Direct performance comparison between the RTX 3090 and V100 across 26 standardized AI benchmarks collected from our production fleet. Testing shows the RTX 3090 winning 24 out of 26 benchmarks (92% win rate), while the V100 wins 2 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 3090 is 100% faster than the V100 (median across 2 benchmarks). For Qwen/Qwen3-8B, the RTX 3090 achieves 365 tokens/s vs V100's 251 tokens/s (45% faster). The RTX 3090 wins 2 out of 2 high-throughput tests, making it the stronger choice for production chatbots and batch processing.
For personal AI assistants and local development with one request at a time, the RTX 3090 is 23% faster than the V100 (median across 8 benchmarks). Running gpt-oss:20b, the RTX 3090 generates 144 tokens/s vs V100's 113 tokens/s (27% faster). The RTX 3090 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 3090 is 36% faster than the V100 (median across 12 benchmarks). Testing sdxl-turbo, the RTX 3090 completes at 0.41 s/image vs V100's 0.66 s/image (60% faster). The RTX 3090 wins 12 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 3090 delivers 97% higher throughput than the V100 (median across 2 benchmarks). Testing llava-1.5-7b, the RTX 3090 processes 147 images/min vs V100's 53 images/min (178% faster). The RTX 3090 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 3090 and V100 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 3090 and V100 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 3090 and V100 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 3090 and V100 handle production-scale visual AI workloads - critical for content moderation, document processing, and automated image analysis.
También incluimos el poder de cómputo del CPU (que afecta la tokenización y preprocesamiento) y las velocidades de almacenamiento NVMe (críticas para cargar modelos grandes y conjuntos de datos) – la visión completa para sus cargas de trabajo de IA.
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 3090 and V100 compare overall for AI workloads. Learn more about TAIFlops →
Nota: Los resultados pueden variar según la carga del sistema y su configuración. Estos benchmarks representan valores medios de múltiples ejecuciones de prueba.
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