V100 vs RTX Pro 4000 Blackwell - GPU Benchmark Comparison

Direct performance comparison between the V100 and RTX Pro 4000 Blackwell across 45 standardized AI benchmarks collected from our production fleet. Testing shows the V100 winning 21 out of 45 benchmarks (47% win rate), while the RTX Pro 4000 Blackwell wins 24 tests. All benchmark results are automatically gathered from active rental servers, providing real-world performance data.

vLLM High-Throughput Inference: V100 55% faster

For production API servers and multi-agent AI systems running multiple concurrent requests, the V100 is 55% faster than the RTX Pro 4000 Blackwell (median across 3 benchmarks). For Qwen/Qwen3-4B, the V100 achieves 401 tokens/s vs RTX Pro 4000 Blackwell's 258 tokens/s (55% faster). The V100 wins 2 out of 3 high-throughput tests, making it the stronger choice for production chatbots and batch processing.

Ollama Single-User Inference: V100 roughly equal performance

For personal AI assistants and local development with one request at a time, both the V100 and RTX Pro 4000 Blackwell deliver nearly identical response times across 12 Ollama benchmarks. Running qwen3:32b, the V100 generates 30 tokens/s vs RTX Pro 4000 Blackwell's 9.6 tokens/s (211% faster). The V100 wins 9 out of 12 single-user tests, making it ideal for personal coding assistants and prototyping.

Image Generation: V100 29% slower

For Stable Diffusion, SDXL, and Flux workloads, the V100 is 29% slower than the RTX Pro 4000 Blackwell (median across 22 benchmarks). Testing sd3.5-medium, the V100 completes at 3.7 images/min vs RTX Pro 4000 Blackwell's 1.9 images/min (91% faster). The V100 wins 4 out of 22 image generation tests, making the RTX Pro 4000 Blackwell the better choice for Stable Diffusion workloads.

Vision AI: V100 13% lower throughput

For high-concurrency vision workloads (16-64 parallel requests), the V100 delivers 13% lower throughput than the RTX Pro 4000 Blackwell (median across 4 benchmarks). Testing llava-1.5-7b, the V100 processes 145 images/min vs RTX Pro 4000 Blackwell's 66 images/min (121% faster). The V100 wins 1 out of 4 vision tests, making the RTX Pro 4000 Blackwell the better choice for high-throughput vision AI workloads.

About These Benchmarks of V100 vs RTX Pro 4000 Blackwell

Our benchmarks are collected automatically from servers having GPUs of type V100 and RTX Pro 4000 Blackwell 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.

LLM Inference Benchmarks

We test both vLLM (High-Throughput) and Ollama (Single-User) frameworks. vLLM benchmarks show how V100 and RTX Pro 4000 Blackwell 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

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 V100 and RTX Pro 4000 Blackwell handle your image workloads.

Vision AI Benchmarks

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 V100 and RTX Pro 4000 Blackwell handle production-scale visual AI workloads - critical for content moderation, document processing, and automated image analysis.

System Performance

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.

TAIFlops Score

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 V100 and RTX Pro 4000 Blackwell 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.