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timm/fastvit_mci2.apple_mclip

Embedl-optimized
timm fastvit_mci2.apple_mclip architecture graph
timm fastvit_mci2.apple_mclip architecture graph
  • timm/fastvit_mci2.apple_mclip
  • INT8
  • L4
Labels
  • Image
Build
Embedl-optimized
Precision
INT8
Runtime
TensorRT 11.3.0.99 with Embedl optimizations
Input size
256×256
Run fastvit_mci2.apple_mclip on L4

fastvit_mci2.apple_mclip is an image encoder from timm: it turns an image into a 512-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 fastvit_mci2/apple_mclip-timm_int8_embedl_tensorrt_l4, on an NVIDIA L4.

Files

Image Size

Model

docker.io/embedl/model-artifacts:fastvit_mci2.apple_mclip-timm_int8_embedl_tensorrt_l4.762f7980123de539

0.50 GiB

Runtime

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

0.52 GiB