Direct performance comparison between the A100 and V100 across 26 standardized AI benchmarks collected from our production fleet. Testing shows the A100 winning 25 out of 26 benchmarks (96% win rate), while the V100 wins 1 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 189% faster than the V100 (median across 2 benchmarks). For Qwen/Qwen3-8B, the A100 achieves 550 tokens/s vs V100's 251 tokens/s (119% faster). The A100 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 A100 is 32% faster than the V100 (median across 8 benchmarks). Running gpt-oss:20b, the A100 generates 150 tokens/s vs V100's 113 tokens/s (32% faster). The A100 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 A100 is 226% faster than the V100 (median across 12 benchmarks). Testing sd3.5-medium, the A100 completes at 6.7 s/image vs V100's 51 s/image (663% faster). The A100 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 A100 delivers 275% higher throughput than the V100 (median across 2 benchmarks). Testing llava-1.5-7b, the A100 processes 282 images/min vs V100's 53 images/min (434% faster). The A100 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 A100 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 A100 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 A100 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 A100 and V100 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 A100 and V100 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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