devidasmishra/ai-inference-hardware-economics-2026

🚀 2026 AI Inference & Hardware Economics Telemetry Index

  

This repository hosts the official open-access empirical telemetry dataset for 2026 AI Inference, Silicon Architecture, and Hardware Economics, curated by [EyesTech Systems & FinOps Intelligence](https://eyestech.in/ai-inference-hardware-economics-statistics-tco-2026/).

Original Research Investigation: For the complete whitepaper, interactive latency calculators, and per-token TCO models, see the flagship publication at: 👉 [https://eyestech.in/ai-inference-hardware-economics-statistics-tco-2026/](https://eyestech.in/ai-inference-hardware-economics-statistics-tco-2026/)

📊 Dataset Summary

• —Telemetry Sample Size: 58 production cluster configurations across 8 hyperscaler & private datacenter regions (US-East, US-West, EU-Central, APAC-South).
• —Serving Frameworks Audited:
• —vLLM v0.9.2 (FlashAttention-3 + PagedAttention)
• —TensorRT-LLM v1.2.0 (FP8 / FP4 GEMM)
• —Triton Inference Server 26.08
• —Google MaxText TPU Runtime
• —AWS NeuronCore SDK 2.21
• —Audited Accelerators: NVIDIA H100 SXM5, NVIDIA B200 NVL, Google TPU v5p, AWS Trainium2, Cerebras CS-3, NVIDIA L40S, AMD Instinct MI300X.

💾 Files Included

1. 1.ai-inference-statistics-2026.json: Complete hierarchical telemetry dataset (25 KB) including model-specific token economics, TTFT, TPOT, tokens/joule, and GPU cluster MTBF failure logs.
2. 2.hardware_accelerators.csv: Normalized tabular matrix for instant viewing and pandas/Polars ingestion.

🐍 Quick Start with Python & Pandas

import pandas as pd
import json

# Ingest tabular hardware economics
df = pd.read_csv("https://huggingface.co/datasets/devidasmishra/ai-inference-hardware-economics-2026/raw/main/hardware_accelerators.csv")
print(df[["accelerator", "memory_bandwidth_tb_s", "blended_cloud_hourly_rate_usd", "fp8_dense_tflops"]])

# Ingest full hierarchical JSON
import requests
telemetry = requests.get("https://huggingface.co/datasets/devidasmishra/ai-inference-hardware-economics-2026/raw/main/ai-inference-statistics-2026.json").json()
print("Dataset Title:", telemetry["metadata"]["title"])

📚 Citation & Academic Reference

If you cite or utilize these benchmarks in academic papers, technical whitepapers, or enterprise TCO reports, please cite:

@dataset{eyestech_inference_economics_2026,
  author       = {Vance, Marcus and Sethi, Arjun and Mishra, Abhishek Raaj},
  title        = {AI Inference & Hardware Economics: 2026 Statistics, Silicon Telemetry & TCO Index},
  year         = {2026},
  publisher    = {EyesTech Systems Lab},
  howpublished = {\url{https://eyestech.in/ai-inference-hardware-economics-statistics-tco-2026/}},
  note         = {Hugging Face Dataset: devidasmishra/ai-inference-hardware-economics-2026}
}