Direct performance comparison between the RTX 4080 Super Pro and A100 across 26 standardized AI benchmarks collected from our production fleet. Testing shows the RTX 4080 Super Pro winning 5 out of 26 benchmarks (19% win rate), while the A100 wins 21 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 4080 Super Pro is 37% slower than the A100 (median across 2 benchmarks). For Qwen/Qwen3-4B, the RTX 4080 Super Pro reaches 549 tokens/s while A100 achieves 826 tokens/s (33% slower). The RTX 4080 Super Pro wins none out of 2 high-throughput tests, making the A100 better suited for production API workloads.
For personal AI assistants and local development with one request at a time, the RTX 4080 Super Pro is 15% slower than the A100 (median across 8 benchmarks). Running llama3.1:8b-instruct-q8_0, the RTX 4080 Super Pro generates 82 tokens/s while A100 achieves 124 tokens/s (34% slower). The RTX 4080 Super Pro wins 1 out of 8 single-user tests, making the A100 the better choice for local AI development.
For Stable Diffusion, SDXL, and Flux workloads, the RTX 4080 Super Pro is 20% slower than the A100 (median across 12 benchmarks). Testing sdxl, the RTX 4080 Super Pro completes at 17 images/min while A100 achieves 23 images/min (29% slower). The RTX 4080 Super Pro wins none out of 12 image generation tests, making the A100 the better choice for Stable Diffusion workloads.
For high-concurrency vision workloads (16-64 parallel requests), the RTX 4080 Super Pro delivers 34% lower throughput than the A100 (median across 2 benchmarks). Testing llava-1.5-7b, the RTX 4080 Super Pro processes 175 images/min while A100 achieves 282 images/min (38% slower). The RTX 4080 Super Pro wins none out of 2 vision tests, making the A100 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 4080 Super Pro and A100 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 4080 Super Pro and A100 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 4080 Super Pro and A100 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 4080 Super Pro and A100 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 4080 Super Pro and A100 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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