RAID 0 Calculator

ByAlex Mercer, MSCS ✓ Verified 📚 201 Calculators Authored

Principal Systems Architect & Network Performance Engineer · M.S. in Computer Science, Certified Kubernetes Administrator (CKA)

📅 Published: Sep 28, 2026

The formula

usable = N × disk size; throughput ≈ N × per-disk throughput; P(a disk fails in a year) = 1 − (1 − AFR)^N

RAID 0 is a performance tool, not protection

Striping splits each block across every disk, so a four-disk array delivers roughly four times the sequential throughput and 100% of the raw capacity. The trade is brutal: the array fails if any member fails, and when it does there is no parity or mirror to rebuild from — the data is gone. With a 0.6% annual failure rate per disk, a twelve-disk stripe has about a 7% chance of total loss in a single year, rising to roughly 30% over five years. RAID 0 belongs on scratch disks, edit caches, temporary render volumes and swap — anywhere the data is reproducible from a source you still control.

When striping still makes sense

Modern NVMe drives are fast enough that a single disk saturates many workloads, so RAID 0 purely for speed is less compelling than it was with spinning media. It remains genuinely useful where one device cannot deliver the required queue depth: multi-camera high-bitrate video editing, large sequential scientific datasets, and database volumes where IOPS aggregate linearly. In each case the correct pattern is a stripe over disks holding only derived or reloadable data, with the irreplaceable source stored elsewhere.

Frequently asked questions

Is RAID a backup?

No. RAID protects against disk failure, not against deletion, ransomware, controller faults, fire, or a bad firmware flash propagating to every disk at once. The "R" is redundancy, not recovery — you still need independent, off-site copies.

Does adding more disks always make RAID 0 faster?

Up to a point. Throughput scales until you hit controller bandwidth, available PCIe lanes or the host bus limit — after that, extra disks add capacity and failure risk without adding speed.

Can I recover data from a failed RAID 0?

Only partially and expensively. Because blocks are striped across all members, a single dead disk leaves gaps throughout the volume. Specialists can sometimes reconstruct contiguous files, but plan on the array being lost.

What should I use instead?

RAID 10 for a balance of speed and resilience, or RAID 6 where capacity efficiency matters more than write performance. Both survive disk loss; neither replaces a backup.

Source & methodology

Patterson, Gibson & Katz (1988), "A Case for Redundant Arrays of Inexpensive Disks (RAID)"; AFR conversion from MTBF using the standard 8,760-hour year.

RAID 0: the number, the method and the caveats

This page was written for one job: turning your own inputs into a defensible figure, with the working shown. RAID 0 is the kind of number people argue about from memory. This page ends that argument with arithmetic you can watch happen: enter your figures, get Usable capacity in real time, and see every substitution printed below.

In plain terms, RAID 0 answers the question "Usable capacity?". The measurement matters because it converts a situation that feels complicated — several inputs pulling in different directions — into a single number you can act on. Within Technology & Computing, Networks & Storage is the context where this figure is quoted most often, and professionals treat it as a first-pass screen rather than a verdict.

Like most useful measures, RAID 0 is a summary, and summaries hide as much as they reveal. The companion outputs and the input table are here to reopen what the headline compressed.

Nothing below is stock text pasted around a number: the worked example computes usable capacity = 16.00 TB from this tool’s own defaults, and the sensitivity section ranks the inputs that move the result — Number of disks first, at ≈10.0 % of the headline for a ten-percent error either way in that input. Work down the page and by the end you could re-derive the result in a spreadsheet from memory.

How RAID 0 is computed, line by line

Here is the complete model this tool runs — usable = N × disk size; throughput ≈ N × per-disk throughput; P(a disk fails in a year) = 1 − (1 − AFR)^N — with each input explained below. If a competitor’s page gives you a different answer from the same inputs, this is the text that settles which of you is right.

Number of disks (example value 4) is a plain measured quantity — the unit shown in the label is the unit the formula expects. Quantities scale everything downstream — a one-unit error here is multiplied across the whole result.

Capacity per disk (TB) (example value 4) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. This input feeds the formula directly; a defensible figure here is worth more than false precision anywhere else.

Throughput per disk (MB/s) (example value 250) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. If you are unsure what belongs in this field, look at the printed steps: the substitution shows exactly how the engine uses it.

Disk MTBF (hours) (example value 1,400,000) is a plain measured quantity — the unit shown in the label is the unit the formula expects. Use the value as of today, not as of the last time you looked — time fields drift silently.

Rebuild time for one disk (hours) (example value 1) is a measured number in the unit named by the label; the formula does not convert for you. Be precise about the boundary (exact date, start of month); time inputs decide the exponent in most formulas.

RAID 0 with real numbers, step by step

Abstract formulas are hard to trust, so here is the complete substitution using the default inputs, written the way an examiner would want it. Starting figures: Number of disks, Capacity per disk (TB), Throughput per disk (MB/s), Disk MTBF (hours). Run to the end, the usable capacity resolves to 16.00 TB — 16.00 TB raw · 100.0% efficiency on the bands this tool uses.

Usable capacity = N × disk size = 4 × 4.00 TB = 16.00 TB (efficiency 100.0%).
Approximate throughput = 4 × 250 MB/s = 1,000 MB/s, provided the controller and host bus can keep up.
Annual failure rate per disk = 8,760 h ÷ 1,400,000 h = 0.626%.
Probability at least one disk fails within a year = 1 − (1 − 0.00626)^4 = 0.63%. Under RAID 0 that event loses all the data, not part of it — there is no rebuild.

Raw capacity — 16.00 TB. This is the number experienced users quote when the headline alone would mislead.

Capacity efficiency — 100.0%. This is the number experienced users quote when the headline alone would mislead.

Fault tolerance — None — any single disk failure destroys the entire array. Read next to the headline, this is the figure that shows the shape of the trade-off.

Sequential throughput (approx.) — 1,000 MB/s. Compare it against the main result of 16.00 TB before drawing conclusions.

Annual failure rate per disk — 0.63%. Compare it against the main result of 16.00 TB before drawing conclusions.

Keep this worked example open while you enter your own figures: matching the shape of the substitution is the fastest way to spot a unit or convention mistake.

Scenario table: RAID 0 at ±10 % and ±20 % of the key input

The honest version of any estimate is a corridor, and this one is cheap to produce: RAID 0 was recomputed at 5 points along the dominant input, with every other field left untouched.

Number of disks setting	Usable capacity
80 % of the example value	12.80 TB
90 % of the example value	14.40 TB
example inputs	16.00 TB
110 % of the example value	17.60 TB
120 % of the example value	19.20 TB

That spread — 12.80 TB at the low end, 19.20 TB at the high — is what a careful read of RAID 0 looks like: a band you can state out loud, with the reason for each edge. Reproduce it in the form above by editing Number of disks alone; the substitution steps will follow your figures exactly.

Sensitivity: which inputs change RAID 0 the most

This list was computed for this specific engine just now: each numeric input was nudged ±10 % while every other field held its example value, and the usable capacity was recalculated from scratch. It is the fastest honest answer to “what matters most in RAID 0?” — and it is specific to these defaults, because sensitivity moves with the starting point.

Nudge Number of disks by ±10 % and RAID 0 lands between 14.40 TB and 17.60 TB (sensitivity ≈10.0 %)
Capacity per disk: ten percent up gives 17.60 TB, ten percent down gives 14.40 TB — ≈10.0 % of the usable capacity either way

Number of disks is the dominant lever here: ten percent either way moves the result by about 10.0 %, from 14.40 TB to 17.60 TB. That is where a careless figure does the most damage, so check it against a source, and Capacity per disk is effectively tied with it (≈10.0 % for the same nudge), so verify the pair together. The rest of the inputs earn only a rounding-error of attention, and that asymmetry is the practical payoff of measuring rather than asserting.

Is your RAID 0 good — reading the result bands

Nobody acts on a bare figure — they act on a figure with a comparison attached. The default case lands in the “16.00 TB raw · 100.0% efficiency” band, and bands are deliberately wide because real situations overlap. How much of that band is luck? On Number of disks alone ten percent either way covers 14.40 TB to 17.60 TB. The practical reading rules: ask what would have to change to move you one band. If the answer is “a small, plausible input difference”, the band itself is not the finding — the uncertainty is.

The comparison worth making is not with somebody else’s RAID 0 but with the version of your own situation measured last quarter — same formula, same conventions, different inputs.

How people get RAID 0 wrong

Reusing an RAID 0 from last season. In Technology & Computing the inputs are the thing that drifts; the formula does not.

Attention is also misallocated between fields. Having measured this engine, we can say where it belongs: Number of disks first, everything else second.

Another is comparing across contexts: an RAID 0 computed with one set of conventions should never be lined up against a figure computed with another. Check the inputs behind both before ranking them.

RAID 0 verification checklist

Re-run the calculation with a ±10 % stress on the two most sensitive inputs.
Check that every field the formula needs actually arrived from a document, not from recollection.
Put Number of disks under a magnifier; the sensitivity table above is the reason why.
Note the date and the inputs beside the number so the figure can be audited later.
Compare the output against one published benchmark or a colleague’s independent estimate.
Run the ±10 % stress on the top two levers and check that your conclusion survives both.

Step by step: getting a trustworthy RAID 0 out of this tool

Using a calculator well sounds trivial, but the difference between a decision-grade figure and a throwaway one lives entirely in the routine. The routine for RAID 0 takes a minute to learn and applies to every input above.

Step 1 — one document, one pass. Open the statement, label or record that holds number of disks, capacity per disk (tb), throughput per disk (mb/s), disk mtbf (hours), rebuild time for one disk (hours) and transcribe straight into the form. Transcription from a single source removes the mismatch errors that appear when figures are assembled from three different places. The fastest sanity test is to guess the answer first and see how far the calculation disagrees.

Step 2 — enter the conservative case first. Resist the flattering scenario. Type the inputs you would defend in front of a sceptical reader, read the Usable capacity (16.00 TB for the example values), and note the verdict band. Comparisons are cheap: run the identical calculation for the alternative and look at the gap.

Step 3 — isolate your variables. Change one field, observe, revert. The live sensitivity list does the ranking for the default inputs; verify it against your own, because dominance shifts when the starting point shifts. Repeating a calculation with the same numbers proves only that the arithmetic is reliable — which is worth knowing.

Step 4 — stress before you commit. A decision that survives a ten-percent squeeze on its two biggest inputs needs no luck to be sound. One that does not survive is a decision about hope, not about arithmetic. Numbers are cheap to produce and expensive to retract; the extra minute is the cheapest insurance available.

Step 5 — record the context, not just the number. Note today’s date and the inputs alongside the 16.00 TB output. Six months from now the context is what turns this figure into a trend. Suspiciously tidy answers deserve the same scrutiny as untidy ones.

From calculation to decision: where RAID 0 earns its keep

In Networks & Storage the figure computed here is usually the first one somebody asks to see. Having it — with the formula behind it — changes your position in the conversation from participant to reference. Compare the calculation to the rough guess you arrived with, and ask why they differ.

The companion outputs — Raw capacity and Capacity efficiency — exist for the second stage of that screen: the headline sizes the prize, while the companions describe the trade-off you are accepting to get it. If the answer changes your behaviour, it deserves a second run with different assumptions.

Re-running this page costs nothing and catches the slow drifts that never announce themselves. In Technology & Computing the expensive mistakes are rarely sudden; they are small changes in Number of disks that nobody recalculated for a year. Half of a good calculation is knowing which input you would hate to be wrong about.

The questions that come up most about RAID 0

What affects RAID 0 the most?

The short answer: Number of disks. The longer answer is the sensitivity table on this page, where every numeric input was probed by ten percent and the usable capacity recalculated — Number of disks moved it furthest, by roughly 10.0 % (14.40 TB to 17.60 TB).

Do capacity per disk and number of disks matter equally?

Effectively. Measured on this engine they land at ≈10.0 % and ≈10.0 % for the same ten-percent nudge, so the honest advice is to get both right; chasing precision in only one of them buys almost nothing.

What is a typical RAID 0?

On the worked example above RAID 0 comes out at 16.00 TB. Treat that as one point on a distribution rather than a norm: the bands, the companion outputs and the sensitivity ranking tell you how wide the plausible range really is.

How do I calculate RAID 0 by hand?

Use the stated model — usable = N × disk size; throughput ≈ N × per-disk throughput; P(a disk fails in a year) = 1 − (1 − AFR)^N — and substitute in the order the tool prints (it starts from Usable capacity = N × disk size = 4 × 4.00 TB = 16.00 TB (efficiency 100.0%).). On the example inputs the hand route lands on 16.00 TB, exactly what the calculator shows. A spreadsheet reproduces it in two or three cells; doing it once by hand is what makes the automated answer trustworthy.

What happens to RAID 0 if number of disks changes by 10 %?

Try it in the form above and the printed substitution will follow you; the pre-computed answer for the defaults is 14.40 TB to 17.60 TB across the ten-percent probe, i.e. ≈10.0 % sensitivity on the usable capacity.

Is the RAID 0 result of 16.00 TB good or bad?

Neither, on its own — the label “16.00 TB raw · 100.0% efficiency” is the band this figure falls in for the example inputs, and your own inputs will place you elsewhere on the same ladder. Judge the number against your history and your alternatives, and remember that a ten-percent error in Number of disks alone moves it by ≈10.0 %.

How do I calculate RAID 0 manually?

Write down the inputs, then follow the printed steps in order — usable = N × disk size; throughput ≈ N × per-disk throughput; P(a disk fails in a year) = 1 − (1 − AFR)^N. A spreadsheet replicates it with two or three cells. The manual route is worth doing once to build intuition; after that the calculator saves time and eliminates transcription slips.

Why is my measured value different from RAID 0?

Real hardware and networks add contention, throttling, and overheads that first-principles RAID 0 assumes away. Expect the measured number to sit below 16.00 TB; if it sits far below, that gap itself is the diagnostic.

Is RAID a backup?

Does adding more disks always make RAID 0 faster?

Up to a point. Throughput scales until you hit controller bandwidth, available PCIe lanes or the host bus limit — after that, extra disks add capacity and failure risk without adding speed.

Is my data private on this page?

Yes — computation happens locally in your browser, with no account, no tracking of your inputs and nothing sent to a server. The printed formula is the whole story of what happens to your figures.

Key terms on this page

Raw capacity: 16.00 TB on the example inputs — a companion figure printed beside the headline of RAID 0 so that no result is read in isolation.
Capacity efficiency: 100.0% for the RAID 0 worked case; it sits next to the main result because it carries context the headline leaves out.
Fault tolerance: None — any single disk failure destroys the entire array in the default RAID 0 calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
Usable capacity: The primary output of this tool: 16.00 TB for the default inputs, computed exactly from usable = N × disk size; throughput ≈ N × per-disk throughput; P(a disk fails in a year) = 1 − (1 − AFR)^N.
Worked example: Every substitution in order, from the example inputs to 16.00 TB — the audit trail for the headline figure.
Verdict band: The plain-language label — here “16.00 TB raw · 100.0% efficiency” — mapping the computed figure onto experience-based ranges for Technology & Computing.
Sensitivity (±10 %): How far the RAID 0 moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.

Method, sources and what this tool cannot tell you

There is no black box on this page — the engine evaluates usable = N × disk size; throughput ≈ N × per-disk throughput; P(a disk fails in a year) = 1 − (1 − AFR)^N. Source of record: Patterson, Gibson & Katz (1988), "A Case for Redundant Arrays of Inexpensive Disks (RAID)"; AFR conversion from MTBF using the standard 8,760-hour year. Any other implementation of this model should return the same figure for the same inputs. If one does not, the difference is in its assumptions — which this page prints and that one probably does not.

Treat the output as a precise answer to the question you typed: exact arithmetic, transparent formula, published source, no hidden rounding. The promise to the reader is narrow and testable: same inputs, same output, no silent rounding, and no figure presented without the formula that produced it.

Turning this RAID 0 into a decision

Save the number together with its inputs and today’s date; an RAID 0 figure without context is unusable the moment an input changes.

This page was written for people who need one trustworthy number before their next decision, and the promise is that nothing above required trust. Bookmark the tool, re-run it whenever an input moves by more than a few percent, and keep the date with the number.