Graphics Processing Units (GPU) – Inference
by NVIDIA (dominant ecosystem vendor; GPUs also produced by AMD, Intel, others) • Santa Clara, California, USA (NVIDIA) • Founded 1993
Graphics processing units (GPUs) are massively parallel processors originally designed for rendering graphics that are now widely used to accelerate AI and machine learning inference workloads. For inference, GPUs execute large numbers of matrix and tensor operations concurrently, dramatically reducing latency and increasing throughput versus general‑purpose CPUs. They matter because they underpin most production-scale deep learning services, from recommendation systems to generative AI, enabling cost-effective, high-performance deployment of trained models.
Key Features
- •Massively parallel architecture with thousands of cores optimized for matrix/tensor math
- •Specialized units (e.g., Tensor Cores) for mixed-precision deep learning inference (FP16, INT8, INT4)
- •High memory bandwidth (HBM/GDDR) for fast access to model parameters and activations
- •Mature software stack (CUDA, cuDNN, TensorRT, ROCm, oneAPI) and framework integrations (PyTorch, TensorFlow, JAX)
- •Scalability from single-GPU servers to multi-GPU nodes and large clusters with NVLink/NVSwitch/PCIe
- •Support for multi-tenant and virtualized deployments (MIG, vGPU) in data centers and clouds
- •Ecosystem of optimized libraries, compilers, and deployment tools for inference optimization
Use Cases
- •Real-time inference for computer vision (object detection, segmentation, video analytics)
- •Online recommendation and ranking systems (ads, e-commerce, content feeds)
- •Generative AI inference (LLMs, image generation, speech synthesis, code assistants)
- •Conversational AI and speech (ASR, TTS, translation, chatbots)
- •Batch inference for large-scale scoring (fraud detection, risk modeling, personalization)
- •Scientific and engineering simulations requiring fast surrogate models
- •On-premise and edge inference in appliances, gateways, and industrial systems
Adoption
Used By
Performance Benchmarks
Funding
Alternatives
TPUs (Tensor Processing Units)
Inference AcceleratorsCustom ASICs optimized for TensorFlow and large-scale Google Cloud workloads; offer high performance-per-watt for specific deep learning operations.
AWS Inferentia / Trainium
Inference AcceleratorsAmazon-designed ASICs for cost-optimized training and inference on AWS, tightly integrated with AWS ML services.
AMD Instinct / Radeon GPUs
Inference AcceleratorsGPU accelerators using ROCm software stack, positioned as open alternative to NVIDIA for AI training and inference.
Intel Gaudi / GPU Max Series
Inference AcceleratorsIntel’s AI accelerators and GPUs targeting data center training and inference with oneAPI and Habana software stacks.
Edge AI ASICs (e.g., Google Edge TPU, Hailo, Mythic)
Edge Inference AcceleratorsSpecialized low-power chips for on-device inference in embedded and IoT scenarios.