High-Performance GPU Programming & Inference

NVIDIA AI and Tools

• CUTLASS: A collection of CUDA C++ templates designed for high-performance matrix multiplication.
• RAFT: A suite of GPU-accelerated machine learning primitives.
• cuDNN: A library for GPU-accelerated deep neural network operations.

Deployment and Inference Options

• TensorRT-LLM: Implements LoRA adapters and merged checkpoints for on-device inference.
• llama.cpp: Supports LoRA adapters for on-device inference.
• ONNX Runtime - DML: Enables LoRA adapters for on-device inference.
• vLLM: Supports cloud inference with LoRA adapters and merged checkpoints.
• NVIDIA Inference Microservices (NIMs): Provides cloud inference options with LoRA adapter support.

NVIDIA-Specific Tooling

• spaCy: An open-source Python library for advanced NLP tasks, including tokenization and named entity recognition.
• NumPy: Fundamental library for scientific computing in Python, supporting large, multi-dimensional arrays and mathematical functions.
• NVIDIA RAPIDS: Open-source platform for GPU-accelerated data analytics and machine learning, integrating with popular data science tools.
• cuDF: A GPU DataFrame library that uses Apache Arrow format, offering a pandas-like API for manipulating tabular data.
• Dask cuDF: Extends cuDF for parallel computing across multiple GPUs, handling larger-than-memory datasets.
• cuML: Suite of fast, GPU-accelerated machine learning algorithms with both GPU and CPU execution capabilities.
• cuGraph: Part of NVIDIA RAPIDS, this library provides high-performance graph algorithms for large-scale analytics.
• Apache Arrow: Cross-language development platform defining a standardized columnar memory format for data.
• NVIDIA NeMo: An open-source toolkit for building conversational AI models, supporting speech recognition and text-to-speech tasks.
• NVIDIA Triton: Inference server that simplifies AI model deployments across various frameworks and hardware.
• TensorRT: High-performance inference library optimizing neural networks for NVIDIA GPUs.
• NCCL: Library for inter-GPU communication, implementing collective operations optimized for NVIDIA GPUs.

NVIDIA NeMo Framework

• NeMo: End-to-end enterprise framework for building and deploying generative AI models.
• NeMo Core: Contains foundational elements like the Neural Module Factory for training and inference.
• NeMo Collections: Specialized modules for automatic speech recognition (ASR), natural language processing (NLP), and text-to-speech (TTS).
• NeMo Curator: Tool for preparing high-quality datasets for large-language model (LLM) pretraining.
• NeMo Customizer: Scalable microservice for fine-tuning LLMs to domain-specific needs.
• NeMo Retriever: Low-latency retrieval tool to enhance generative AI applications.
• NeMo Guardrails: Tool to enforce programmable constraints on LLM outputs.

NVIDIA AI Enterprise

• Cloud-native suite including over 50 frameworks and pretrained models, optimized for GPU infrastructures.
• NVIDIA AI Workbench: Manages data, models, and resources for generative AI development.
• NVIDIA Base Command: Tool for managing large-scale AI workloads on multi-node configurations.
• NVIDIA AI Inference Manager (AIM) SDK: Unified interface for deploying AI models across various devices.
• NVIDIA RTX AI Toolkit: Tools and SDKs for customizing and deploying AI models on RTX PCs and cloud environments.

Key Features and Capabilities

• TRT-LLM: Leverages tensor parallelism for efficient multi-GPU execution and supports multiple precisions for better quantization.
• Inflight Batching: Improves GPU utilization, doubles throughput, and lowers energy costs through continuous request processing.
• Model Quantization: Enhances throughput, latency, and scalability, requiring testing for specific use cases.
• Windows Compatibility: Runs LLMs on Windows via Triton Inference Server, ensuring low latency and data privacy.
• Multimodal LLMs: Capable of processing both text and images, enabling new applications, exemplified by NEMO Vision and Language Assistant.
• LLM Agents and Plugins: Allows reasoning, task execution, and enhanced capabilities through plugins.

Glossary of NVIDIA-Specific Tooling

• TRT-LLM: Optimization library for LLM inference.
• TensorRT: Deep learning compiler for NVIDIA GPUs.
• NVLink: High-bandwidth interconnect for GPUs.
• CUDA: Parallel computing platform for GPUs.
• NGC: Hub for GPU-optimized software.
• DGX Cloud: NVIDIA's cloud service for AI development.
• NEMO: Framework for generative AI models.
• Triton Inference Server: Scalable AI model serving software.
• WSL: Windows Subsystem for Linux.
• NVIDIA AI Enterprise: Software suite for AI development and deployment.

This quiz explores advanced concepts in GPU programming, covering CUDA C++ templates for matrix multiplication, GPU-accelerated machine learning primitives, and deep neural network libraries. Test your knowledge on deployment options like TensorRT-LLM and ONNX Runtime. Perfect for those studying high-performance computing and machine learning techniques.