Test Suites

Intel® ESQ provides a comprehensive collection of test suites to assess and qualify your edge system capabilities. Choose from qualification tests with pass/fail criteria, data collection suites for analysis, or industry-specific vertical tests.

Table of Contents

• Available Test Suites
• Test Suite Types
• Qualifications
  • AI Edge System Qualification
• Vertical
  • Manufacturing
  • Metro
  • Retail
    • Automated Self Checkout
    • Loss Prevention
• Horizontal
  • Generative AI
  • Vision AI
    • DL Streamer Analysis - Multi-Stream Pipelines With Multiple AI Stages
    • DL Streamer Analysis - Verified Reference Blueprints
    • OpenVINO
    • Video Analytics
  • System GPU - OpenVINO
  • System Memory - STREAM
  • Media Performance
  • Timeseries AI

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Available Test Suites

Quick reference of all available test suites and their profile names.

Profile Name	Category	Description	Run Command
profile.qualification.ai-edge-system	Qualification	Intel® AI Edge Systems qualification	esq run --profile profile.qualification.ai-edge-system
profile.vertical.manufacturing	Vertical	Manufacturing	esq run --profile profile.vertical.manufacturing
profile.vertical.metro	Vertical	Metro proxy workloads (LPR, Smart NVR, Visual AI, VSaaS)	esq run --profile profile.vertical.metro
profile.vertical.retail-asc	Vertical	Retail Automated Self-Checkout	esq run --profile profile.vertical.retail-asc
profile.vertical.retail-lp	Vertical	Retail Loss Prevention	esq run --profile profile.vertical.retail-lp
profile.vertical.retail-lp-vlm	Vertical	Retail Loss Prevention Visual Language Model	esq run --profile profile.vertical.retail-lp-vlm
profile.suite.ai.gen	Horizontal	Gen AI profile	esq run --profile profile.suite.ai.gen
profile.suite.ai.timeseries-wind-turbine	Horizontal	Timeseries AI profile - Wind Turbine Skeleton	esq run --profile profile.suite.ai.timeseries-wind-turbine
profile.suite.ai.vision-light	Horizontal	DL Streamer Analysis - Multi-Stream Pipelines With Multiple AI Stages	esq run --profile profile.suite.ai.vision-light
profile.suite.ai.vision-ov	Horizontal	OpenVINO™ Toolkit Benchmark - Measures raw inference performance using OpenVINO Runtime API	esq run --profile profile.suite.ai.vision-ov
profile.suite.ai.vision-va	Horizontal	Multi-stage video analytics pipelines with detection, tracking, and classification	esq run --profile profile.suite.ai.vision-va
profile.suite.ai.vision-vrb	Horizontal	Vision AI profile - Verified Reference Blueprints	esq run --profile profile.suite.ai.vision-vrb
profile.suite.media.performance-pipelines	Horizontal	Media Performance	esq run --profile profile.suite.media.performance-pipelines
profile.suite.system.gpu-ov	Horizontal	System GPU Performance using OpenVINO™ Toolkit benchmark	esq run --profile profile.suite.system.gpu-ov
profile.suite.system.memory-stream	Horizontal	System Memory Performance using STREAM benchmark	esq run --profile profile.suite.system.memory-stream

List all available profiles:

esq list

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Test Suite Types

Test Suite	Purpose	Benefit
Qualifications	Measuring system performance to qualify against Intel® AI Edge Systems Qualifications Metrics	Gain Catalog inclusion and other marketing benefits from Intel.
Vertical	System benchmarking vertical specific proxy workloads like retail self checkout, smart NVR and manufacturing defect detection	Gain understanding and communicate on system's potential to be used in a variety of verticals and use-cases
Horizontal	General system benchmarking (includes OpenVINO™ Toolkit, Audio, Memory Performance)	Gain understanding on system's resource utilization and performance like System memory and GPU during select AI workload

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Qualifications

Intel® AI Edge Systems Qualification

Profile: profile.qualification.ai-edge-system

Test Cases:

Generative AI test on text generation

Tier	Test ID	Test Case	Qualification Criteria
Entry	AES-GEN-001	Gen AI LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-1.5B INT4	>= 10.0 tokens/sec
Mainstream	AES-GEN-001	Gen AI LLM Serving Benchmark - Phi-4-mini-reasoning 3.8B INT4	>= 10.0 tokens/sec
Efficiency Optimized	AES-GEN-001	Gen AI LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-7B INT4	>= 10.0 tokens/sec
Scalable Performance	AES-GEN-001	Gen AI LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-14B INT4	>= 10.0 tokens/sec
Scalable Performance Graphics Media	AES-GEN-001	Gen AI LLM Serving Benchmark - Phi-4-mini-reasoning 3.8B INT4 Gen AI LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-14B INT4 Gen AI LLM Serving Benchmark - Qwen3-32B INT4	>= 10.0 tokens/sec

Vision AI test using Intel® DL Streamer

Tier	Test ID	Test Case	Qualification Criteria
Entry	AES-VSN-001	Vision AI Analysis - multi-stream 1080p15 H.264 gvadetect YOLO11n INT8 gvatrack gvaclassify EfficientNet-B0 INT8	>= 4.0 streams
Mainstream	AES-VSN-001	Vision AI Analysis - multi-stream 1080p15 H.264 gvadetect YOLO11n INT8 gvatrack gvaclassify EfficientNet-B0 INT8	>= 8.0 streams
Efficiency Optimized	AES-VSN-001	Vision AI Analysis - multi-stream 1080p15 H.264 gvadetect YOLO11n INT8 gvatrack gvaclassify EfficientNet-B0 INT8	>= 25.0 streams
Scalable Performance	AES-VSN-001	Vision AI Analysis - multi-stream 1080p15 H.264 gvadetect YOLO11n INT8 gvatrack gvaclassify EfficientNet-B0 INT8	>= 10.0 streams
Scalable Performance Graphics Media	AES-VSN-001	Vision AI Analysis - multi-stream 1080p15 H.264 gvadetect YOLO11n INT8 gvatrack gvaclassify EfficientNet-B0 INT8	>= 40.0 streams

Run this profile:

esq run --profile profile.qualification.ai-edge-system

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Vertical

Manufacturing

Profile: profile.vertical.manufacturing

Test Case:

Test ID	Test Case
MFG-PDD-001	Pallet Defect Detection - multi-stream 480p30 H.264 gvadetect YOLOX-TINY INT8 (CPU)
MFG-PDD-002	Pallet Defect Detection - multi-stream 480p30 H.264 gvadetect YOLOX-TINY INT8 (iGPU)
MFG-PDD-003	Pallet Defect Detection - multi-stream 480p30 H.264 gvadetect YOLOX-TINY INT8 (dGPU)
MFG-PDD-004	Pallet Defect Detection - multi-stream 480p30 H.264 gvadetect YOLOX-TINY FP32 (CPU)
MFG-PDD-005	Pallet Defect Detection - multi-stream 480p30 H.264 gvadetect YOLOX-TINY FP32 (iGPU)
MFG-PDD-006	Pallet Defect Detection - multi-stream 480p30 H.264 gvadetect YOLOX-TINY FP32 (dGPU)
MFG-PDD-007	Pallet Defect Detection - multi-stream 480p30 H.264 gvadetect YOLOX-TINY FP16 (CPU)
MFG-PDD-008	Pallet Defect Detection - multi-stream 480p30 H.264 gvadetect YOLOX-TINY FP16 (iGPU)
MFG-PDD-009	Pallet Defect Detection - multi-stream 480p30 H.264 gvadetect YOLOX-TINY FP16 (dGPU)
MFG-WPC-001	Weld Porosity Classification - multi-stream 1024p30 H.264 gvaclassify EfficientNet-B0 FP16 (CPU)
MFG-WPC-002	Weld Porosity Classification - multi-stream 1024p30 H.264 gvaclassify EfficientNet-B0 FP16 (iGPU)
MFG-WPC-003	Weld Porosity Classification - multi-stream 1024p30 H.264 gvaclassify EfficientNet-B0 FP16 (dGPU)
MFG-WPC-004	Weld Porosity Classification - multi-stream 1024p30 H.264 gvaclassify EfficientNet-B0 INT8 (CPU)
MFG-WPC-005	Weld Porosity Classification - multi-stream 1024p30 H.264 gvaclassify EfficientNet-B0 INT8 (iGPU)
MFG-WPC-006	Weld Porosity Classification - multi-stream 1024p30 H.264 gvaclassify EfficientNet-B0 INT8 (dGPU)
MFG-WPC-007	Weld Porosity Classification - multi-stream 1024p30 H.264 gvaclassify EfficientNet-B0 FP32 (CPU)
MFG-WPC-008	Weld Porosity Classification - multi-stream 1024p30 H.264 gvaclassify EfficientNet-B0 FP32 (iGPU)
MFG-WPC-009	Weld Porosity Classification - multi-stream 1024p30 H.264 gvaclassify EfficientNet-B0 FP32 (dGPU)

Run this profile:

esq run --profile profile.vertical.manufacturing

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Metro

Profile: profile.vertical.metro

Test Cases:

Test ID	Test Case
METRO-PROXY-001	LPR Pipeline (Multi-Devices) - LPR proxy workload on iGPU and dGPU for multi-device throughput scaling.
METRO-PROXY-002	Smart NVR (iGPU) - Smart NVR proxy workload on iGPU with display output for real-time analytics.
METRO-PROXY-003	Smart NVR (dGPU) - Smart NVR proxy workload on dGPU for high-density stream analytics.
METRO-PROXY-004	Headed Visual AI Proxy Pipeline (iGPU) - Headed Visual AI proxy workload on iGPU with display output for interactive analytics.
METRO-PROXY-005	Headed Visual AI Proxy Pipeline (dGPU) - Headed Visual AI proxy workload on dGPU with display output for interactive analytics.
METRO-PROXY-006	VSaaS Visual AI Proxy Pipeline (iGPU) - VSaaS Visual AI proxy workload on iGPU with multi-model inference and encode stages.
METRO-PROXY-007	VSaaS Visual AI Proxy Pipeline (dGPU) - VSaaS Visual AI proxy workload on dGPU for scalable stream analytics.

Run this profile:

esq run --profile profile.vertical.metro

Note: Running esq run --profile profile.vertical.metro also runs dependent profiles: profile.suite.system.memory-stream, profile.suite.system.gpu-ov, profile.suite.ai.vision-ov, profile.suite.media.performance-pipelines, and profile.suite.ai.vision-va. Total execution time depends on your hardware capabilities and available accelerators.

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Retail

Automated Self Checkout

Profile: profile.vertical.retail-asc

Test Cases:

Test ID	Test Case
RTL-ASC-001	Automated Self Checkout - multi-stream 1920p15 H.264 gvadetect YOLO11n INT8 (CPU)
RTL-ASC-002	Automated Self Checkout - multi-stream 1920p15 H.264 gvadetect YOLO11n INT8 (iGPU)
RTL-ASC-003	Automated Self Checkout - multi-stream 1920p15 H.264 gvadetect YOLO11n INT8 (dGPU)
RTL-ASC-004	Automated Self Checkout - multi-stream 1920p15 H.264 gvadetect YOLO11n INT8 (NPU)

Run this profile:

esq run --profile profile.vertical.retail-asc

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Loss Prevention

Profile: profile.vertical.retail-lp

Test Cases:

Test ID	Test Case
RTL-LPP-001	Loss Prevention - multi-stream 1080p15 Items-in-Basket H.264 gvadetect YOLO11n INT8 gvatrack gvaclassify EfficientNet-B0 INT8 (iGPU)
RTL-LPP-002	Loss Prevention - multi-stream 1080p15 Hidden-Items-Product-Switching H.264 gvadetect YOLO11n INT8 gvatrack gvaclassify EfficientNet-B0 INT8 (iGPU)
RTL-LPP-003	Loss Prevention - multi-stream 1080p15 Fake-Scan-Detection H.264 gvadetect YOLO11n INT8 gvatrack gvaclassify EfficientNet-B0 INT8 (iGPU)
RTL-LPP-004	Loss Prevention - multi-stream 1080p15 H.264 gvadetect YOLO11n INT8 gvatrack gvaclassify EfficientNet-B0 INT8 (iGPU)

Run this profile:

esq run --profile profile.vertical.retail-lp

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Profile: profile.vertical.retail-lp-vlm

Test Cases:

Test ID	Test Case
LP-VLM-001	Loss Prevention VLM - 1080p15 H.264 gvadetect YOLO11n FP16 (CPU) VLM analysis Qwen2.5-VL-7B-Instruct INT8 (GPU)

Run this profile:

esq run --profile profile.vertical.retail-lp-vlm

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Horizontal

Generative AI

LLM Serving Benchmark

Profile: profile.suite.ai.gen-llm

Test Cases (click to expand)

Test ID	Test Case
GEN-LLM-001	LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-1.5B INT4 (CPU)
GEN-LLM-002	LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-1.5B INT4 (iGPU)
GEN-LLM-003	LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-1.5B INT4 (dGPU)
GEN-LLM-004	LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-1.5B INT4 (Hetero dGPU)
GEN-LLM-005	LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-1.5B INT4 (NPU)
GEN-LLM-006	LLM Serving Benchmark - Phi-4-mini-reasoning 3.8B INT4 (CPU)
GEN-LLM-007	LLM Serving Benchmark - Phi-4-mini-reasoning 3.8B INT4 (iGPU)
GEN-LLM-008	LLM Serving Benchmark - Phi-4-mini-reasoning 3.8B INT4 (dGPU)
GEN-LLM-009	LLM Serving Benchmark - Phi-4-mini-reasoning 3.8B INT4 (Hetero dGPU)
GEN-LLM-010	LLM Serving Benchmark - Phi-4-mini-reasoning 3.8B INT4 (NPU)
GEN-LLM-011	LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-7B INT4 (CPU)
GEN-LLM-012	LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-7B INT4 (iGPU)
GEN-LLM-013	LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-7B INT4 (dGPU)
GEN-LLM-014	LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-7B INT4 (Hetero dGPU)
GEN-LLM-015	LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-7B INT4 (NPU)
GEN-LLM-016	LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-14B INT4 (CPU)
GEN-LLM-017	LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-14B INT4 (iGPU)
GEN-LLM-018	LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-14B INT4 (dGPU)
GEN-LLM-019	LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-14B INT4 (Hetero dGPU)
GEN-LLM-020	LLM Serving Benchmark - DeepSeek-R1-Distill-Qwen-14B INT4 (NPU)
GEN-LLM-021	LLM Serving Benchmark - Qwen3-32B INT4 (CPU)
GEN-LLM-022	LLM Serving Benchmark - Qwen3-32B INT4 (iGPU)
GEN-LLM-023	LLM Serving Benchmark - Qwen3-32B INT4 (dGPU)
GEN-LLM-024	LLM Serving Benchmark - Qwen3-32B INT4 (Hetero dGPU)
GEN-LLM-025	LLM Serving Benchmark - Qwen3-32B INT4 (NPU)
GEN-LLM-026	LLM Serving Benchmark - DeepSeek-R1-Distill-Llama-70B INT4 (CPU)
GEN-LLM-027	LLM Serving Benchmark - DeepSeek-R1-Distill-Llama-70B INT4 (iGPU)
GEN-LLM-028	LLM Serving Benchmark - DeepSeek-R1-Distill-Llama-70B INT4 (dGPU)
GEN-LLM-029	LLM Serving Benchmark - DeepSeek-R1-Distill-Llama-70B INT4 (Hetero dGPU)
GEN-LLM-030	LLM Serving Benchmark - DeepSeek-R1-Distill-Llama-70B INT4 (NPU)

Run this profile:

esq run --profile profile.suite.ai.gen-llm

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Chat Question and Answer Core

Profile: profile.suite.ai.gen-chatqna-core

Benchmarks the Intel® Edge AI Chat Question and Answer Core sample application. Tests document ingestion, query latency (P50/P95), time to first token (TTFT), and estimated output throughput across OpenVINO* CPU, OpenVINO* GPU, and Ollama* CPU backends.

Suite-specific notes

• GEN-CHAT-002 requires an Intel® GPU driver to be installed and visible to Docker*.
• The first run downloads and converts the LLM model (~30–45 minutes for OpenVINO* CPU). Subsequent runs reuse the cached model and start faster.
• A Hugging Face* token (HUGGINGFACEHUB_API_TOKEN) is only needed if you use gated or private model assets.

Test Cases:

Test ID	Test Case	Backend
GEN-CHAT-001	Chat Q&A Core - OpenVINO* CPU	OpenVINO* CPU
GEN-CHAT-002	Chat Q&A Core - OpenVINO* GPU	OpenVINO* GPU (Intel® iGPU)
GEN-CHAT-003	Chat Q&A Core - Ollama* CPU	Ollama* CPU

Metrics collected:

Metric	Description
service_startup_seconds	Time from container start to service ready
document_ingestion_seconds	Time to ingest benchmark corpus
p50_query_latency_ms	Median end-to-end query latency
p95_query_latency_ms	95th percentile query latency
ttft_ms	Time to first token (streaming)
estimated_output_tokens_per_second	Output throughput estimate
query_success_rate	Fraction of queries that succeeded

Run this profile:

esq run --profile profile.suite.ai.gen-chatqna-core

Run a specific test case:

esq -d run --profile profile.suite.ai.gen-chatqna-core --filter test_id=GEN-CHAT-001

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Vision AI

DL Streamer Analysis - Multi-Stream Pipelines With Multiple AI Stages

Profile: profile.suite.ai.vision-light

Test Cases:

Test ID	Test Case
VSN-LGT-001	DL Streamer Analysis - multi-stream 1080p30 H.265 gvadetect YOLO11n INT8 gvatrack gvaclassify ResNet50 INT8
VSN-LGT-002	DL Streamer Analysis - multi-stream 1080p30 H.265 gvadetect YOLO11n INT8 gvatrack gvaclassify ResNet50 INT8 (CPU)
VSN-LGT-003	DL Streamer Analysis - multi-stream 1080p30 H.265 gvadetect YOLO11n INT8 gvatrack gvaclassify ResNet50 INT8 (iGPU)
VSN-LGT-004	DL Streamer Analysis - multi-stream 1080p30 H.265 gvadetect YOLO11n INT8 gvatrack gvaclassify ResNet50 INT8 (dGPU)
VSN-LGT-005	DL Streamer Analysis - multi-stream 1080p30 H.265 gvadetect YOLO11n INT8 gvatrack gvaclassify ResNet50 INT8 (NPU)

Run this profile:

esq run --profile profile.suite.ai.vision-light

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Verified Reference Blueprints

Profile: profile.suite.ai.vision-vrb

Test Cases:

Test ID	Test Case
VSN-VRB-001	DL Streamer Analysis - multi-stream 1080p15 H.264 gvadetect YOLO11n INT8 gvatrack gvaclassify EfficientNet-B0 INT8
VSN-VRB-002	DL Streamer Analysis - multi-stream 1080p15 H.264 gvadetect YOLO11n INT8 gvatrack gvaclassify EfficientNet-B0 INT8 (CPU)
VSN-VRB-003	DL Streamer Analysis - multi-stream 1080p15 H.264 gvadetect YOLO11n INT8 gvatrack gvaclassify EfficientNet-B0 INT8 (iGPU)
VSN-VRB-004	DL Streamer Analysis - multi-stream 1080p15 H.264 gvadetect YOLO11n INT8 gvatrack gvaclassify EfficientNet-B0 INT8 (dGPU)
VSN-VRB-005	DL Streamer Analysis - multi-stream 1080p15 H.264 gvadetect YOLO11n INT8 gvatrack gvaclassify EfficientNet-B0 INT8 (NPU)

Run this profile:

esq run --profile profile.suite.ai.vision-vrb

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OpenVINO

Profile: profile.suite.ai.vision-ov

Test Cases (click to expand)

Test ID	Test Case
VSN-OBM-001	OpenVINO Benchmark - resnet-50-tf INT8 (iGPU)
VSN-OBM-002	OpenVINO Benchmark - resnet-50-tf INT8 (dGPU)
VSN-OBM-003	OpenVINO Benchmark - resnet-50-tf INT8 (NPU)
VSN-OBM-004	OpenVINO Benchmark - efficientnet-b0 INT8 (iGPU)
VSN-OBM-005	OpenVINO Benchmark - efficientnet-b0 INT8 (dGPU)
VSN-OBM-006	OpenVINO Benchmark - efficientnet-b0 INT8 (NPU)
VSN-OBM-007	OpenVINO Benchmark - ssdlite_mobilenet_v2 INT8 (iGPU)
VSN-OBM-008	OpenVINO Benchmark - ssdlite_mobilenet_v2 INT8 (dGPU)
VSN-OBM-009	OpenVINO Benchmark - ssdlite_mobilenet_v2 INT8 (NPU)
VSN-OBM-010	OpenVINO Benchmark - mobilenet-v2-pytorch INT8 (iGPU)
VSN-OBM-011	OpenVINO Benchmark - mobilenet-v2-pytorch INT8 (dGPU)
VSN-OBM-012	OpenVINO Benchmark - mobilenet-v2-pytorch INT8 (NPU)
VSN-OBM-013	OpenVINO Benchmark - yolo-v5s INT8 (iGPU)
VSN-OBM-014	OpenVINO Benchmark - yolo-v5s INT8 (dGPU)
VSN-OBM-015	OpenVINO Benchmark - yolo-v5s INT8 (NPU)
VSN-OBM-016	OpenVINO Benchmark - yolo-v8s INT8 (iGPU)
VSN-OBM-017	OpenVINO Benchmark - yolo-v8s INT8 (dGPU)
VSN-OBM-018	OpenVINO Benchmark - clip-vit-base-patch16 INT8 (iGPU)
VSN-OBM-019	OpenVINO Benchmark - clip-vit-base-patch16 INT8 (dGPU)

Run this profile:

esq run --profile profile.suite.ai.vision-ov

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Video Analytics

Profile: profile.suite.ai.vision-va

Test Cases:

Test ID	Test Case
VSN-LIGHT-001	VA Light - All Available Devices (YOLO11n + ResNet-50, H.265)
VSN-MEDIUM-001	VA Medium - All Available Devices (YOLOv5m + ResNet-50 + MobileNet-v2, H.265)
VSN-HEAVY-001	VA Heavy - All Available Devices (YOLO11m + ResNet-v1-50 + MobileNet-v2, H.265)

Run this profile:

esq run --profile profile.suite.ai.vision-va

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System GPU - OpenVINO™ Toolkit

Profile: profile.suite.system.gpu-ov

Test Cases:

Test ID	Test Case
GPU-OBM-001	AI GPU Frequency Measure - OV Benchmark yolo-v5s FP16 (iGPU)
GPU-OBM-002	AI GPU Frequency Measure - OV Benchmark yolo-v5s FP16 (dGPU)

Run this profile:

esq run --profile profile.suite.system.gpu-ov

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System Memory - STREAM

Profile: profile.suite.system.memory-stream

Test Cases:

Test ID	Test Case
MEM-STR-001	STREAM Memory Benchmark - Copy
MEM-STR-002	STREAM Memory Benchmark - Scale
MEM-STR-003	STREAM Memory Benchmark - Add
MEM-STR-004	STREAM Memory Benchmark - Triad

Run this profile:

esq run --profile profile.suite.system.memory-stream

Media Performance

Profile: profile.suite.media.performance-pipelines

Test Cases (click to expand)

Test ID	Test Case
MDA-DEC-001	Media Decode 4Mbps H.264 1080p@30 (iGPU)
MDA-DEC-002	Media Decode 16Mbps H.264 4k@30 (iGPU)
MDA-DEC-003	Media Decode 4Mbps H.264 1080p@30 (dGPU)
MDA-DEC-004	Media Decode 16Mbps H.264 4k@30 (dGPU)
MDA-DEC-005	Media Decode 2Mbps H.265 1080p@30 (iGPU)
MDA-DEC-006	Media Decode 8Mbps H.265 4k@30 (iGPU)
MDA-DEC-007	Media Decode 2Mbps H.265 1080p@30 (dGPU)
MDA-DEC-008	Media Decode 8Mbps H.265 4k@30 (dGPU)
MDA-COMP-001	Media Decode + Compose 4Mbps H.264 1080p@30 (iGPU)
MDA-COMP-002	Media Decode + Compose 16Mbps H.264 4k@30 (iGPU)
MDA-COMP-003	Media Decode + Compose 2Mbps H.265 1080p@30 (iGPU)
MDA-COMP-004	Media Decode + Compose 8Mbps H.265 4k@30 (iGPU)
MDA-COMP-005	Media Decode + Compose 4Mbps H.264 1080p@30 (dGPU)
MDA-COMP-006	Media Decode + Compose 16Mbps H.264 4k@30 (dGPU)
MDA-COMP-007	Media Decode + Compose 2Mbps H.265 1080p@30 (dGPU)
MDA-COMP-008	Media Decode + Compose 8Mbps H.265 4k@30 (dGPU)
MDA-ENC-001	Media Encode 4Mbps H.264 1080p@30 (iGPU)
MDA-ENC-002	Media Encode 16Mbps H.264 4k@30 (iGPU)
MDA-ENC-003	Media Encode 4Mbps H.264 1080p@30 (dGPU)
MDA-ENC-004	Media Encode 16Mbps H.264 4k@30 (dGPU)
MDA-ENC-005	Media Encode 2Mbps H.265 1080p@30 (iGPU)
MDA-ENC-006	Media Encode 8Mbps H.265 4k@30 (iGPU)
MDA-ENC-007	Media Encode 2Mbps H.265 1080p@30 (dGPU)
MDA-ENC-008	Media Encode 8Mbps H.265 4k@30 (dGPU)

Run this profile:

esq run --profile profile.suite.media.performance-pipelines

Timeseries AI

Profile: profile.suite.ai.timeseries-wind-turbine

Test Cases (click to expand)

Test ID	Test Case
TS-WT-CONS-001	Wind Turbine Timeseries - Combined Functional Flow
TS-WT-001	TS Wind Turbine - s40p500 CPU OPCUA
TS-WT-002	TS Wind Turbine - s40p500 GPU OPCUA
TS-WT-003	TS Wind Turbine - s40p500 CPU MQTT
TS-WT-004	TS Wind Turbine - s40p500 GPU MQTT

Run this profile:

esq run --profile profile.suite.ai.timeseries-wind-turbine

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