Dan Fu

next week at @aiDotEngineer, we are joining @togethercompute for a conversation on what goes into running agents at scale. @olive_jy_song, Research Lead, RL at MiniMax, and @realDanFu, VP of Kernels at Together AI, will walk through both sides of M3: the training decisions behind long-context reasoning and tool use, and the infrastructure work needed to serve agentic workloads in production. Wed July 1, 2:50 PM PDT Room 2016, Track 9 Catch you there!

Samir Menon @blintzbase and I are thrilled to announce Sail @sailresearchco ! We build infrastructure for long-horizon agents: inference served at unbeatable prices-per-token for open models, plus sandboxes designed to run for days, weeks, or longer. We've raised $80M, w/ our seed led by @Sequoia and series A led by @KleinerPerkins. We're using this capital to build the most efficient infrastructure for long-horizon agents. What makes agents so different? Unlike a human waiting at a keyboard (top priority: speed), agents need scale, reliability, and sustainable cost. Sail finds this efficiency everywhere in the stack: we carefully choose our chips, write custom inference engines, and run a global controller that fully utilizes every computer in our fleet. Tight integration from silicon to API lets Sail open up the cost / latency frontier to our customers - the most patient agents can now access 10x more intelligence per dollar. We're excited to be working with great companies like @parallelweb, @detaildotdev,@Jackandjillai, and @quadrillion_ai to deploy long-horizon agents with trillions of tokens. Our team is thoughtful in our engineering craft and relentlessly ambitious in our pursuit of peak performance. We previously trained at companies like NVIDIA, OpenAI, Google, and so many trading firms. Now we're ready to do the work that will define our careers, in the most compute intensive market of all time. Welcome to the era of abundant intelligence. We can't wait to build with you!

LLMs write fast single-GPU kernels. Ask for a multi-GPU one and they fall apart. ParallelKernelBench measures how they fail by benchmarking against 87 problems pulled from real codebases including Megatron-LM, DeepSpeed, DeepEP, TensorRT-LLM, NeMo-RL. New research from Willy Chan @asplencmnt @simonguozirui @simran_s_arora and @realDanFu

LLMs write fast single-GPU kernels. Ask for a multi-GPU one and they fall apart. ParallelKernelBench measures how they fail by benchmarking against 87 problems pulled from real codebases including Megatron-LM, DeepSpeed, DeepEP, TensorRT-LLM, NeMo-RL. New research from Willy Chan @asplencmnt @simonguozirui @simran_s_arora and @realDanFu