RTX 5070 for Computer Vision: YOLOv11 and Real-Time Inference on 12 GB GDDR7

Computer Vision on RTX 5070

Computer vision inference is where the RTX 5070’s 12 GB constraint is least relevant — most CV models are tiny relative to LLMs or diffusion models. YOLOv8 Nano is ~6 MB; YOLOv8 XLarge is ~130 MB. Even running 5 simultaneous detection models uses under 1 GB of VRAM. The full 12 GB is available for batch image processing, multi-camera real-time pipelines, or parallel detection + LLM pipelines.

The RTX 5070’s advantages for CV: 6,144 CUDA cores for fast batch inference, TensorRT FP8 optimisation for maximum throughput, and GDDR7’s 672 GB/s bandwidth for fast image data transfers from CPU memory.

YOLO Object Detection

YOLO (You Only Look Once) is the standard real-time detection architecture. Ultralytics YOLOv8 and YOLOv11 support NVIDIA CUDA natively:

Even YOLOv8x at ~1.5 GB leaves 10.5 GB for other tasks — or for processing extremely large batch sizes (batch=512 at 640×640 uses ~4–5 GB).

Setup and Installation

pip install ultralytics

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8m.pt")   # auto-downloads if not present

# Run inference on image(s)
results = model("image.jpg", device="cuda")
results[0].show()   # display with bounding boxes

# Run on video
results = model("video.mp4", stream=True, device="cuda")
for r in results:
    print(r.boxes)  # process frame-by-frame

# Batch inference (maximises GPU utilisation)
import glob
images = glob.glob("/data/images/*.jpg")
results = model(images, batch=64, device="cuda")

For real-time webcam inference:

model = YOLO("yolov8n.pt")
results = model(source=0, stream=True, device="cuda", show=True)
for r in results:
    pass  # display runs automatically via show=True

Throughput Benchmarks

Throughput is measured in images per second at 640×640 input resolution, batch size 32:

Multi-Model Pipelines

12 GB fits several simultaneous CV models:

# Example: Detection + Classification + Segmentation simultaneously
detection = YOLO("yolov8m.pt").to("cuda")      # ~0.5 GB
classifier = YOLO("yolov8m-cls.pt").to("cuda") # ~0.5 GB
segmenter = YOLO("yolov8m-seg.pt").to("cuda")  # ~0.7 GB
# Total: ~1.7 GB — trivial on 12 GB

# Or: CV + LLM pipeline
# YOLO detects objects, LLM describes the scene
from ultralytics import YOLO
import ollama

yolo = YOLO("yolov8m.pt").to("cuda")  # ~0.5 GB on GPU
# Ollama LLM uses remaining 11.5 GB for Qwen 2.5 14B Q4 (~8.7 GB)

def describe_image(image_path):
    detections = yolo(image_path)[0]
    labels = [yolo.names[int(c)] for c in detections.boxes.cls]
    prompt = f"I can see: {', '.join(set(labels))}. Describe this scene."
    response = ollama.chat("qwen2.5:14b", [{"role":"user","content":prompt}])
    return response["message"]["content"]

TensorRT Acceleration

Export YOLO models to TensorRT for maximum throughput on Blackwell (FP8 path available):

from ultralytics import YOLO

model = YOLO("yolov8m.pt")

# Export to TensorRT (FP16)
model.export(format="engine", device="cuda", half=True)

# Or TensorRT with INT8 calibration
model.export(format="engine", device="cuda", int8=True,
             data="coco128.yaml")   # calibration dataset

# Load the .engine file for inference
trt_model = YOLO("yolov8m.engine")
results = trt_model("image.jpg", device="cuda")

TensorRT FP8 on Blackwell (5th Gen Tensor Cores) doubles throughput vs FP16 with negligible mAP loss on standard detection tasks.