Skip to content

Hyperscale Data, Inc.: total assets

Hyperscale Data total assets: $313.7 million at 2025-12-31, up 42.1% on the year. 16 values since 2010, each linked to its SEC filing. In original units.

All Hyperscale Data, Inc. financial histories

At a glance

Hyperscale Data reported total assets of $313.7 million at 2025-12-31, up 42.1% from $220.8 million a year earlier. From 2010 to 2025 it grew at a compound 28.6% a year, from $7.18 million to $313.7 million. Across the last five reported years it ranged from $220.8 million (2024-12-31) to $561.5 million (2022-12-31).

Computed from the values in the table below, in USD; each links to the filing it came from.

What this measure means

Resources recognized on the balance sheet. Book assets are not the market value of the business.

Exact concept: us-gaap:Assets. Each value is a balance at the reporting date, not a flow earned over a year. Different units remain separate; no currency conversion or interpolation is applied.

Coverage of this history

Selected reporting periods run from 2010-12-31 to 2025-12-31. The SEC response was captured on 2026-09-20.

Selected filing history

Total assets in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
-2025-12-31313,707,000USD2026-04-1510-K · 0001214659-26-004697 SEC
-2024-12-31220,766,000USD2026-04-1510-K · 0001214659-26-004697 SEC
-2023-12-31299,190,000USD2025-04-1510-K · 0001214659-25-005868 SEC
-2022-12-31561,514,000USD2024-09-2410-K/A · 0001214659-24-016683 SEC
-2021-12-31490,286,000USD2023-05-2210-K/A · 0001214659-23-007664 SEC
-2020-12-3175,643,000USD2023-04-1410-K/A · 0001214659-23-005422 SEC
-2019-12-3142,750,408USD2021-04-1510-K · 0001214659-21-004221 SEC
-2018-12-3149,425,880USD2020-06-0110-K/A · 0001214659-20-005278 SEC
-2017-12-3130,508,894USD2019-10-1610-K/A · 0001214659-19-006507 SEC
-2016-12-315,472,000USD2018-04-1810-K · 0001214659-18-002877 SEC
-2015-12-315,099,000USD2017-04-1010-K · 0001437749-17-006372 SEC
-2014-12-316,342,000USD2016-03-3110-K/A · 0001437749-16-028793 SEC
-2013-12-316,977,000USD2015-03-2710-K · 0001437749-15-006253 SEC
-2012-12-316,932,000USD2014-03-2810-K · 0001437749-14-005312 SEC
-2011-12-317,349,000USD2013-03-2910-K · 0001437749-13-003708 SEC
-2010-12-317,179,000USD2012-10-0910-K/A · 0001437749-12-010148 SEC

Related HYPERSCALE DATA, INC. histories

All 64 Hyperscale Data histories

Inspect the source

Entity
HYPERSCALE DATA, INC. / CIK 0000896493
Captured
SEC response SHA-256
35e4b4d2a315ac2783cd2a79c5d2b3714f16fb7c52567963f7aa296ebe40328e

Current SEC company facts · Download the original response snapshot (gzip) · Download the selected JSON

Latest-filed annual-report facts per unit and reporting period at capture time. Duration facts cover 300 to 400 days. This selection can include restatements and is not a point-in-time backtest dataset. Missing concepts are omitted, never zero-filled. Values retain original units and are not currency converted. Extended concepts require compatible unit shapes and at least three reporting ends with changing values within one unit. Constant or incompatible added histories are omitted.

Public company accounting reference, not market prices, returns, an investment recommendation, or ALPHAC performance. Validate a separately constructed return series with the validation API; accounting values are not returns.

Use this in research

A financial period ends before its results become public. Use the filing date as a minimum availability boundary, inspect amendments, and retain the original filing vintage when testing historical signals. This latest-filed selection can contain information unavailable at the time.

These pages do not supply prices, total-return histories, corporate-action adjustments or a tradable universe. Build those inputs separately before evaluating a strategy. A profitable backtest can still reflect selection bias or costs that were left out.

Research methodology · Execution and cost assumptions · Check backtest overfitting

Build with the open-source tools

Use these accounting records as inspectable inputs. When you have constructed a return series, the validation tools can help test its statistical evidence and preserve the result with its limitations.

The MCP server is free and MIT-licensed: npx -y canli-mcp. If these records help your research, a star on GitHub helps other researchers find them.

Read the published dataset with Python
import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0000896493.json") as response:
    record = json.load(response)
print(record["fetched_at"])
print(record["policy"])
for concept in record["concepts"]:
    print(concept["tag"], next(iter(concept["observations"])))