Direct performance comparison between the RTX A4000 and RTX 3090 across 19 standardized AI benchmarks collected from our production fleet. Testing shows the RTX A4000 with no wins across 19 benchmarks, while the RTX 3090 wins all 19 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 A4000 is 72% slower than the RTX 3090 (median across 1 benchmarks). For Qwen/Qwen3-4B, the RTX A4000 reaches 163 tokens/s while RTX 3090 achieves 583 tokens/s (72% slower). The RTX A4000 wins none out of 1 high-throughput tests, making the RTX 3090 better suited for production API workloads.
For personal AI assistants and local development with one request at a time, the RTX A4000 is 47% slower than the RTX 3090 (median across 4 benchmarks). Running llama3.1:8b, the RTX A4000 generates 76 tokens/s while RTX 3090 achieves 145 tokens/s (48% slower). The RTX A4000 wins none out of 4 single-user tests, making the RTX 3090 the better choice for local AI development.
For Stable Diffusion, SDXL, and Flux workloads, the RTX A4000 is 31% slower than the RTX 3090 (median across 10 benchmarks). Testing sdxl, the RTX A4000 completes at 7.9 s/image while RTX 3090 achieves 5.4 s/image (32% slower). The RTX A4000 wins none out of 10 image generation tests, making the RTX 3090 the better choice for Stable Diffusion workloads.
For high-concurrency vision workloads (16-64 parallel requests), the RTX A4000 delivers 54% lower throughput than the RTX 3090 (median across 2 benchmarks). Testing llava-1.5-7b, the RTX A4000 processes 42 images/min while RTX 3090 achieves 147 images/min (71% slower). The RTX A4000 wins none out of 2 vision tests, making the RTX 3090 the better choice for high-throughput vision AI workloads.
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Our benchmarks are collected automatically from servers having GPUs of type RTX A4000 and RTX 3090 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 A4000 and RTX 3090 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 A4000 and RTX 3090 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 A4000 and RTX 3090 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 A4000 and RTX 3090 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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