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ascend-ai-coding (@ascend-ai-coding) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Inferencex Report by ascend-ai-coding

Name: Inferencex Report
Author: ascend-ai-coding

Inferencex Report

Automatically fetch InferenceX benchmark data and generate daily performance reports for LLM inference on various hardware (NVIDIA, AMD, etc.). Supports email delivery, data change detection, and 8k1k sequence length performance analysis. Use when needing to track LLM inference performance trends, compare hardware configurations, or monitor benchmark updates.

ascend-ai-coding def7c53 4 files · 30.1 KB Updated Sep 17, 2026

File contents

InferenceX Report - LLM Inference Benchmark Tracker

Automatically fetch InferenceX benchmark data and generate daily performance reports for LLM inference on various hardware platforms.

Overview

This skill fetches real-time benchmark data from InferenceX API, generates comprehensive performance reports, and sends email notifications with:

Daily benchmark data summary
Hardware-Model-Framework performance matrix
8k1k sequence length detailed analysis
Data change detection (new combinations, performance changes)

Supported Models

Model	Records (2026-06-02)
DeepSeek-R1-0528	1,839
gpt-oss-120b	617
Llama-3.3-70B-Instruct-FP8	681
Qwen-3.5-397B-A17B	650
Kimi-K2.5	268
MiniMax-M2.5	840
GLM-5	367
DeepSeek-V4-Pro	607
Total	5,869

Supported Hardware

NVIDIA: B200, B300, GB200, GB300, H100, H200
AMD: MI300X, MI325X, MI355X

Supported Frameworks

vLLM, SGLang, TensorRT-LLM
Dynamo, Dynamo-Serve, Dynamo-Disaggregated
Atom, Mori-SGLang

Quick Start

Run Report Generation

python3 scripts/inferencex_api_report.py

API Endpoint

# Get latest benchmark data
curl -s "https://inferencex.semianalysis.com/api/v1/benchmarks?model=DeepSeek-R1-0528" | gunzip

# Get specific date
curl -s "https://inferencex.semianalysis.com/api/v1/benchmarks?model=DeepSeek-R1-0528&date=2026-06-02&exact=true" | gunzip

Report Contents

1. Data Update Summary

New data availability check
New hardware-model-framework-precision combinations
Performance changes (>5% threshold)

2. Data Overview

Total combinations count
Hardware/Model/Framework coverage
Best throughput records
NVIDIA vs AMD performance comparison

3. 8k1k Performance Matrix ⭐

Sequence Length: ISL=8192, OSL=1024 (RAG typical scenario)
Filter: interactivity > 20 tps
Format: Hardware (rows) × Model (columns)
Color Coding:
- 🟢 Green: > 10k tok/s/GPU (High performance)
- 🟠 Orange: > 5k tok/s/GPU (Medium performance)
- ⚪ White: Normal performance
- ➖ Gray "-": Not supported or no data

Sequence Length Combinations

Combo	ISL	OSL	Scenario	Performance
1k1k	1024	1024	Standard chat	Medium
8k1k	8192	1024	RAG/Search (long input, short output)	Best
1k8k	1024	8192	Code gen (short input, long output)	Lower

Why 8k1k is fastest?

Prefill phase can parallelize 8192 tokens, fully utilizing GPU compute
Decode phase only generates 1024 tokens, reducing autoregression overhead

Configuration

Email Settings

Edit scripts/inferencex_api_report.py:

SMTP_SERVER = "smtp.163.com"
SMTP_PORT = 465
SENDER_EMAIL = "your-email@163.com"
SENDER_PASSWORD = "your-auth-code"  # Not login password!
RECIPIENT_EMAIL = "recipient@example.com"

Cron Job (Optional)

# Add to crontab for daily 9:00 AM execution
0 9 * * * cd /path/to/skill && python3 scripts/inferencex_api_report.py

Output Files

data/inferencex/
├── inferencex_summary_YYYY-MM-DD.csv    # Full performance data
├── inferencex_summary_YYYY-MM-DD.json   # Raw data for comparison
└── email_YYYY-MM-DD.html                # Email content backup

Data Source

API: https://inferencex.semianalysis.com/api/v1/benchmarks
Update Frequency: Real-time
Format: JSON (gzip compressed for some models)

Requirements

Python 3.8+
requests
Standard library only (no external dependencies)

License

MIT License - See repository for details.

Author

Created for Ascend AI Coding community.

---name: inferencex-reportdescription: Automatically fetch InferenceX benchmark data and generate daily performance reports for LLM inference on various hardware (NVIDIA, AMD, etc.). Supports email delivery, data change detection, and 8k1k sequence length performance analysis. Use when needing to track LLM inference performance trends, compare hardware configurations, or monitor benchmark updates.---# InferenceX Report - LLM Inference Benchmark TrackerAutomatically fetch InferenceX benchmark data and generate daily performance reports for LLM inference on various hardware platforms.## OverviewThis skill fetches real-time benchmark data from InferenceX API, generates comprehensive performance reports, and sends email notifications with:- Daily benchmark data summary- Hardware-Model-Framework performance matrix- 8k1k sequence length detailed analysis- Data change detection (new combinations, performance changes)## Supported Models| Model | Records (2026-06-02) ||-------|---------------------|| DeepSeek-R1-0528 | 1,839 || gpt-oss-120b | 617 || Llama-3.3-70B-Instruct-FP8 | 681 || Qwen-3.5-397B-A17B | 650 || Kimi-K2.5 | 268 || MiniMax-M2.5 | 840 || GLM-5 | 367 || DeepSeek-V4-Pro | 607 || **Total** | **5,869** |## Supported Hardware- NVIDIA: B200, B300, GB200, GB300, H100, H200- AMD: MI300X, MI325X, MI355X## Supported Frameworks- vLLM, SGLang, TensorRT-LLM- Dynamo, Dynamo-Serve, Dynamo-Disaggregated- Atom, Mori-SGLang## Quick Start### Run Report Generation```bashpython3 scripts/inferencex_api_report.py```### API Endpoint```bash# Get latest benchmark datacurl -s "https://inferencex.semianalysis.com/api/v1/benchmarks?model=DeepSeek-R1-0528" | gunzip# Get specific datecurl -s "https://inferencex.semianalysis.com/api/v1/benchmarks?model=DeepSeek-R1-0528&date=2026-06-02&exact=true" | gunzip```## Report Contents### 1. Data Update Summary- New data availability check- New hardware-model-framework-precision combinations- Performance changes (>5% threshold)### 2. Data Overview- Total combinations count- Hardware/Model/Framework coverage- Best throughput records- NVIDIA vs AMD performance comparison### 3. 8k1k Performance Matrix ⭐- **Sequence Length**: ISL=8192, OSL=1024 (RAG typical scenario)- **Filter**: interactivity > 20 tps- **Format**: Hardware (rows) × Model (columns)- **Color Coding**: - 🟢 Green: > 10k tok/s/GPU (High performance) - 🟠 Orange: > 5k tok/s/GPU (Medium performance) - ⚪ White: Normal performance - ➖ Gray "-": Not supported or no data## Sequence Length Combinations| Combo | ISL | OSL | Scenario | Performance ||-------|-----|-----|----------|-------------|| 1k1k | 1024 | 1024 | Standard chat | Medium || 8k1k | 8192 | 1024 | RAG/Search (long input, short output) | **Best** || 1k8k | 1024 | 8192 | Code gen (short input, long output) | Lower |**Why 8k1k is fastest?**- Prefill phase can parallelize 8192 tokens, fully utilizing GPU compute- Decode phase only generates 1024 tokens, reducing autoregression overhead## Configuration### Email SettingsEdit `scripts/inferencex_api_report.py`:```pythonSMTP_SERVER = "smtp.163.com"SMTP_PORT = 465SENDER_EMAIL = "your-email@163.com"SENDER_PASSWORD = "your-auth-code" # Not login password!RECIPIENT_EMAIL = "recipient@example.com"```### Cron Job (Optional)```bash# Add to crontab for daily 9:00 AM execution0 9 * * * cd /path/to/skill && python3 scripts/inferencex_api_report.py```## Output Files```data/inferencex/├── inferencex_summary_YYYY-MM-DD.csv # Full performance data├── inferencex_summary_YYYY-MM-DD.json # Raw data for comparison└── email_YYYY-MM-DD.html # Email content backup```## Data Source- **API**: `https://inferencex.semianalysis.com/api/v1/benchmarks`- **Update Frequency**: Real-time- **Format**: JSON (gzip compressed for some models)## Requirements- Python 3.8+- requests- Standard library only (no external dependencies)## LicenseMIT License - See repository for details.## AuthorCreated for Ascend AI Coding community.

Frequently asked questions