Best GPU for YOLOv8 (FPS + Cost Efficiency)

What YOLOv8 Demands from a GPU

YOLOv8 by Ultralytics is the go-to model for real-time object detection, segmentation, and pose estimation. Running it efficiently on a dedicated GPU server requires understanding how different hardware affects throughput. Unlike LLMs, YOLO inference is compute-bound: it saturates tensor cores rather than memory bandwidth, which means raw TFLOPS is the primary performance driver.

We benchmarked YOLOv8 across seven GPUs available on GigaGPU vision model hosting, testing detection inference at multiple resolutions and model sizes. Full interactive results are also available on our YOLOv8 FPS by GPU benchmark page.

YOLOv8 FPS Benchmarks by GPU

Primary benchmark: YOLOv8x (the largest and most accurate model) at 640×640 input resolution, FP16 precision, batch size 1. This represents the most demanding standard YOLO configuration.

The RTX 5090 and 5090 dominate on raw FPS. But notice the RTX 3090 hitting 82 FPS on YOLOv8x, which is more than enough for real-time video processing (30-60 FPS targets). Even YOLOv8n on the RTX 3090 exceeds 365 FPS, leaving headroom for pre/post-processing in the pipeline.

Performance at Different Resolutions

Higher input resolution improves detection of small objects but requires more compute. Here is how YOLOv8x scales with resolution on each GPU.

At 1920×1088 (full HD), even the RTX 3090 drops to 15 FPS on YOLOv8x. If you need real-time full-HD detection, you will want the RTX 5090+ or switch to a lighter model like YOLOv8m (which hits ~42 FPS at 1920px on the 3090). The RTX 3090 vs 5090 comparison covers how these cards differ across other workloads too.

YOLOv8 Model Size Comparison (Nano to X)

YOLOv8 ships in five sizes. Choosing the right one is often more impactful than upgrading your GPU.

YOLO models are lightweight on VRAM. Even YOLOv8x needs only ~5 GB, which means VRAM capacity is rarely the bottleneck. This is purely a compute game, and the GPU with the most tensor TFLOPS wins. All models fit easily on every GPU, including the RTX 4060.

Cost per Million Frames

For batch video processing, surveillance systems, and data labelling pipelines, cost per frame matters more than peak FPS.

The RTX 3090 and RTX 4060 are virtually tied on cost efficiency for YOLOv8. The 4060 wins by a tiny margin per frame, but the 3090 provides 2.2x higher throughput, which means you process your dataset in less than half the time. For most production deployments, the 3090 is the practical winner. Compare this to other workloads in our cheapest GPU for AI inference guide.

GPU Recommendations by Use Case

Real-time single-stream video (30 FPS target):

• YOLOv8x at 640px: RTX 3090 (82 FPS, 2.7x headroom)
• YOLOv8x at 1920px: RTX 5090 or RTX 5090 (27-35 FPS, just meeting target)

Multi-stream video analytics (4-8 cameras):

• YOLOv8m at 640px with batching: RTX 5090 (265 FPS / 8 streams = 33 FPS per stream)
• Budget alternative: RTX 3090 with YOLOv8s (252 FPS / 8 = 31 FPS per stream)
• For more cameras: multi-GPU clusters

Batch processing (dataset labelling, video archives):

• Best cost efficiency: RTX 3090 at $1.52 per million frames
• Fastest turnaround: RTX 5090 at 195 FPS on YOLOv8x

For computer vision workloads that also require OCR, see our PaddleOCR hosting option. Many vision pipelines combine YOLO detection with downstream models on the same GPU, and the 3090’s 24 GB VRAM gives ample room for this. Browse all options in our GPU comparisons section.

FAQ

Is the RTX 4060 fast enough for real-time YOLO?

At 640×640 with YOLOv8n, the 4060 hits 178 FPS, which is far above real-time. Even YOLOv8x runs at 38 FPS. So yes, for single-stream detection the 4060 handles it. The limitation is multi-stream or high-resolution scenarios where you need the 3090’s extra compute.

Does VRAM matter for YOLOv8?

Not much. The largest model (YOLOv8x) uses only ~5 GB. VRAM becomes relevant when running YOLO alongside other models (e.g., an LLM for describing detected objects) or when using very large batch sizes. For pure YOLO inference, compute TFLOPS is what matters.

How does YOLOv8 compare to YOLOv9 or YOLOv10 in terms of GPU requirements?

YOLOv9 and YOLOv10 have similar compute profiles to YOLOv8. The FPS rankings between GPUs remain essentially the same, though absolute numbers shift slightly. Our benchmarks focus on YOLOv8 as it remains the most deployed version, but the GPU recommendations apply to the newer versions as well.

Should I use a PyTorch or TensorRT backend?

TensorRT inference is 1.5-2x faster than native PyTorch for YOLOv8. Our benchmarks use PyTorch for consistency across GPUs, but in production you should export to TensorRT for maximum throughput. The GPU rankings do not change with TensorRT; all cards benefit proportionally.