GPU selection guidance

AI inference, natural language processing, computer vision, video analytics, rendering, and scientific visualization all rely on GPUs, but not all GPU workloads are created equal. While some applications require massive memory footprints for complex, large-scale models, others are dominated by real-time video processing, 8K streaming, or certified design workflows. Choosing the right hardware is critical to balancing performance and cost-efficiency. This guide outlines the technical specifications and workload optimizations for our GPU fleet to help you select the ideal architecture for your specific deployment needs.

Overview of differences

NVIDIA RTX PRO 6000™ Blackwell Server Edition GPU is a high-end, fifth-generation Blackwell architecture GPU designed for demanding professional tasks, AI inference at scale, and large memory-intensive workloads. It features significantly more memory and higher computational throughput, enabling it to serve larger models, higher concurrency, and real-time production inference in distributed and edge contexts.

NVIDIA RTX 4000™ Ada Series GPU is a mid-tier GPU based on the Ada Lovelace architecture. It offers balanced performance, relatively low power consumption, and a modest VRAM footprint, making it suitable for entry-to-mid workloads and development environments. The Ada architecture provides efficient tensor and CUDA core performance at lower cost and power.

NVIDIA Quadro RTX 6000™ GPU is a high-end professional GPU based on the Turing architecture, designed for workstation and data-intensive visualization and compute workloads. It delivers strong FP32 performance, dedicated RT cores for real-time ray tracing, and Tensor Cores for AI-accelerated rendering and inference. With a VRAM capacity and high memory bandwidth, it is well suited for large datasets, complex CAD and DCC scenes, simulation, and multi-application workflows that exceed the memory limits of mid-tier cards. The Turing architecture prioritizes stability, precision, and professional driver support over raw gaming performance.

NVIDIA GPU specification comparison

Choosing the right GPU for your workload

GPU performance varies significantly across different architectures. Each model is optimized for specific inference or graphics tasks.

Supported models for NVIDIA NIM for LLMs

The NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs available on Linode are compatible with a range of Large Language Models (LLMs) validated by NVIDIA through the NVIDIA Inference Microservices (NIM) platform. The following table lists select example models and quantization formats that have been validated by NVIDIA to run on Blackwell in a single‑GPU configuration.

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This section is intended as a compatibility reference only and does not guarantee specific throughput, latency, or performance characteristics. Actual performance depends on workload characteristics, model configuration, quantization settings, concurrency, context length, and other deployment parameters.

The model list below provides select examples and is not exhaustive. Additional models may be supported. For the most current and complete listing, refer to the NVIDIA NIM Supported Models documentation.

Some models may require specific quantization profiles to fit within the memory of a single GPU. The quantization profile used can affect output quality and behavior.