High performance starts with wellbeing. It was a pleasure to welcome our Brand Ambassador, @leylahfernandez, for an energizing workout and conversation on mindset, recovery, and sustainable performance. Thank you, Leylah, for inspiring our employees and reminding us that resilience, recovery, and mindset are key to success.

Solana is the high-performance network powering payments, AI agents, and crypto applications at scale. @_rishinsharma, Head of AI Growth at @solana, will join the panel on "Payment Rail Selection for Agent Commerce" at Agentic Finance Summit. June 3 · New York ·

🚀 Introducing FlashQLA: high-performance linear attention kernels built on TileLang. ⚡ 2–3× forward speedup. 2× backward speedup. 💻 Purpose-built for agentic AI on your personal devices. 💡Key insights: 1. Gate-driven automatic intra-card CP. 2. Hardware-friendly algebraic reformulation. 3. TileLang fused warp-specialized kernels. FlashQLA boosts SM utilization via automatic intra-device CP. The gains are especially pronounced for TP setups, small models, and long-context workloads. Instead of fusing the entire GDN flow into a single kernel, we split it into two kernels optimized for CP and backward efficiency. At large batch sizes this incurs extra memory I/O overhead vs. a fully fused approach, but it delivers better real-world performance on edge devices and long-context workloads. The backward pass was the hardest part: we built a 16-stage warp-specialized pipeline under extremely tight on-chip memory constraints, ultimately achieving 2×+ kernel-level speedups. We hope this is useful to the community!🫶🫶 Learn more: 📖 Blog: 💻 Code:

We're open-sourcing FlashKDA — our high-performance CUTLASS-based implementation of Kimi Delta Attention kernels. Achieves 1.72×–2.22× prefill speedup over the flash-linear-attention baseline on H20, and works as a drop-in backend for flash-linear-attention. Explore on github: