GPU Benchmark Comparison for AI

Compare real-world performance across our GPU fleet for AI workloads. All benchmarks are collected automatically from running servers.

Performance:

Slower Faster

Colors are relative within each benchmark row

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All Comparisions

Explore these one by one comparision of GPUs:

How We Benchmark GPU Performance

Every GPU in our rental fleet undergoes continuous performance testing to provide you with transparent, real-world data. Unlike synthetic benchmarks that run in controlled lab environments, our results come from actual production servers handling real workloads. Each server automatically reports performance metrics multiple times throughout its lifecycle, creating a comprehensive dataset that reflects true operational capabilities rather than idealized scenarios.

Our GPU Fleet

Our infrastructure spans multiple GPU generations to serve different workload requirements and budgets. The RTX Pro 6000 Blackwell represents our flagship tier with massive VRAM capacity, ideal for training large models and running the biggest LLMs without quantization. The RTX 5090 delivers exceptional single-GPU performance with cutting-edge Ada Lovelace architecture, excelling at inference tasks where raw speed matters most.

For production AI workloads, the A100 remains the datacenter gold standard with tensor cores optimized for transformer architectures and excellent multi-instance GPU (MIG) support. The RTX 4090 and RTX 4090 Pro offer outstanding price-to-performance ratios, handling most LLM inference and image generation tasks with impressive efficiency. Our RTX 3090 fleet provides budget-friendly access to capable hardware, while V100 and RTX A4000 cards serve lighter workloads and development environments where cost optimization takes priority.

LLM Inference Testing

We evaluate language model performance using both Ollama and VLLM frameworks with FP8 quantization where supported. Our test suite includes models ranging from efficient 8B parameter variants like Llama 3.1 and Qwen3 up to demanding 70B+ models including DeepSeek-R1 and GPT-OSS. Token generation speed (tokens per second) directly determines how quickly your chatbots respond, how fast you can process documents, and overall user experience in conversational AI applications.

Image Generation Testing

Diffusion model benchmarks cover the complete spectrum from lightweight Stable Diffusion 1.5 to resource-intensive Flux and SD3.5-large architectures. We measure both throughput (images per minute) for batch processing scenarios and latency (seconds per image) for interactive applications. SDXL-Turbo results are particularly relevant for real-time generation, while standard SDXL and Flux benchmarks reflect quality-focused production workloads.

System Performance

GPU performance alone doesn't tell the complete story. Our benchmarks include CPU compute power (single-core and multi-core operations per second) which affects data preprocessing, tokenization, and model loading times. NVMe storage speeds determine how quickly you can load large datasets, checkpoint models, and swap between different AI projects. These factors become critical bottlenecks when working with large-scale training or serving multiple concurrent users.

Data Quality: All metrics represent averaged values from multiple test runs across different times and system states. Performance can fluctuate based on thermal conditions, concurrent workloads, and driver versions. Our historical data accumulation ensures increasingly accurate averages over time.

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