Direct performance comparison between the RTX 4070 Ti Super and RTX 3090 across 16 standardized AI benchmarks collected from our production fleet. Testing shows the RTX 4070 Ti Super winning 2 out of 16 benchmarks (13% win rate), while the RTX 3090 wins 14 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 4070 Ti Super is 59% slower than the RTX 3090 (median across 1 benchmarks). For Qwen/Qwen3-4B, the RTX 4070 Ti Super reaches 243 tokens/s while RTX 3090 achieves 591 tokens/s (59% slower). The RTX 4070 Ti Super 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 4070 Ti Super is 19% slower than the RTX 3090 (median across 3 benchmarks). Running qwen3:8b, the RTX 4070 Ti Super generates 99 tokens/s while RTX 3090 achieves 123 tokens/s (19% slower). The RTX 4070 Ti Super wins none out of 3 single-user tests, making the RTX 3090 the better choice for local AI development.
For Stable Diffusion, SDXL, and Flux workloads, the RTX 4070 Ti Super is 33% slower than the RTX 3090 (median across 8 benchmarks). Testing sd3.5-medium, the RTX 4070 Ti Super completes at 0.98 images/min while RTX 3090 achieves 1.7 images/min (42% slower). The RTX 4070 Ti Super wins 2 out of 8 image generation tests, making the RTX 3090 the better choice for Stable Diffusion workloads.
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Our benchmarks are collected automatically from servers having GPUs of type RTX 4070 Ti Super 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 4070 Ti Super 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 4070 Ti Super and RTX 3090 handle your image workloads.
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.
Note: Results may vary based on system load and configuration. These benchmarks represent median values from multiple test runs.
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