Develop High-Performance GPU Kernels in C++ with NVIDIA CUDA Tile | NVIDIA Technical Blog
… First, for best performance, the input and output arrays should only be accessed through their respective pointers while the kernel is running. …
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Running AI Workloads on Rack-Scale Supercomputers: From Hardware to Topology-Aware Scheduling | NVIDIA Technical Blog
… By doing this, ComputeDomains make the high-performance fabric first-class in scheduling . …
Maximize AI Infrastructure Throughput by Consolidating Underutilized GPU Workloads | NVIDIA Technical Blog
… One workload can’t impact the performance or memory stability of another. …
NVIDIA CUDA 13.3 Enhances GPU Development with Tile Programming in C++, Compiler Autotuning, and Python Updates | NVIDIA Technical Blog
… PCG64 is the default PRNG in Numpy and provides a good balance between quality and performance. include include global void sample kernel { cuda::pcg64 rng threadIdx.x ; cuda::std::normal distribution dist 0.0f, 1.0f ; float sample = dist rng ; } Search: cub::DeviceFind::FindIf CCCL 3.3 adds cub::D… …
Advancing Emerging Optimizers for Accelerated LLM Training with NVIDIA Megatron | NVIDIA Technical Blog
… Check out the Megatron Bridge performance recipes . …
Achieving Peak System and Workload Efficiency on NVIDIA GB200 NVL72 with Slurm Block Scheduling | NVIDIA Technical Blog
… For example, to define two GB200 NVL72 domains use the following script: --- - topology: gb200-nvl72 cluster default: true block: block sizes: - 18 blocks: - block: block01 nodes: node 0001-0018 - block: block02 nodes: node 0019-0036 The Slurm topology/block plugin supports multiple levels of hiera… …
Scaling the AI-Ready Data Center with NVIDIA RTX PRO 4500 Blackwell Server Edition and NVIDIA vGPU 20 | NVIDIA Technical Blog
… To do this, first ensure the VM is powered off. …
Controlling Floating-Point Determinism in NVIDIA CCCL | NVIDIA Technical Blog
… Determinism performance comparison The level of determinism selected affects the performance of cub::DeviceReduce . Not-guaranteed determinism, with its relaxed requirements, provides the highest performance. …
Accelerating Long-Context Model Training in JAX and XLA | NVIDIA Technical Blog
… Communication backend comparison Each configuration was evaluated with two communication backends: NCCL baseline NVSHMEM-enabled implementation Measurements: TFLOP/s per device : GPU computational throughput Step time seconds : Time per training iteration Speedup : Relative performance improvement … …
Removing the Guesswork from Disaggregated Serving | NVIDIA Technical Blog
… HiSim also aids HiCache architecture exploration and cost/performance optimization through three-level KV cache design e.g., L2 size, prefetch/eviction policy, L3 bandwidth needs, write-through vs write-back to find the best cost–performance point. …