Skip to content

timm/vit_huge_patch16_sapiens2.fb

Embedl-optimized
timm vit_huge_patch16_sapiens2.fb architecture graph
timm vit_huge_patch16_sapiens2.fb architecture graph
  • timm/vit_huge_patch16_sapiens2.fb
  • INT8
  • L4
Labels
  • Image
Build
Embedl-optimized
Precision
INT8
Runtime
TensorRT 11.3.0.99 with Embedl optimizations
Input size
1024×768
Run vit_huge_patch16_sapiens2.fb on L4

vit_huge_patch16_sapiens2.fb is an image encoder from timm: it turns an image into a 1,280-dimensional embedding, for search, retrieval and as features for other models.

Methodology

  • Latency: trtexec on the NVIDIA L4, batch 1, 201 iterations after warm-up, during the build: GPU compute time per inference, mean and p99, and throughput.
  • Quantization: Embedl Deploy, activations INT8, weights INT8 per-channel; calibrated on Imagenette (1,000 images). Exported as ONNX opset 20, compiled with TensorRT 11.3.0.99, built on NVIDIA L4 (sm_89).
  • Build: config vit_huge_patch16_sapiens2/fb-timm_int8_embedl_tensorrt_l4, on an NVIDIA L4.

Files

Image Size

Model

docker.io/embedl/model-artifacts:vit_huge_patch16_sapiens2.fb-timm_int8_embedl_tensorrt_l4.b5c19027ddd7b79c

11.24 GiB

Runtime

docker.io/embedl/models:runtimes.tensorrt.amd64.2026101815014ee0

0.52 GiB