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NVIDIA GPU Performance Showcase

Explore performance characteristics and best use cases for NVIDIA's most popular GPUs

🟩
NVIDIA GeForce RTX 3060
+
✓ Best For
Entry-level fine-tunes, lightweight inference, and image generation jobs.
💡 Usage Tip
Great for low-cost batch inference; use quantization to fit larger models.
VRAM
12 GB
CUDA Cores
3,584
AI Perf.
40%
🟦
NVIDIA GeForce RTX 3080
+
✓ Best For
High-throughput SDXL generations, multi-model deployments, and real-time inference.
💡 Usage Tip
Use Torch-TensorRT to squeeze the most out of its 10GB of VRAM.
VRAM
10 GB
CUDA Cores
8,704
AI Perf.
70%
🟥
NVIDIA GeForce RTX 4090
+
✓ Best For
High-performance fine-tunes (e.g., LLaMA 7B, Mistral), SDXL rendering, and multi-instance jobs.
💡 Usage Tip
Use mixed precision training with PyTorch AMP for huge speedups.
VRAM
24 GB
CUDA Cores
16,384
AI Perf.
90%
⚙️
NVIDIA A100 Tensor Core GPU
+
✓ Best For
Enterprise-grade training, large batch inference, and multi-user scheduling.
💡 Usage Tip
Perfect for long-form LLM training or high-batch LoRA jobs.
VRAM
80 GB
Tensor Cores
432
AI Perf.
95%
🚀
NVIDIA H100 Tensor Core GPU
+
✓ Best For
Training GPT-class models, massive vision transformers, and enterprise-level inference APIs.
💡 Usage Tip
Ideal for multi-node distributed training and inference pipelines.
VRAM
80 GB
Tensor Cores
528
AI Perf.
100%
Key Takeaways
1
RTX 3060–3080: Great for affordability, test runs, or parallel inference jobs.
2
RTX 4090: Best value for fine-tuning and creator-heavy workloads.
3
A100 & H100: Go-to for serious training, long-running jobs, and professional-grade reliability.