NIXL is an open source library for accelerating point-to-point data transfers in AI inference frameworks. NIXL provides a single, easy-to-use API that can be used to address a variety of data transfer challenges within these frameworks while maintaining maximum performance. This API supports multiple technologies such as RDMA, GPU-initiated networking, GPU-Direct storage, block and file storage, and advanced cloud storage options including S3 over RDMA and Azure Blob Storage. It is vendor-agnostic and can run across diverse environments. For example, it supports Amazon Web Services (AWS) with
Enhancing Distributed Inference Performance with the NVIDIA Inference Transfer Library | NVIDIA Technical Blog…Prior to NVIDIA, she worked at Google for over a decade in various engineering and management roles and holds a degree in computer science from the University of Texas at Austin. View…
…Additionally, OpenFold’s throughput was enhanced through the integration of the NVIDIA cuEquivariance library and NVIDIA TensorRT SDK . These modular libraries and SDKs can be leveraged to accelerate operations common in protein…
…Advancing KV caching to storage In Dynamo 1.0, KV Block Manager (KVBM) introduces several features that enhance flexibility, visibility, and deployment options: Object storage support: KVBM now works with the Amazon…
…The math libraries provide expanded support for high-performance emulated libraries, and CUDA Core Compute Libraries (CCCL) continue to add both performance and feature improvements, providing C++ developers with a high-performance…
…low latency point-to-point inference data transfer library that accelerates the transfer of KV cache between GPUs and across heterogeneous memory and storage types. KV Block Manager : A cost-aware KV…
…NVIDIA Dynamo and NIXL coordinate KV block management and prestaging across the hierarchy, with DOCA providing KV communication and storage interfaces that treat context as a first-class resource. Operating the AI…
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…Lower-precision formats significantly reduce parameter and activation storage while preserving FP32 optimizer state, enabling higher throughput and larger batch sizes without compromising training stability. Low-precision training with NeMo Megatron Bridge…
…First, the data collection phase generates performance metrics from each rank and stores them individually in a JSON file, typically on shared storage. Then, the data analysis phase processes the data. This…
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