accelerator string | architecture string | memory_capacity_gb int64 | memory_type string | memory_bandwidth_tb_s float64 | tdp_watts int64 | cooling string | interconnect string | fp8_dense_tflops int64 | fp16_dense_tflops int64 | fp4_dense_tflops float64 | blended_cloud_hourly_rate_usd float64 | reserved_3yr_hourly_usd float64 | idle_power_watts int64 | peak_inference_power_watts int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
NVIDIA H100 SXM5 | Hopper (GH100) | 80 | HBM3 | 3.35 | 700 | Air / Hybrid Liquid | NVLink-4 (900 GB/s) | 1,979 | 989 | null | 2.65 | 1.48 | 215 | 685 |
NVIDIA H200 SXM5 | Hopper Refresh (GH100) | 141 | HBM3e | 4.8 | 700 | Direct Liquid / Air | NVLink-4 (900 GB/s) | 1,979 | 989 | null | 3.2 | 1.85 | 220 | 690 |
NVIDIA B200 NVL | Blackwell (GB200/B200) | 192 | HBM3e | 8 | 1,000 | Direct-to-Chip Liquid Cooling | NVLink-5 (1800 GB/s) | 4,500 | 2,250 | 9,000 | 4.6 | 2.75 | 290 | 980 |
Google TPU v5p | TPU v5p Pod | 95 | HBM2e | 4.8 | 650 | Liquid-to-Air Exchanger | ICI 4800 Gbps 3D Torus | 918 | 459 | null | 2.1 | 1.25 | 180 | 610 |
Google TPU v6e Trillium | Trillium Tensor Core | 32 | HBM | 1.64 | 310 | Air Cooled | ICI 3200 Gbps | 460 | 230 | null | 0.85 | 0.52 | 95 | 295 |
AWS Trainium2 (Trn2) | NeuronCore-v3 | 96 | HBM | 4.1 | 600 | Liquid Cooling | NeuronLink-v2 (832 GB/s) | 840 | 420 | null | 1.65 | 0.98 | 170 | 570 |
NVIDIA L40S | Ada Lovelace (AD102) | 48 | GDDR6 (ECC) | 0.864 | 350 | Passive Air | PCIe Gen 5 x16 (64 GB/s) | 733 | 366 | null | 1.15 | 0.68 | 110 | 340 |
Cerebras CS-3 | Wafer-Scale Engine 3 (WSE-3) | 44 | On-Die Ultra-SRAM | 21,000 | 23,000 | Closed-Loop Liquid Chiller | Switched Wafer Mesh (450 Tbps) | 125,000 | 62,500 | null | 48 | 29.5 | 5,800 | 22,400 |
🚀 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.
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/
📊 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
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.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}
}
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