…Modern AI workloads—including reasoning, mixture-of-experts (MoE), long-context inference, and reinforcement learning—are not limited by peak floating point operations (FLOPS) alone. They are constrained by whether execution efficiency…
…He earlier co-founded Efflux Systems, a cybersecurity and machine learning company acquired by NETSCOUT. John began his career in the U.S. Air Force as a cyberspace operations officer supporting intelligence…
…delivering the bandwidth needed for agentic inference and reinforcement learning environments. For AI factories, this translates into better performance per watt, lower operating costs, and more efficient use of power and cooling…
…Figure 15. Learn more This post has illustrated the breadth of tensor storage formats encompassed by the UST. Stay tuned for the integration of the UST with polymorphic operations that dispatch to…
…workloads, complete GPU observability is a prerequisite for operating that infrastructure efficiently. The GPU Usage Monitor makes that observability accessible in minutes. Learn more Access the comprehensive Helm chart for monitoring GPU…
…Because the combine operator performs high-accuracy accumulation operations on tokens, which are currently performed only at the CUDA core within the SM, these operations must be accumulated hierarchically. In the multi…
…The weights might come from GPU memory, host memory, or storage Reinforcement learning : Streams updated weights from learners to actors with minimal transfer overhead Elastic expert parallelism : Dispatches and combine stages in…
…The profiling revealed that while the heavy General Matrix Multiplication (GEMM) operations (experts and attention) were performing well, significant latency bubbles existed in the non-compute-intensive operations—specifically in the MoE…
…The operational shift is mindset. Stop optimizing for one headline metric and start optimizing for a range of real-world operating points. Learn more Dive deeper into the architecture behind NVIDIA Groq…
…this post, you’ll need: Familiarity with geometric deep learning foundation models like AlphaFold3 or Boltz-2. Understanding of PyTorch Distributed (DTensor) operations and custom autograd functions. Access to an NVIDIA H100…