Direct performance comparison between the A100 and RTX 4090 Pro across 26 standardized AI benchmarks collected from our production fleet. Testing shows the A100 winning 11 out of 26 benchmarks (42% win rate), while the RTX 4090 Pro wins 15 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 A100 is 35% slower than the RTX 4090 Pro (median across 2 benchmarks). For Qwen/Qwen3-8B, the A100 reaches 550 tokens/s while RTX 4090 Pro achieves 810 tokens/s (32% slower). The A100 wins none out of 2 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, both the A100 and RTX 4090 Pro deliver nearly identical response times across 8 Ollama benchmarks. Running llama3.1:8b-instruct-q8_0, the A100 generates 124 tokens/s vs RTX 4090 Pro's 108 tokens/s (15% faster). The A100 wins 1 out of 8 single-user tests, making the RTX 4090 Pro the better choice for local AI development.
For Stable Diffusion, SDXL, and Flux workloads, both the A100 and RTX 4090 Pro perform nearly identically across 12 benchmarks. Testing sd3.5-medium, the A100 completes at 8.9 images/min while RTX 4090 Pro achieves 9.7 images/min (8% slower). The A100 wins 6 out of 12 image generation tests, showing both GPUs are equally suitable for image generation.
For high-concurrency vision workloads (16-64 parallel requests), both the A100 and RTX 4090 Pro deliver nearly identical throughput across 2 benchmarks. Testing llava-1.5-7b, the A100 processes 282 images/min vs RTX 4090 Pro's 266 images/min (6% faster). The A100 wins 1 out of 2 vision tests, showing both GPUs handle production vision workloads equally well.
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Our benchmarks are collected automatically from servers having GPUs of type A100 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 A100 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 A100 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 A100 and RTX 4090 Pro 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 A100 and RTX 4090 Pro 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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