context LLM serving

Jiejing Zhang, Yubo Wang, Yinghui Liu, Mourya Vangala Srinivasa, Chenxi Li, Jue Wang, Yineng Zhang, Shuaiwen Leon Song, Ce Zhang

Agents
CoderForge-Preview: SOTA open dataset for training efficient coding agents

By Alpay Ariyak*, Junda Zhang, Junxiong Wang, Shang Zhu, Federico Bianchi, Sanjana Srivastava, Ashwinee Panda, Siddhant Bharti, Chenfeng Xu, John Heo, Xiaoxia Shirley Wu, James Zou, Percy Liang, Leon Song, Ce Zhang, Ben Athiwaratkun, Zhongzhu Zhou*, Qingyang Wu* *Project Core Leads

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