Direct performance comparison between the RTX 5090 and RTX 4090 Pro across 27 standardized AI benchmarks collected from our production fleet. Testing shows the RTX 5090 winning 20 out of 27 benchmarks (74% win rate), while the RTX 4090 Pro wins 7 tests. All benchmark results are automatically gathered from active rental servers, providing real-world performance data.
For production API servers and multi-agent AI systems running multiple concurrent requests, the RTX 5090 is 28% slower than the RTX 4090 Pro (median across 3 benchmarks). For Qwen/Qwen3-4B, the RTX 5090 reaches 954 tokens/s while RTX 4090 Pro achieves 1318 tokens/s (28% slower). The RTX 5090 wins none out of 3 high-throughput tests, making the RTX 4090 Pro better suited for production API workloads.
For personal AI assistants and local development with one request at a time, the RTX 5090 is 50% faster than the RTX 4090 Pro (median across 8 benchmarks). Running deepseek-r1:32b, the RTX 5090 generates 71 tokens/s vs RTX 4090 Pro's 45 tokens/s (59% faster). The RTX 5090 wins 8 out of 8 single-user tests, making it ideal for personal coding assistants and prototyping.
For Stable Diffusion, SDXL, and Flux workloads, the RTX 5090 is 31% faster than the RTX 4090 Pro (median across 12 benchmarks). Testing sd3.5-medium, the RTX 5090 completes at 4.5 s/image vs RTX 4090 Pro's 6.2 s/image (38% faster). The RTX 5090 wins 10 out of 12 image generation tests, making it the preferred GPU for AI art and image generation.
For high-concurrency vision workloads (16-64 parallel requests), the RTX 5090 delivers 30% higher throughput than the RTX 4090 Pro (median across 2 benchmarks). Testing trocr-base, the RTX 5090 processes 1976 pages/min vs RTX 4090 Pro's 1468 pages/min (35% faster). The RTX 5090 wins 2 out of 2 vision tests, making it the preferred GPU for production-scale document processing and multimodal AI.
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Our benchmarks are collected automatically from servers having GPUs of type RTX 5090 and RTX 4090 Pro in our fleet. Unlike synthetic lab tests, these results come from real production servers handling actual AI workloads - giving you transparent, real-world performance data.
We test both vLLM (High-Throughput) and Ollama (Single-User) frameworks. vLLM benchmarks show how RTX 5090 and RTX 4090 Pro perform with 16-64 concurrent requests - perfect for production chatbots, multi-agent AI systems, and API servers. Ollama benchmarks measure single-request speed for personal AI assistants and local development. Models tested include Llama 3.1, Qwen3, DeepSeek-R1, and more.
Image generation benchmarks cover Flux, SDXL, and SD3.5 architectures. That's critical for AI art generation, design prototyping, and creative applications. Focus on single prompt generation speed to understand how RTX 5090 and RTX 4090 Pro handle your image workloads.
Vision benchmarks test multimodal and document processing with high concurrent load (16-64 parallel requests) using real-world test data. LLaVA 1.5 7B (7B parameter Vision-Language Model) analyzes a photograph of an elderly woman in a flower field with a golden retriever, testing scene understanding and visual reasoning at batch size 32 to report images per minute. TrOCR-base (334M parameter OCR model) processes 2,750 pages of Shakespeare's Hamlet scanned from historical books with period typography at batch size 16, measuring pages per minute for document digitization. See how RTX 5090 and RTX 4090 Pro handle production-scale visual AI workloads - critical for content moderation, document processing, and automated image analysis.
We also include CPU compute power (affecting tokenization and preprocessing) and NVMe storage speeds (critical for loading large models and datasets) - the complete picture for your AI workloads.
The TAIFlops (Trooper AI FLOPS) score shown in the first row combines all AI benchmark results into a single number. Using the RTX 3090 as baseline (100 TAIFlops), this score instantly tells you how RTX 5090 and RTX 4090 Pro compare overall for AI workloads. Learn more about TAIFlops →
Note: Results may vary based on system load and configuration. These benchmarks represent median values from multiple test runs.
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