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MLPerf Inference Benchmarks

Overview

The currently valid MLPerf Inference Benchmarks as of MLPerf inference v5.0 round are listed below, categorized by tasks. Under each model you can find its details like the dataset used, reference accuracy, server latency constraints etc.

Image Classification

ResNet50-v1.5

  • Dataset: Imagenet-2012 (224x224) Validation
    • Dataset Size: 50,000
    • QSL Size: 1,024
  • Number of Parameters: 25.6 million
  • FLOPs: 3.8 billion
  • Reference Model Accuracy: 76.46% ACC
  • Server Scenario Latency Constraint: 15ms
  • Equal Issue mode: False
  • High accuracy variant: No
  • Submission Category: Datacenter, Edge

Text to Image

Stable Diffusion

  • Dataset: Subset of Coco2014
    • Dataset Size: 5,000
    • QSL Size: 5,000
  • Number of Parameters: 3.5 billion
  • FLOPs: 1.28 - 2.4 trillion
  • Reference Model Accuracy (fp32): CLIP: 31.74981837, FID: 23.48046692
  • Required Accuracy (Closed Division):
    • CLIP: 31.68631873 ≤ CLIP ≤ 31.81331801 (within 0.2% of the reference model CLIP score)
    • FID: 23.01085758 ≤ FID ≤ 23.95007626 (within 2% of the reference model FID score)
  • Equal Issue mode: False
  • High accuracy variant: No
  • Submission Category: Datacenter, Edge

Object Detection

Retinanet

  • Dataset: OpenImages
    • Dataset Size: 24,781
    • QSL Size: 64
  • Number of Parameters: TBD
  • Reference Model Accuracy (fp32) : 0.3755 mAP
  • Server Scenario Latency Constraint: 100ms
  • Equal Issue mode: False
  • High accuracy variant: Yes
  • Submission Category: Datacenter, Edge

Medical Image Segmentation

3d-unet

  • Dataset: KiTS2019
    • Dataset Size: 42
    • QSL Size: 42
  • Number of Parameters: 32.5 million
  • FLOPs: 100-300 billion
  • Reference Model Accuracy (fp32) : 0.86330 Mean DICE Score
  • Server Scenario: Not Applicable
  • Equal Issue mode: True
  • High accuracy variant: Yes
  • Submission Category: Datacenter, Edge

Language Tasks

Question Answering

Bert-Large

  • Dataset: Squad v1.1 (384 Sequence Length)
    • Dataset Size: 10,833
    • QSL Size: 10,833
  • Number of Parameters: 340 million
  • FLOPs: ~128 billion
  • Reference Model Accuracy (fp32) : F1 Score = 90.874%
  • Server Scenario Latency Constraint: 130ms
  • Equal Issue mode: False
  • High accuracy variant: yes
  • Submission Category: Edge

LLAMA2-70B

  • Dataset: OpenORCA (GPT-4 split, max_seq_len=1024)
    • Dataset Size: 24,576
    • QSL Size: 24,576
  • Number of Parameters: 70 billion
  • FLOPs: ~500 trillion
  • Reference Model Accuracy (fp32) :
    • Rouge1: 44.4312
    • Rouge2: 22.0352
    • RougeL: 28.6162
    • Tokens_per_sample: 294.45
  • Server Scenario Latency Constraint:
    • TTFT: 2000ms
    • TPOT: 200ms
  • Equal Issue mode: True
  • High accuracy variant: Yes
  • Submission Category: Datacenter

Text Summarization

GPT-J

  • Dataset: CNN Daily Mail v3.0.0
    • Dataset Size: 13,368
    • QSL Size: 13,368
  • Number of Parameters: 6 billion
  • FLOPs: ~148 billion
  • Reference Model Accuracy (fp32) :
    • Rouge1: 42.9865
    • Rouge2: 20.1235
    • RougeL: 29.9881
    • Gen_len: 4,016,878
  • Server Scenario Latency Constraint: 20s
  • Equal Issue mode: True
  • High accuracy variant: Yes
  • Submission Category: Datacenter, Edge

Mixed Tasks (Question Answering, Math, and Code Generation)

Mixtral-8x7B

  • Datasets:
    • OpenORCA (5k samples of GPT-4 split, max_seq_len=2048)
    • GSM8K (5k samples of the validation split, max_seq_len=2048)
    • MBXP (5k samples of the validation split, max_seq_len=2048)
    • Dataset Size: 15,000
    • QSL Size: 15,000
  • Number of Parameters: 47 billion
  • Reference Model Accuracy (fp16) :
    • OpenORCA
      • Rouge1: 45.4911
      • Rouge2: 23.2829
      • RougeL: 30.3615
    • GSM8K Accuracy: 73.78%
    • MBXP Accuracy: 60.12%
  • Tokens_per_sample: 294.45
  • Server Scenario Latency Constraint:
    • TTFT: 2000ms
    • TPOT: 200ms
  • Equal Issue mode: True
  • High accuracy variant: Yes
  • Submission Category: Datacenter

Recommendation

DLRM_v2

  • Dataset: Synthetic Multihot Criteo
    • Dataset Size: 204,800
    • QSL Size: 204,800
  • Number of Parameters: ~23 billion
  • Reference Model Accuracy: AUC = 80.31%
  • Server Scenario Latency Constraint: 60ms
  • Equal Issue mode: False
  • High accuracy variant: Yes
  • Submission Category: Datacenter

Graph Neural Networks

R-GAT

  • Dataset: Illinois Graph Benchmark Heterogeneous validation dataset
    • Dataset Size: 788,379
    • QSL Size: 788,379
  • Number of Parameters:
  • Reference Model Accuracy: ACC = 72.86%
  • Server Scenario Latency Constraint: N/A
  • Equal Issue mode: True
  • High accuracy variant: No
  • Submission Category: Datacenter

Automotive

3D Object Detection

PointPainting

  • Dataset: Waymo
    • Dataset Size: 39,986
    • QSL Size: 1,024
  • Number of Parameters: 44 million
  • FLOPs: 3 trillion
  • Reference Model Accuracy (fp32): mAP: 54.25%
  • Required Accuracy (Closed Division):
    • mAP: 54.25%
  • Equal Issue mode: False
  • High accuracy variant: Yes
  • Submission Category: Edge

Submission Categories

  • Datacenter Category: All benchmarks except bert are applicable to the datacenter category for inference v5.0.
  • Edge Category: All benchmarks except DLRMv2, LLAMA2-70B, Mixtral-8x7B and R-GAT are applicable to the edge category for v5.0.

High Accuracy Variants

  • Benchmarks: bert, llama2-70b, gpt-j, dlrm_v2, and 3d-unet have a normal accuracy variant as well as a high accuracy variant.
  • Requirement: Must achieve at least 99.9% of the reference model accuracy, compared to the default 99% accuracy requirement.