Results for “gpu-acceleration”

Reduces peak GPU memory in Megatron Bridge training by applying expandable segments, parallelism resizing, activation recompute, and CPU offloading constraints.

Accelerate pandas workflows with GPU DataFrames using cuDF and dask-cuDF for ETL, joins, groupby, and large-scale data processing.

Build, optimize, backtest, rebalance, or analyze stock portfolios using NVIDIA-accelerated Mean-CVaR optimization with cuOpt GPU solver.

Diagnose and optimize slow-loading, high-memory, or low-FPS USD scenes using a structured workflow with profiling, validation, and mutation phases.

Guide users through installing CUDA-Q, writing quantum kernels, running GPU-accelerated simulations, connecting to QPU hardware, and exploring built-in applications.

Guides optimization of Qdrant query latency by tuning segments, memory, quantization, and search parameters.

Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers.

Optimize GSAP animations for smooth 60fps by preferring transforms, avoiding layout thrashing, using will-change, batching reads and writes, and following best practices for stagger, quickTo, and ScrollTrigger performance.

Optimize Qdrant vector search performance through indexing strategies, query tuning, memory management, and hardware considerations.

Write, review, and migrate code using NVIDIA DALI's imperative dynamic-mode API for efficient data loading and preprocessing.