MinerU

5 posts with the tag “MinerU”

How to self-host the MinerU API on RunPod

Jun 8, 2026

Sergei Shmakov

Last Updated: 2026-06-08

If you’re calling the official MinerU API (mineru.net/api/v4) in production, you’ve probably hit one of three walls: the daily quota of 1,000 high-priority pages, the per-file 200 MB cap, or a compliance review asking why your documents leave your infrastructure for a third-party cloud. You can run the exact same MinerU engine yourself on RunPod Serverless, keep documents in your own bucket, drop the quota, and pay roughly $0.001 per page warm. The migration is close to drop-in: swap the client, keep your create-task / poll / download loop.

The cloud API is the right way to try MinerU. Once you’re parsing real volume, self-hosting changes the economics and the data path. Here’s why, what it costs, and how to move your code over.

Why self-host the MinerU API instead of using mineru.net?

Three reasons: cost at volume, data residency, and control. The cloud API meters a daily high-priority page quota and then deprioritizes you; a self-hosted endpoint has no quota. Self-hosted, your PDFs and their parsed output never leave your RunPod worker and your own S3 bucket. And you pin the model version, pick the GPU, and set your own concurrency.

The official MinerU API caps each file at 200 MB and gives each account a daily quota of 1,000 high-priority pages (see the published limits) before jobs drop to lower priority. That ceiling is fine for evaluation and light use. It becomes a planning problem the moment you’re ingesting thousands of pages a day on a deadline.

The data path matters too. On the SaaS, every document round-trips through mineru.net. Self-hosted, the worker pulls the file from a URL you control (or you send the bytes inline), parses on your GPU, and writes the result to your bucket. Nothing transits a vendor you have to put in a data-processing agreement.

The trade-off is honest: you run the infrastructure. There’s a cold-start cost, but FlashBoot blunts it: once a host has booted the worker, it restores from a process snapshot in ~7-8 s across scale-to-zero cycles, and only a brand-new host pays the full ~110 s (vLLM init plus loading the model into VRAM). The model is baked into the image, so nothing is downloaded at request time. If your traffic is a handful of pages a month, the cloud API’s free tier is simpler; cross into steady volume and the math flips.

What does self-hosting MinerU cost vs the cloud API?

Roughly $0.001 per page warm on a 24 GB RTX 4090, plus a ~$0.03 cold-start tax the first time a host boots the worker (≈110 s of GPU billing for vLLM init and loading the baked model into VRAM). FlashBoot then snapshots that state, so the same host restarts in ~7-8 s across scale-to-zero cycles. RunPod bills per second and scales to zero, so an idle endpoint costs nothing. The cloud API is free under its daily quota, then queues or meters you; self-hosting trades that ceiling for a steady, predictable per-page rate.

The real number depends on how well you amortize that first-boot tax. A thousand pages parsed across one warm window land near $0.001/page; a single short doc on a cold host lands closer to $0.005–$0.01 because you’re paying the one-time tax against very few pages. FlashBoot shrinks that in practice, since a host pays the full tax only once. I broke the full workload-shape math down in Serverless MinerU on RunPod: honest cost math.

One cost the marketing pages skip: the first-boot cold start (~110 s on a brand-new host) is billed GPU time. FlashBoot snapshots that boot, so the same host restarts in ~7-8 s afterward. The model is baked into the image, so there’s no model-download step at boot or request time. Budget for the first boot per host, not for every request.

How do you deploy your own MinerU endpoint on RunPod?

Deploy the open-source mineru-runpod template from the RunPod Hub, or fork it and point RunPod’s GitHub build at your fork. Create a Serverless Endpoint on a 24 GB GPU (ADA_24 / RTX 4090) with FlashBoot enabled. For full_zip_url parity with the cloud API, also set four BUCKET_* env vars pointing at an S3-compatible bucket.

The fastest path is the Hub listing: one click, fill in the deploy-time form, done. The full walkthrough (fork-and-build and bring-your-own-image included) is in the deploy guide.

The one piece worth getting right up front is object storage. The compat client returns results as a full_zip_url, which means the worker uploads the output archive to a bucket and hands back a presigned URL, exactly like the SaaS. That path needs four env vars on the endpoint:

BUCKET_ENDPOINT_URL=https://<account>.r2.cloudflarestorage.com
BUCKET_NAME=mineru-outputs
BUCKET_ACCESS_KEY_ID=<key>
BUCKET_SECRET_ACCESS_KEY=<secret>

Cloudflare R2 is a good pairing here because its egress is free, so downloading your own results costs nothing. Any S3-compatible store works (R2, Backblaze B2, MinIO, AWS S3). To get started you’ll need a RunPod account; sign up here (full disclosure: that’s a referral link). Add a few dollars of credit and it covers thousands of cold starts plus millions of warm pages.

How do you migrate your code off the MinerU API?

Install the mineru-client package and swap the official requests calls for MineruApiClient. It mirrors the cloud API’s create_task / get_task surface and returns the same response dicts, so your existing poll loop keeps working. Under the hood it requests archive_format="zip", so full_zip_url comes back as a real .zip like the SaaS does.

Install it (no version pin, so it tracks the repo):

pip install "mineru-client @ git+https://github.com/sergeyshmakov/mineru-runpod"

Here’s the before and after. The official API, polling by hand:

import requests, time

H = {"Authorization": f"Bearer {MINERU_TOKEN}"}
task_id = requests.post(
    "https://mineru.net/api/v4/extract/task",
    headers=H, json={"url": pdf_url, "model_version": "vlm"},
).json()["data"]["task_id"]

while True:
    data = requests.get(
        f"https://mineru.net/api/v4/extract/task/{task_id}", headers=H
    ).json()["data"]
    if data["state"] in ("done", "failed"):
        break
    time.sleep(2)
zip_url = data["full_zip_url"]   # then download + unzip yourself

Self-hosted, against your own endpoint:

from mineru_client import MineruApiClient

client = MineruApiClient(endpoint_id="<your-endpoint-id>", api_key="<runpod-key>")

task_id = client.create_task(pdf_url, model_version="vlm")["data"]["task_id"]
done = client.wait_for_task(task_id)        # polls to a terminal state
client.download_results(done, "./out")      # full_zip_url is a real .zip; unpacked for you

Same lifecycle, same {"code": 0, "data": {...}} response shape. The parameter names map across cleanly too: model_version to the worker’s backend, language to lang, enable_formula / enable_table straight through. The full field-by-field mapping is in Migrate from the MinerU API, and the Clients page covers the native client if you’d rather use the worker’s own (richer) request shape once you’ve moved.

One auth note: the self-hosted endpoint authenticates with your RunPod API key, also via Authorization: Bearer, so even that part of your code barely changes.

What doesn’t carry over from the cloud API?

Most of it carries; four things don’t. The compat client rejects callback (a RunPod webhook delivers a different, unsigned payload than MinerU’s signed {checksum, content} callback, so it raises instead of misleading you), and it doesn’t support extra_formats (docx/html/latex), the MinerU-HTML model, or multi-range page_ranges like "2,4-6". There’s no batch endpoint (RunPod’s queue already parallelizes individual jobs across workers, so one isn’t needed). And full_zip_url requires the BUCKET_* setup above.

The full list, so there are no surprises:

Cloud API feature	Self-hosted status
create_task / get_task, state machine, full_zip_url	Supported (with object storage configured)
model_version: pipeline / vlm	Supported (maps to the worker backends)
model_version: MinerU-HTML	Not supported, raises
extra_formats (docx / html / latex)	Not produced by this worker, raises
page_ranges multi-range ("2,4-6")	One contiguous range per job, raises otherwise
callback + seed	Rejected (poll with get_task / wait_for_task instead)
Batch (/extract/task/batch)	Not offered; submit tasks individually and raise workers_max — RunPod’s queue parallelizes them across workers

Where it falls down: if your pipeline leans on webhook callbacks or exports to DOCX/LaTeX, the compat client isn’t a clean swap today. There’s no batch endpoint either, but you rarely need one: submit tasks individually and let RunPod fan them across workers_max workers (the queue parallelizes them). For everything else, the create / poll / download path behaves like the SaaS.

One more practical detail. The compat client is URL-only, mirroring the cloud API’s POST /extract/task, which doesn’t accept file uploads. For small local files you don’t need to host anything: use the native MineruClient with file_b64 instead, which sends the bytes inline (fine under RunPod’s ~20 MB request cap). I parsed a 522 KB scanned Russian invoice that way and got clean Cyrillic Markdown back, no bucket round-trip involved.

FAQ

Is the MinerU API free?

The official MinerU cloud API has a free daily quota of 1,000 high-priority pages, after which jobs run at lower priority. There’s no per-call charge inside the quota. Self-hosting removes the quota entirely and replaces it with per-second GPU billing (around $0.001 per page warm).

What are the MinerU API’s limits?

Each file is capped at 200 MB, with a daily quota of 1,000 high-priority pages plus rate limits of 50 files/minute and 5,000 files/day (see the published limits). Self-hosting on RunPod removes the daily quota; the per-file practical limit becomes your GPU’s memory and your endpoint’s job timeout rather than a fixed page count.

Can I self-host MinerU without RunPod?

Yes. MinerU is Apache-2.0 open source and runs anywhere with a CUDA GPU. RunPod Serverless is the path this template targets because it scales to zero, so a bursty workload doesn’t pay for an idle GPU. On a fixed VPS or your own hardware you’d run MinerU directly and skip the serverless wrapper.

Does the self-hosted output match the cloud API’s full_zip_url?

Yes, when the endpoint has object storage configured. The compat client requests archive_format="zip", so the worker uploads a .zip to your bucket and returns a presigned full_zip_url, the same container and field the SaaS returns. download_results fetches and unpacks it for you, and autodetects .tar.gz too if you change the format.

Is self-hosting actually cheaper than the MinerU API?

At steady volume, usually yes, because you stop being throttled by the daily quota and pay only for GPU seconds used. The crossover depends on cold-start amortization: dense traffic lands near $0.001/page, sparse one-doc-per-cold-start traffic closer to $0.005–$0.01. Below a few hundred pages a month, the cloud API’s free tier is the cheaper and simpler option.

Do my documents stay private when self-hosting?

Yes. The worker fetches each file from a URL you control or from bytes you send inline, parses on your own RunPod GPU, and writes output to your own S3 bucket. No document or result passes through a third-party parsing service, which is the usual blocker in a data-residency or compliance review.

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Self-hosting MinerU isn’t about beating the cloud API on accuracy. The accuracy is identical: it’s the same model. It’s about removing the quota ceiling, keeping documents in your stack, and paying per-second instead of per-tier. If that’s the wall you’ve hit, fork the template, or deploy it from the RunPod Hub and grab a RunPod account to run the first parse.

Tags:

• MinerU
• RunPod
• Serverless
• PDF
• API

Structuring MinerU output into a clean doc tree

Jun 4, 2026

Sergei Shmakov

Last Updated: 2026-06-04

In part 1 I parsed the whole 5,039-page ECMA-376 Part 1 standard with MinerU on RunPod: 36 clause-aligned batches, about $1.15 of GPU time, out came 36 content_list.json files. That’s where most write-ups stop. Parsed is not the same as usable. A vision-language model hands you a flat stream of typed blocks with OCR quirks and no document structure. For a coding agent to answer “what does §17.9.4 isLgl (legal numbering) say?” it needs one small, faithful, addressable file, not a 200-page batch of blocks.

This post is the post-processing half: cleaning, structuring, cross-linking, and verifying the output. The whole thing is distilled into a small, document-agnostic toolkit you can run on your own MinerU output: examples/doc-structuring/ (start with the README).

End state: 5,039 pages became 9,948 Markdown files, every section addressable, ~7,900 cross-references turned into relative links, and every tag and attribute name verified against the official schema and the source PDF.

What does MinerU’s content_list.json actually give you?

A flat, page-ordered list of typed blocks: text (with an optional text_level for headings), list, table (HTML in table_body), code (in code_body, not text), equation, and image (with a VLM content description). Mixed in is page_number/header noise. No tree, no cross-reference graph, and a long tail of OCR artifacts.

That shape is fine for a quick read. It’s wrong for retrieval. Three jobs turn it into something an agent can navigate: rebuild the structure, render each block faithfully to Markdown, and verify the names didn’t get garbled on the way through the model. Everything below is generic; nothing in the toolkit knows about ECMA-376 specifically.

How do you rebuild the document structure?

The blocks arrive in reading order, so structure is just segmenting that stream by heading. A single forward walk does it: each block belongs to the section whose heading appeared most recently. Boundaries are only ever set by a real heading, so a section can never steal a neighbour’s content. You inject one callback, heading_id(block), where all your domain logic lives.

That callback is the only place document specifics enter: numbered headings, styled text_level lines, the occasional heading MinerU buried inside a code caption. The forward walk is the part that doesn’t break, because it has no notion of page or position to get wrong. The segmenter lives in segment.py.

Then the tree itself (tree.py):

• a section with sub-sections becomes a folder plus a barrel file (*-0-index.md) holding its intro prose and a child index;
• a leaf becomes one file. The golden rule: never split a leaf into parts.
• an agent walks root → barrel → barrel → leaf, reading one small index per level instead of one giant batch.

Naming encodes the section id plus a short slug (17-4-37-tbl-table.md), so any clause is glob-findable and the camelCase tag (instrText) survives for a regex lookup.

Why is the rendering the hard part?

Because faithful Markdown is where every MinerU artifact bites. The structuring is a clean algorithm; the rendering is a pile of special cases, each one traced to a real defect on this run. Skip any of them and the content silently corrupts: examples render empty, tables lose columns, prose gets promoted to a heading.

Every fix in render.py earned its place:

• Code lives in code_body (pre-fenced) and code_caption, not text. Miss this and all 3,591 XML examples render empty.
• [Example: … end example] markers have to bracket the code. They routinely land before or inside the fence.
• Page-split code halves (one example broken across a page) sit directly adjacent, so merge them. Genuinely separate examples have prose between them, so they’re left alone.
• Mislabelled fences (txt/asp/hcl that actually hold XML) get relabelled to xml, but only when the body has namespaced tags, so a text-output example stays text.
• Tables: HTML to Markdown. Inline XML examples wrapped as $<w:…>$ math get deleted by naive tag-stripping unless you protect them. Fully-empty illustration columns get dropped.
• Lists: no doubled bullets (- - foo), and ordered items keep their numbers.
• A long or sentence-ending text_level block is figure text or prose, not a heading. Don’t render it as ##.
• $§17.4.62$-wrapped references and \- escaped-dash bullets get normalized.

None of these are exotic. They’re just what VLM output looks like at scale, and each one quietly damages content if you skip it.

How do you cross-link a densely-referenced spec?

ECMA-376 cites itself constantly (§17.9.11, ST_Jc (§17.18.44), and on). Two moves in crosslink.py: normalize every reference to one canonical §N.N.N form so a single regex collects them all, then turn each resolvable one into a relative Markdown link to the target’s file or barrel.

The regex is §(\d+(?:\.\d+)*|[A-Z](?:\.\d+)+), which catches both numbered clauses and lettered annexes and gives you the citation graph for free. Links are relative on purpose: they’re computed section-to-section within the tree, so they stay valid wherever you mount it. No host path baked in, no rewrite on move. On this run, 7,877 of 7,959 references (98%) became working relative links.

How do you verify the parse is actually correct?

Two independent signals, both in verify.py. First, a vocabulary check: build the canonical set of element, attribute, and type names plus enum values straight from the official XSDs, then flag any name in the tree that isn’t in it but closely resembles one. Second, a source cross-check against the PDF text layer, which is the definitive one.

The vocabulary check (Vocabulary.from_xsd([...])) is fast and needs no PDF. It catches the obvious garbles: fontAlign when the schema only knows fontAlgn. But it misses the nasty case where a misread happens to spell a different real name.

That’s what the source cross-check is for. A name a file uses that is absent from that section’s own PDF page, while a near-miss correct name is present, is a confirmed garble. The PDF text layer is independent ground truth. This catches algn misread as align: align is a real element elsewhere, so it passes the vocabulary check, but on the actual page the source says algn. The check is bounded, each token is tested only against its section’s pages and deduped, so there’s no whole-document scan and no processing hole.

Confirmed garbles feed a vetted correction map (corrections.py), applied scoped to name contexts so a garble that collides with a real name is corrected only as an attribute, never as an element. Re-run the verifier and it reports zero. On this run that fixed a consistent align→algn / fontAlign→fontAlgn class across DrawingML, plus displacedByCustomXML, t12br→tl2br, subseted, and more, each confirmed against the PDF before it was applied.

Why replace OCR’d schema dumps instead of correcting them?

Because for the annexes you already have the real source, so OCR is the wrong input. The annexes are machine-generated schema listings (the full XSD and RELAX-NG for the formats). MinerU OCR’d them like everything else, producing the same garbles: CT_Placelder for CT_Placeholder, underscores read as spaces, a dangling fragment where a split cut mid-element. The real schema files exist, so swap them in.

The generic core is schema.py. Index every declaration in the official .xsd/.rnc, work out which schema file each annex dump came from (highest declaration-name overlap), and replace each parsed declaration with the authoritative one, matched by name then kind, exact → case-insensitive → fuzzy. The authoritative kind even drives the output folder and filename, so a mis-named OCR fragment self-corrects on rebuild. Result: 99.8% (5,699 of 5,710) declarations replaced. The ~11 too garbled to match confidently keep their OCR text.

How do you close the long tail of one-off OCR damage?

The residue is per-instance damage that won’t generalize into a correction map: a \@ date-switch read as $@$, a < read as #, a dropped ), glued attribute names. Past a point, stop writing heuristics. Let agents propose fixes and gate every one of them on the source PDF.

I ran an adversarial multi-agent fan-out. The worklist was ~100 items: every §17.16.5 field clause, enum truncations found by diffing the rendered table against the authoritative XSD, and the named defects. Each item carried its PDF ground truth. About 130 agents proposed fixes, and only PDF-verified ones were accepted: 41 patches, zero that the build couldn’t apply.

They live as a per-section overlay (apply_overlay) applied last, so each find matches the on-disk text and a stale one gets reported on the next rebuild rather than vanishing silently. After all of it, verify_against_pdf reports 0 actionable garbles. The 11 it still flags were each reviewed against the PDF as genuine, distinct OOXML names (useFirstPageNumber and firstPageNumber both exist; o:cname confirmed on p4968) and recorded as benign.

What does the finished tree look like?

Source	5,039 pages, 36 MinerU batches
Output	9,948 Markdown files (4,245 leaf clauses, 356 barrels, 5,130 split schema declarations, 1 root index)
Cross-references linked	7,877 / 7,959 (98%), relative
Verification	names vs official XSD vocab (2,058 elements + 1,806 attributes) and vs the source PDF text layer

The full worked wiring is example_pipeline.py. Note how little domain code it is: an outline loader, a heading detector, a naming scheme, a reference regex, and a correction map, all driven by CLI flags with nothing hard-coded. Everything else is the library.

Where does this fall down?

Honest limits, because the toolkit isn’t magic:

• The long tail is OCR, not logic, and verification closes it, not more regex. The systematic fixes get you most of the way, the authoritative-schema swap handles the annexes, and the per-instance residue is closed by the adversarial fan-out where every proposed edit is gated by the source PDF before it lands.
• Verification needs a schema and a text-layer PDF. Without an authoritative vocabulary you lose the first signal; without a real text layer (a scanned PDF) you lose the second.
• Structure quality equals outline quality. The tree is only as good as the section hierarchy you feed it, here the PDF bookmarks. Garbage outline, garbage tree.

FAQ

Isn’t MinerU’s Markdown output enough?

For reading, sometimes. For an addressable, agent-navigable, verified corpus, no. You need structure (the tree), a citation graph (the cross-links), and verification against ground truth. That’s the post-processing this toolkit does on top of what MinerU emits.

Why a per-section PDF cross-check instead of just trusting the schema?

Because a garble can collide with a valid name elsewhere (align is a real element), so the schema vocabulary alone passes it. The source page is the only authority on which name belongs here. Scoping the check to the section’s own pages keeps it cheap.

Do I have to use it on ECMA-376?

No, it’s document-agnostic. Supply your own Section hierarchy and a few callbacks and it runs on any MinerU output. See the README. ECMA-376 is just the worked example.

Relative or absolute cross-links?

Relative, computed within the tree. They’re identical wherever the tree is mounted, so the output ships anywhere with zero rewriting.

What block types does content_list.json contain?

text (with an optional text_level for headings), list, table (HTML in table_body), code (in code_body), equation, and image (with a VLM content description), plus page_number and header noise you filter out.

How do you handle code that MinerU split across a page?

The two halves arrive directly adjacent in the block stream, so merge adjacent code blocks. Genuinely separate examples always have prose between them, so they’re left untouched.

If this saved you time, the easiest way to say thanks is signing up for RunPod through this link. Star the repo on GitHub for updates.

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Disclosure: RunPod links in this post use a referral code that credits me at no cost to you. The post would read the same without it.

Tags:

• MinerU
• RunPod
• PDF parsing
• Document AI
• Post-processing
• Markdown

Clause-aligned batching for large PDFs on MinerU + RunPod

Jun 3, 2026

Sergei Shmakov

Last Updated: 2026-06-03

ECMA-376 Part 1 (the Office Open XML File Formats — Fundamentals and Markup Language Reference) is 5,039 pages of dense, table-heavy, XML-schema-laden specification in a single 35 MB PDF. It is the document that defines .docx, .xlsx, and .pptx down to the attribute. If you want a machine-readable, clause-addressable version of it, you have to parse all 5,039 pages, and almost everything about that page count makes a naive approach fall over.

This is the story of parsing the whole thing through the mineru-runpod serverless worker. The headline result: 36 batches, 5,039 pages, 46,637 content blocks, 4,174 tables, full contiguous coverage, ~$1.15 of GPU time. The interesting part is not the total. It’s how you cut a 5,000-page document into pieces without breaking it, which it turns out is a decision about clause structure, not page numbers.

Why not just send the whole 5,000-page PDF?

The worker accepts a file_url and parses front-to-back, so technically you could send all 5,039 pages as one job. You shouldn’t, for four reasons that all get worse with size:

• All-or-nothing failure. A single job that dies at page 4,800 (OOM, a transient GPU eviction, a timeout) costs you the entire run. At ~78 minutes of GPU work (more on that below), that’s an expensive coin flip.
• No resumability. One job has no natural checkpoint. If it fails you start over.
• The 20 MB response cap. MinerU’s output for a few hundred pages already blows past RunPod’s ~20 MB sync-response ceiling. For 5,000 pages it isn’t close: the extracted output here was 869 MB on disk.
• Memory. Holding the layout model output for thousands of pages in one process is a needless VRAM/RAM risk when the work is embarrassingly sliceable.

Batching fixes all four, but only if you batch at the right boundaries.

Why cut at clause boundaries instead of every N pages?

The obvious split is “every 100 pages.” The problem: a standard isn’t a stream of interchangeable pages, it’s a tree of clauses. Clause §17.4 (Tables) might start three lines from the bottom of a page and run for 40 pages. If a batch boundary lands in the middle of it, you’ve torn a logical unit across two parse jobs, and every downstream step (clause extraction, cross-referencing, chunking for retrieval) has to stitch it back together.

So I don’t cut by page count. I cut by clause:

1. Build an outline from the PDF’s 4,600+ bookmarks, giving a clause → page index for the whole document.
2. Place batch boundaries only at clause starts, never mid-clause.
3. Treat the huge top-level clauses (§17 WordprocessingML, §18 SpreadsheetML, §19 PresentationML, §20/§21 DrawingML, §22 Shared MLs, and the annexes) as mandatory anchors, so a big reference section always begins a fresh batch.
4. Aim for ~100 pages per batch, allow up to ~200, and accept whatever the nearest clause boundary gives.

The result was 36 batches averaging 140 pages (smallest 66, largest 238). Every batch starts and ends on a clause edge, so no clause is ever split across the seam between two parse jobs.

(One calibration gotcha specific to this PDF: printed page 1 is PDF page index 9. There’s a 9-page front-matter offset you have to fold into the bookmark→page mapping or every boundary is off by nine.)

A useful consequence: because the worker slices the PDF server-side via start_page/end_page (see the API reference), you never pre-split the PDF. You upload it once and each batch job asks for its page range out of the same source file.

Did the batches actually cover the whole document?

Yes, and this is worth verifying mechanically rather than trusting. After the run, I checked each batch’s produced page span against its planned range and confirmed the batches tile the document with no gaps and no overlaps:

Metric	Value
Batches	36
Pages	5,039 (contiguous 0–5038, 0 gaps, 0 overlaps)
Content blocks	46,637
Tables	4,174
Code blocks	3,591
Pages/batch	mean 140, min 66 (b35), max 238 (b25)
Output downloaded	~465 MB (compressed tarballs)
Output on disk	869 MB (extracted)

The contiguity check is the one piece of validation I wouldn’t skip on a document this size: it’s the difference between “the run finished” and “the run is complete.”

How was the document transported in and out?

Two different transports, for two different size problems.

Input: R2 URL. At 35 MB the PDF is well over the 20 MB inline (file_b64) limit, so it can’t ride in the request body. I put it on Cloudflare R2 and passed a public URL as file_url. The worker downloads it (≤200 MB cap) and slices the requested pages itself. One upload, 36 jobs read from it.

Output: transport="s3". Per-batch output is large (the biggest batch produced a 32 MB tarball), so embedding results in the sync response was out. With transport="s3", the worker uploads each result .tar.gz back to R2 and returns a presigned URL the client downloads and extracts. The tarball carries everything: content_list.json (the flat, typed, page-indexed block list I treat as source of truth), the rendered markdown, middle.json, and a layout-overlay PDF.

The presigned URL has a 1-hour TTL, which has a real consequence for batching: you must download each batch’s result as its job finishes, not in a sweep at the end of a 78-minute run. By then the early URLs have expired.

What GPU and backend, and what did throughput look like?

Backend: vlm-auto-engine (MinerU 2.5 Pro, the MinerU2.5-Pro-2605-1.2B vision-language model) on a 24 GB AMPERE_24 (RTX A5000-class) RunPod serverless GPU. One parse per worker (MINERU_MAX_CONCURRENCY=1: vLLM’s KV cache isn’t safe to drive from concurrent parses on a 24 GB card). For how to pick a card, see Choosing a GPU.

Across the 35 timed batches, total GPU compute was 4,674.8 s (77.9 min) at an overall 1.04 pages/sec, with individual batches ranging 0.84–1.27 pp/s depending on table density. A few representative batches:

Batch	Clause	Pages	Worker time	pp/s
b00	§1 Scope (front matter)	176	145.0 s	1.21
b01	§17 WordprocessingML	100	102.8 s	0.97
b17	§18.17.7 (functions)	176	147.9 s	1.19
b25	§21.2 DrawingML – Charts	238	231.8 s	1.03
b32	Annex L Primer	102	80.2 s	1.27

Cost worked out to roughly $0.00023/page, ~$1.15 for the whole standard. Before committing to that, a 3-page smoke test (cents, ~110–130 s dominated by cold start) validated the entire pipeline end-to-end (URL fetch → parse → R2 upload → download → extract), the cheapest insurance you can buy on a big run.

The parallelism lesson: it’s RunPod-side, not in the worker

This is the part that cost the most confusion: a single batch is already parallelized inside the worker (the VLM batches many page-images through the GPU at once), but running multiple batches at once is a RunPod scaling decision, not something you trigger by submitting more jobs.

I learned this the hard way. The client submitted 3 batches concurrently, and RunPod ran exactly one while two sat in the queue. The endpoint was configured workersMax=1: one GPU worker, one batch at a time, no matter how many jobs you fire. Raising workersMax to 3 (and matching the client’s concurrency) is what actually delivered 3×: the remaining 31 batches then finished in 27.8 minutes wall-clock. The scaling guide covers how concurrency and workersMax interact.

The mental-model fix:

• Inside one job: pages are parallelized on one GPU. Already maxed.
• workersMax: how many separate GPUs run separate jobs at once. This is your throughput dial.

A related myth worth busting: MinerU’s pipeline logs mention a window_size=64. That is a GPU throughput batch (how many page-images stream through the model at a time to bound VRAM), not a context window. Pages are recognized independently regardless of it, so it has zero effect on content continuity across pages. Which is exactly why clause-aligned batch boundaries matter and the internal window size doesn’t: continuity is something you protect at the batch layer, not by tuning a throughput knob.

Which clauses produced the most structure?

Block and table counts track the content shape of the standard almost perfectly: the reference-material and function-catalog clauses dominate:

Batch	Clause	Blocks	Tables
b25	§21.2 DrawingML – Charts	2,829	378
b17	§18.17.7 (spreadsheet functions)	2,805	239
b26	§22 Shared MLs	2,509	—
b10	§17.17 Miscellaneous	—	238
b11	§18 SpreadsheetML	—	224

These are the dense element/attribute reference tables that make ECMA-376 what it is. They’re a good reminder to spot-check table fidelity on exactly these batches before trusting the output downstream.

The annex schema dumps look completely different

The most striking per-batch contrast is the annexes. Annex A (W3C XML Schema), Annex B (RELAX NG) and friends are long code listings, not prose with tables, and the numbers show it. Same ~150-page batch size, radically smaller output:

Batch	Annex	Tarball
b29	Annex B (RELAX NG)	1.15 MB
b30	B.3 PresentationML	1.75 MB
b27	Annex A (XML Schema)	2.06 MB
b28	A.3 PresentationML	2.23 MB

Compare that to the prose-and-table batches that ran 31–32 MB (b10, b11) for a similar page count: roughly a 15× size difference driven entirely by content type. MinerU classifies the schema listings as code, so they compress to almost nothing relative to a table-dense reference section.

How did resumability actually work?

The runner keeps a manifest.json keyed by batch, and writes each batch’s result atomically: extract into a temporary directory, then rename into place. A batch is only marked ok after its download, extraction, and rename all succeed. Two payoffs:

• Pause/resume. Midway through, I paused the run to raise workersMax (you don’t want to change cluster settings while jobs are in flight). Stopping the client abandoned the in-flight jobs, but because their downloads hadn’t completed, the manifest never marked them done, so resuming re-ran them. Completed batches were skipped. No corruption, no duplicate downloads.
• Crash recovery is free. The same mechanism means any crash resumes from the last completed batch.

For a 36-job run that you might interrupt, the resumable manifest is what turns “a long fragile script” into “a process you can walk away from.”

Where this falls down / what I’d change

Honest limitations:

• 1-hour presign expiry forces eager download. You cannot defer pulling results to the end of a long run; download each batch as it lands. My runner does this, but it’s a constraint to design around, not a free lunch.
• Clause boundaries are only as good as the outline. The whole scheme leans on the PDF’s bookmark tree being accurate and complete. A document with missing or wrong bookmarks needs a fallback (TOC parsing, heading detection) before this works.
• Table/code fidelity needs spot-checking. 4,174 tables and 3,591 code blocks is a lot of structure to trust blindly; the dense reference batches (b25, b17, b11) and the annex code dumps are where I’d sample-verify first.
• One GPU is the ceiling. Throughput is fundamentally workersMax × per-GPU rate. There’s no in-job trick to go faster: you pay for more workers or you wait. And more workers means more cold starts, so wall-clock and cost don’t scale perfectly linearly.

What I’d change next time: drive client concurrency directly from the endpoint’s live workersMax so the two never drift, and prune the middle.json + layout PDF from batches where I only need content_list.json. They were roughly half the on-disk footprint.

FAQ

How long does it take to parse a 5,000-page PDF with MinerU?

About 78 minutes of single-GPU compute (~1 page/sec on a 24 GB RTX A5000-class card with the VLM backend), or ~28 minutes of wall-clock at 3× worker concurrency. Cost is roughly $1.15 total at ~$0.00023/page.

Why batch at clause boundaries instead of fixed page counts?

So no logical unit is split across two parse jobs. A clause can start mid-page and span dozens of pages; cutting by page count tears it in half and forces every downstream step to reassemble it. Cutting at clause starts keeps each clause whole within a batch.

How do you handle output larger than RunPod’s 20 MB response cap?

Use transport="s3": the worker uploads each result tarball to an S3-compatible bucket (Cloudflare R2 here) and returns a presigned URL you download. Per-batch output here reached 32 MB, far past the sync-response ceiling.

Does sending more concurrent jobs make a single endpoint faster?

No. Concurrency above the endpoint’s workersMax just fills the queue. Parallelism is the number of GPU workers RunPod runs, set by workersMax. Raise that to go wider.

Do I need to split the PDF before uploading?

No. Upload the full PDF once (or host it on R2 and pass file_url); each batch job requests its page range via start_page/end_page and the worker slices server-side.

How do I know the whole document was covered?

Verify mechanically: check each batch’s produced page span against its planned range and confirm the batches tile the document with zero gaps and zero overlaps. “The run finished” and “the run is complete” are not the same claim.

Where to next

The output is 36 batches of content_list.json with page-indexed, typed blocks. The next step is joining each block’s page back to the clause outline to emit a clause-addressable tree (one compact file per clause) that an agent or a retrieval index can navigate. The clause-aligned batching is what makes that join clean: every block already lives inside exactly one clause’s batch. Part 2 walks through that post-processing: cleaning the blocks, building the tree, cross-linking, and verifying the result against the source PDF.

ECMA-376 is freely available from Ecma International; it’s used here purely as a parsing benchmark. The parsed corpus is kept in a private repository for internal use, and this post shares only the parsing process and aggregate statistics, not the standard’s content.

If you want per-phase timings (fetch / parse / package) and throughput dashboards for a run like this, the worker can ship OpenTelemetry traces and metrics to any OTLP backend. See the observability guide.

If this saved you time, the easiest way to say thanks is signing up for RunPod through this link. Star the repo on GitHub for updates.

---

Disclosure: RunPod links in this post use a referral code that credits me at no cost to you. The post would read the same without it.

Tags:

• MinerU
• RunPod
• PDF parsing
• Batching
• Document AI
• Serverless

RunPod 20 MB Response Cap: Fix NoneType with Cloudflare R2

May 20, 2026

Sergei Shmakov

Last Updated: 2026-05-26

API note

The job-input field return shown below was renamed to transport, and success responses now wrap each parsed file in a results: [...] list — so today the presigned URL lives at result["results"][0]["tarball_url"] instead of result["tarball_url"]. The behavior described in this post (large outputs → S3 → small response) is unchanged. See the current API reference for the new field names; the snippets below are kept in their original form as a historical record.

If your RunPod serverless worker logs say done but your client raises unexpected handler return type: <class 'NoneType'>, you’ve hit RunPod’s bidirectional 20 MB payload cap on /runsync. The handler succeeded. The gateway dropped the response on the way back because the payload was too large.

The fix is two steps. Set return: "s3" on the job, and configure four env vars on the endpoint pointing at a Cloudflare R2 bucket. The worker uploads the result to R2 and returns a small presigned URL. Your client downloads from R2 directly. No gateway cap in the path.

I hit this on an 82-page Cyrillic fiscal report (30 MB input, ~25 MB output with embedded images) running my open-source mineru-runpod template. Two retries via return: "inline" and return: "tarball_b64" failed the same way. R2 mode worked first try. The rest of this post is the symptom, the env-var recipe, the cost comparison vs S3, and a few gotchas worth knowing.

Why does my RunPod worker return NoneType after a successful parse?

The worker handler completed and returned a valid dict. RunPod’s runtime then tried to POST that result back to RunPod’s API via /job-done, and the API returned HTTP 400 because the payload exceeded ~20 MB. The result was discarded. The SDK saw no output, returned None to the client, and the client wrapper raised the NoneType error.

The worker logs make the chain explicit:

[mineru-worker] done: elapsed=91.77s phase_ms={'fetch_input': 972, 'mineru_parse': 90789, 'package': 66}
{"requestId": "sync-fdcd03cd-...", "message": "Failed to return job results. | 400, message='Bad Request',
 url='https://api.runpod.ai/v2/<endpoint>/job-done/<worker>/sync-fdcd03cd-...?gpu=NVIDIA+RTX+A5000&isStream=false'"}

The first line shows the handler finished cleanly: 82 pages parsed in 91.8 s on the worker (this test ran on A5000; on the current 4090 default the warm parse is 2–3× faster). The second line shows the gateway rejecting the result. The handler already returned and never knows the rejection happened. The SDK sees the discarded result and returns None to your code.

If you see this NoneType error on a small doc, the diagnosis is different (worker OOM, crash, timeout). On a multi-page parse that the worker logs as done, the answer is almost always the 20 MB cap.

What is RunPod’s /runsync response payload limit?

RunPod’s /runsync gateway caps payloads at roughly 20 MB in both directions. The request cap affects file_b64 inline uploads. The response cap affects what the worker can return. Both are independent of execution time and memory budget. A fast, successful parse can hit the response cap simply by producing a large output.

Direction	Limit	What triggers it
Request → gateway → worker	~20 MB	file_b64 inline transport for large PDFs
Worker → gateway → client	~20 MB	Multi-page parse outputs with embedded images

The request cap is in RunPod’s docs and widely discussed. The response cap is mentioned only in passing. I found three open issues on the runpod-workers repos where other users hit the same symptom and didn’t realise what it was, so this post is partly to make that searchable.

Practical threshold for mineru-runpod: pure-text PDFs are fine for longer. Image-heavy PDFs with embedded raster output hit the response cap around 50–80 pages on inline or tarball_b64 transport.

Does return: "tarball_b64" get around the 20 MB cap?

No. return: "tarball_b64" gzips the output into a single .tar.gz before base64-encoding it. Gzip compresses the JSON and Markdown text well, but the page images inside the tarball are already raster bytes (PNG, JPEG) and barely compress further. Multi-page parses with embedded images keep the tarball over 20 MB.

I confirmed this on the same 82-page PDF. Same 400 from /job-done. Same NoneType in the client. Both inline and tarball_b64 route through the gateway response, so both inherit the cap. Only return: "s3" avoids it because the worker uploads out-of-band.

How do I configure Cloudflare R2 to bypass the RunPod response cap?

Set return: "s3" in the job input, then add four env vars on the RunPod endpoint pointing at a Cloudflare R2 bucket. The worker uploads the gzipped tarball directly to R2 and returns a small presigned URL (~1 h TTL). Your client downloads from R2.

The job input changes one field:

{
  "input": {
    "file_url": "https://example.com/big.pdf",
    "return": "s3"
  }
}

The four env vars go on the endpoint (not the template — they’re secrets):

Env var	Cloudflare R2 value
BUCKET_ENDPOINT_URL	https://<account-id>.r2.cloudflarestorage.com
BUCKET_NAME	your bucket name
BUCKET_ACCESS_KEY_ID	R2 API token access key
BUCKET_SECRET_ACCESS_KEY	R2 API token secret
BUCKET_REGION (optional)	auto

You generate the access key pair in the Cloudflare dashboard: R2 → Manage R2 API Tokens → Create API Token → Object Read & Write scoped to the bucket. The worker auto-restarts when you save endpoint env vars in RunPod. Test with one small doc before sending production traffic.

Why pick Cloudflare R2 over AWS S3 for RunPod output storage?

R2 has zero egress fees, a 10 GB free storage tier, 1M Class A ops and 10M Class B ops per month free, and is fully S3-compatible. AWS S3 charges egress at roughly $0.085/GB plus storage at $0.023/GB/month. For a RunPod pipeline doing dozens of GB of I/O per month, R2’s bill stays near zero while S3 lands in the $5–$15 range.

A back-of-envelope month for the workload I tested:

• 1,000 multi-page parses, average output 8 MB → 8 GB stored then deleted
• 1,000 worker→bucket uploads + 1,000 client→bucket downloads = 2,000 ops
• Storage: free (under 10 GB). Egress: free (R2 doesn’t bill egress). Ops: free (well under 1M Class A).

Same workload on S3: ~$0.18 storage + ~$0.68 egress + per-request fees, maybe $1–$3 total. Cheap but R2’s $0 is cheaper.

S3 still makes sense if you’re already deep in AWS, if you need IAM-controlled access patterns, or if RunPod workers and your AWS region are colocated tightly enough that egress doesn’t apply. For everyone else and especially for solo / indie deploys, R2 is the right default. See R2 pricing for current rates.

What does the parse flow look like end-to-end with return: "s3"?

The worker fetches the input PDF, runs MinerU, gzips the outputs into a tarball, uploads to R2 via the configured BUCKET_* env vars, and returns a small JSON response with tarball_url, tarball_url_expires_in (3600 s), and bucket_key. Your client follows the URL and extracts the tarball locally. No payload ever crosses RunPod’s 20 MB-capped response path.

Concrete numbers from the 82-page test (on A5000; current default is 4090):

result = client.parse_document(
    file_url="https://pub-....r2.dev/report.pdf",
    backend="vlm-auto-engine",
    return_format="s3",
)
# result["tarball_url"]            -> presigned R2 URL, valid ~1 h
# result["tarball_url_expires_in"] -> 3600
# result["bucket_key"]             -> "report-<hash>.tar.gz"

client.save_s3_tarball(result, "./out/")
# downloads + extracts -> out/report.md, out/report_content_list_v2.json, out/images/, ...

End-to-end wall-clock: 211 s for an 82-page doc on a cold worker. Breakdown: ~112 s before MinerU started parsing (worker boot + warmup), ~92 s warm parsing (1.1 s/page on A5000), ~11 s gzip and upload to R2 (the package phase). The extracted output: 313 KB Markdown plus structured JSON plus per-page images. Roughly 3.5 minutes for a document that previously couldn’t return its output at all.

The cold-start portion is a separate concern from the response cap. The FlashBoot mechanism investigation covers why the ~112 s exists, how the boot-time warmup interacts with RunPod’s snapshot system, and when subsequent cold starts are much faster.

What should I watch out for with the R2 bridge?

Four things the docs don’t say loudly. The presigned URL TTL is 60 minutes. R2 doesn’t auto-clean uploaded objects. One bucket can serve input and output. The 20 MB cap applies to /run (async) too, not just /runsync.

• Presigned URL TTL is 60 minutes. If your client is slow to download (e.g. a job-queue worker that picks up results minutes later), bump _S3_PRESIGN_TTL_SECONDS in the handler. Don’t rely on the default in long-tail flows.
• R2 doesn’t auto-clean uploaded objects. Add an R2 lifecycle rule (e.g. delete after 7 days) so your output bucket doesn’t grow forever.
• One R2 bucket can serve input and output. Upload your PDFs to R2 ahead of time, pass file_url pointing at the R2 public dev URL, and the worker writes outputs to the same bucket at the root. Add BUCKET_PREFIX env var if you want outputs in a subdirectory.
• The 20 MB cap applies to /run (async) too. Same gateway, same limit. Switching to async polling doesn’t help.

FAQ

How do I get the R2 access key for BUCKET_ACCESS_KEY_ID and BUCKET_SECRET_ACCESS_KEY?

In the Cloudflare dashboard: R2 → Manage R2 API Tokens → Create API Token. Set permissions to “Object Read & Write” scoped to the specific bucket. Cloudflare shows the access key ID and secret access key once; copy both into your RunPod endpoint env vars immediately. The secret isn’t retrievable later.

Does the presigned URL expire?

Yes. The default TTL is 3600 seconds (one hour). If your downstream client picks up the response asynchronously (job queue, cron, etc.), download promptly or bump _S3_PRESIGN_TTL_SECONDS in the worker handler before redeploying.

Can I reuse the same R2 bucket for input and output?

Yes. The worker doesn’t care about the bucket layout. Upload your input PDFs to bucket/inputs/ and the worker writes outputs to bucket/<basename>-<hash>.tar.gz at the root. Add BUCKET_PREFIX env var if you want outputs pushed into a subdirectory.

What if I can’t set up R2? Is there a fallback?

Page chunking. Split the parse with start_page and end_page into segments small enough that each output tarball stays under 20 MB, then concatenate the .md files client-side. Slower (you may pay multiple cold starts if the worker scales to zero between calls) and you handle joining yourself, but no infra changes needed.

Is the 20 MB cap on /run too, or only /runsync?

Both. RunPod’s /run (async) and /runsync (synchronous) share the same gateway and the same payload limits. Switching to async doesn’t help the response-size problem. The cap is at the gateway layer, not the polling protocol.

Does using return: "s3" add to cold-start time?

No. The S3 upload happens at the end of the parse, not the beginning. The handler’s package phase grew from ~95 ms (in-memory tarball) to ~11 s (gzip + upload to R2) on an 82-page job, but cold-start is unchanged. The S3 mode adds a small constant to warm-job latency, not a multiplier.

How big can the R2-uploaded tarball be?

Effectively unlimited for mineru-runpod workloads. R2 supports multipart uploads up to 5 TB per object. You’ll hit the worker’s executionTimeoutMs long before you hit R2’s per-object limit.

Does R2 work for input PDFs too, or only output?

Both. The worker accepts file_url pointing at an R2 public dev URL (or a presigned R2 GET URL for private buckets) and fetches the input from R2. This avoids the inbound 20 MB cap on file_b64 for large PDFs. You can run an R2-in / R2-out setup with one bucket and avoid every payload-size limit RunPod has.

Where to next

If you’ve shipped a multi-page PDF pipeline on RunPod and you’re not using return: "s3", you’ll hit the gateway cap eventually. Set it up before you need it. The cost is ten minutes of env-var configuration and possibly zero dollars per month at indie volumes.

If you’re new to the template, the getting-started guide walks through the full deploy in about ten minutes. For the cold-start side of the picture (separate from the response cap covered here), see the FlashBoot mechanism investigation. For GPU sizing, Choosing a GPU covers when the default ADA_24 (RTX 4090) is enough and when to opt up.

If this saved you time, the easiest way to say thanks is signing up for RunPod through this link. Star the repo on GitHub for updates.

---

Disclosure: RunPod links in this post use a referral code that credits me at no cost to you. The post would read the same without it.

Tags:

• RunPod
• Cloudflare R2
• Serverless
• MinerU
• PDF

Serverless MinerU on RunPod: honest cost math (2026)

May 19, 2026

Sergei Shmakov

Last Updated: 2026-05-26

API note

The job-input field return was renamed to transport (with an orthogonal formats filter for inline outputs), and success responses now wrap each parsed file in a results: [...] list — see the current API reference. Snippets below predate that change and use the older return field; they’re left intact as the post’s original record.

If you’re building a RAG pipeline, a document indexer, or any product that ingests PDFs at scale, you’ve probably hit the same wall I did. Hosted OCR APIs charge pennies per page that compound into thousands per million. CPU parsers are too slow for production volume. A permanent GPU pod is wasteful when traffic comes in bursts.

MinerU 2.5 is genuinely state-of-the-art for PDF → Markdown / structured JSON. Apache 2.0 license. The MinerU2.5-Pro-2604-1.2B model fits comfortably on a 24 GB GPU. RunPod Serverless scales to zero when nothing is calling. Wiring those two together is the obvious move.

Real numbers from my open-source mineru-runpod template, measured on a 24 GB RTX 4090 in May 2026: ~$0.001 per page for warm parses, plus a ~$0.03 fixed tax per cold start. The all-in per-page cost depends on how much work you do before the worker scales back to zero. Here’s the deploy, the response shape, and the workload patterns this template is the right fit for.

What does it actually cost to run MinerU on RunPod Serverless?

About $0.001 per page on an RTX 4090 once the worker is warm. Each scale-from-zero adds a ~$0.03 fixed tax: roughly 110 seconds of GPU billing for vLLM engine init plus model load. Per-page math depends entirely on amortization. Sparse traffic with one short request per cold start lands closer to $0.005–$0.01 per page.

Real workload-shape math using ADA_24 (RTX 4090, ~$1.10/hr Flex):

Workload shape	Per-page cost
1,000 pages amortized across one cold start	~$0.001
100 pages amortized across one cold start	~$0.0013
10 pages then idle out	~$0.004
One short doc per scale-from-zero (worst case)	~$0.007

Compared to alternatives:

Tool / setup	Per-page cost	Notes
Hosted OCR APIs (typical)	$0.001 – $0.01	vendor lock-in, rate limits, documents leave your stack
Permanent GPU pod (24 h on A5000)	$0.001 – $0.003	24 h of bills whether you use it or not
mineru-runpod, amortized	~$0.001 – $0.004	scales to zero; cold-start tax is real
Marker / Nougat on CPU	$0 cash, $$$ time	~30 s/page sequential (Marker docs)

The trick is RunPod’s per-second billing. No worker running, no bill. The catch is every scale-from-zero pays a real fixed cost.

How do I deploy MinerU to RunPod Serverless in ten minutes?

Fork the repo, point RunPod’s GitHub auto-build at your fork, create a Serverless Endpoint with ADA_24 (RTX 4090) and FlashBoot enabled, send a request via the included Python client. Total wall-clock from RunPod sign-up to first parse: roughly ten minutes, dominated by the image build (~5–10 min) plus the first cold start (~110 s).

1. Get a RunPod account

Sign up here. Add $5 of credit. That covers several thousand cold starts plus a few million warm pages.

2. Fork the repo

gh repo fork sergeyshmakov/mineru-runpod --clone
cd mineru-runpod

The repo stays small. Dockerfile, handler.py, a worker/ package, a Python client (mineru_client), three GitHub Actions workflows, Hub metadata under .runpod/. MIT licensed, ~30 files.

3. Wire RunPod’s GitHub auto-build

In the RunPod dashboard:

1. Serverless → Templates → New → Import Git Repository
2. Point at your fork. Branch main, Dockerfile path Dockerfile.
3. RunPod clones, builds the image, stores it in its own registry, and gives you a template_id. The build runs ~5–10 minutes. Watch the log if you want.

4. Create the endpoint

Dashboard path:

• Serverless → Endpoints → New
• Template: the one you just created
• GPU pool: ADA_24 (RTX 4090, 24 GB)
• Workers min: 0, max: 3
• Idle timeout: 10 seconds
• FlashBoot: on
• Save, grab the endpoint id

Or as code (reproducible across redeploys):

pip install -e .[deploy]
python deploy.py --template-id <tid>

deploy.py exposes every endpoint setting as a CLI flag.

5. Parse your first PDF

from mineru_client import MineruClient

client = MineruClient(
    endpoint_id="<your-endpoint-id>",
    api_key="<your-runpod-api-key>",
)
result = client.parse_document(
    file_url="https://example.com/report.pdf",
    end_page=4,  # smoke test on first 5 pages
)
client.save_tarball(result, "./out/doc")
# → ./out/doc/<basename>.md
# → ./out/doc/<basename>_content_list_v2.json
# → ./out/doc/<basename>_middle.json
# → ./out/doc/images/*.png

First parse pays a cold start. Subsequent parses on the same warm worker run at ~1–6 s/page on the 4090, content density dependent. After 10 s of idle, the worker scales to zero.

What does the MinerU response actually contain?

Three structured outputs plus extracted images. <basename>.md is Markdown with LaTeX equations, HTML tables, and image references. <basename>_content_list_v2.json is a flat list of typed entries (text, equation, table, image, code) each tagged with page_idx. <basename>_middle.json carries the full layout with bounding boxes and reading order. Pick the transport via return: tarball_b64, inline, or s3.

For a document indexer or RAG pipeline, content_list_v2.json is the file you’ll spend the most time with. Group entries by level: "title" boundaries for section-based chunking. Embed each chunk and store page_idx for citation back to the source.

The Markdown is for human-readable display. middle.json has bounding boxes per span when you need page coordinates for hover-to-source UI.

Transport options on the request: tarball_b64 (default) for outputs under ~20 MB, inline if you want the markdown directly in the JSON response, s3 for anything that would exceed RunPod’s response cap. See the R2 bridge post for the s3 setup.

When does mineru-runpod fit your workload, and when doesn’t it?

Good fit: batch ingest jobs, bursty traffic (50 docs in a minute, then quiet), background pipelines, OCR-API replacement. Poor fit: interactive single-document apps (cold starts make users think it’s broken), sparse traffic (one job per cold start dominates the bill), strict latency SLOs without provisioning workers_min ≥ 1.

I run this template in production for a document indexer. Six months of operation, here’s the honest fit picture:

Good fit:

• Batch ingest. Drop 500 PDFs into a queue. One cold start amortizes across the whole batch at ~$0.001 per page.
• Bursty traffic. A user uploads 50 documents in a minute. One cold start, 49 warm parses.
• Background pipelines. Nightly cron processes yesterday’s intake. Cold start cost is rounding error against a multi-hour batch.
• OCR-API replacement. Comparable per-page cost without shipping documents to a third party.

Poor fit:

• Interactive single-document parsing. Your user uploads one PDF and waits two minutes for the cold start. They’ll think it’s broken.
• Sparse traffic (one job every 20–60 min). Almost every request is a cold start. The ~$0.03 cold-start tax dominates. Rent a permanent low-tier GPU pod and skip serverless instead.
• Strict latency SLOs. Cold-start latency is partly outside your control. Provisioning workers_min ≥ 1 eliminates cold starts but you pay for the warm worker around the clock.

The repo’s defaults (workers_min=0, idle_timeout=10s) are tuned for batch-with-bursts. The dashboard’s scaling settings are where you tune for other patterns.

What’s the real cold-start cost on RunPod Serverless?

Roughly 110 seconds before MinerU starts parsing your first request after a scale-from-zero. The composition: ~3 s fitness checks, ~20 s vLLM engine config, ~20 s model load, ~25 s torch.compile, ~5 s CUDA graph capture, ~5 s of actual parse. Billed at ~$1.10/hr on the 4090 default, that’s roughly $0.03 per cold start.

The per-phase breakdown is documented in the troubleshooting guide if you want to see where the time goes. The boot-time warmup in this template loads MinerU’s model and JIT-compiles vLLM kernels before the worker accepts requests. When RunPod’s FlashBoot snapshot is available on a subsequent scale-from-zero, the wall-clock drops to ~7–8 seconds because the snapshot captured a warm process. When the snapshot isn’t available (new host, image rebuild), warmup re-runs and you pay the full ~110 s again.

The FlashBoot mechanism investigation covers when the fast path applies, with measured numbers across multiple consecutive cold starts.

What should I watch out for before going to production?

Three production gotchas the marketing won’t mention. The 20 MB response cap silently drops large outputs (symptom: NoneType after a successful parse — covered by the R2 bridge). execution_timeout defaults to 900 s and won’t cover full books. file_b64 inline payloads cap around 10 MB on the way in. None of these crash the worker; they manifest as confusing client-side errors.

• 20 MB response cap. RunPod’s /runsync gateway drops responses over ~20 MB. Multi-page parses with embedded images hit this around 50–80 pages. Worker logs done; client gets NoneType. Fix: return: "s3" + Cloudflare R2, walked through in the R2 bridge post.
• Long-job timeout. Repo defaults execution_timeout=900s (good for ~150–300 pages on 4090). A 5,000-page book is 80–500 minutes depending on content density. Bump execution_timeout for long jobs; the endpoint upper limit is 24 hours.
• Inline payload cap on the way in. file_b64 requests cap around 10 MB. For bigger files, pass file_url and let the worker fetch from your storage. R2 public dev URLs work well.
• Cold-start economics. “Pennies per page” depends on amortization. Track average pages per cold start in your logs. If it’s under 30, bump idle_timeout or run workers_min=1.

Where to next

The repo ships with:

• Typed Python client (MineruClient)
• deploy.py / destroy.py for endpoint lifecycle automation
• Reference adapter pattern for wrapping MinerU output into domain models
• 96 unit tests, CI on every PR
• Commitlint + semantic-release for automated CHANGELOG / GitHub Releases

For the deeper context that didn’t fit:

• How RunPod FlashBoot actually works — four-request investigation into the cold-start mechanism and the per-host snapshot caveat.
• The R2 bridge for the 20 MB response cap — fix for NoneType on multi-page outputs.
• Choosing a GPU — when 24 GB is enough, when to opt up to 48 GB.

If this saved you time, the easiest way to say thanks is signing up for RunPod through this link. Star the repo on GitHub for updates.

FAQ

How does mineru-runpod compare to hosted PDF APIs?

Per-page cost is in the same ballpark ($0.001–$0.004) when amortizing cold starts across reasonable batches. The differences are control and lock-in. You deploy your own RunPod endpoint, pick your GPU and concurrency, run whichever MinerU version you want, and never send documents to a third party. The trade-off is operating a serverless template instead of consuming a managed API.

Can MinerU 2.5 handle non-English PDFs?

Yes. The vlm-auto-engine default backend handles English and Chinese well per the model card. For other scripts (Cyrillic, Arabic, Devanagari, Japanese, Korean), the pipeline backend uses PaddleOCR with script-family models, covering 109 languages. Empirically the Pro VLM also handles Cyrillic correctly even though lang is ignored on the VLM path. Switch backends per-request via the backend field.

What’s the difference between vlm-auto-engine, pipeline, and hybrid-auto-engine?

vlm-auto-engine uses MinerU’s 1.2B VLM via vLLM. Fastest on English / Chinese, ~1–6 s/page warm. pipeline uses PaddleOCR plus dedicated layout / formula / table models. Slower (~3–5 s/page) but more memory-predictable (4 GB minimum VRAM) and covers 109 languages. hybrid-auto-engine routes each page through either backend based on content. Highest quality on mixed-content docs; needs 48 GB on dense layouts.

Does the per-page cost include the cold-start tax?

No. The ~$0.001 per page is warm-worker math. Each scale-from-zero adds a roughly $0.03 fixed cost on the 4090 default. Your effective per-page cost is (0.001 × pages) + (0.03 × cold_starts) / pages. For 100 pages across one cold start, that’s $0.0013 per page. For 10 pages, it’s $0.004.

Can I use mineru-runpod with my own MinerU model?

Yes. Fork the repo and update the Dockerfile’s huggingface_hub.snapshot_download call to point at your model. Rebuild and redeploy. The handler is model-agnostic; MinerU’s aio_do_parse resolves whatever model is in HF_HOME at runtime.

What GPU does the template default to?

ADA_24 (RTX 4090, 24 GB). Switched from AMPERE_24 (A5000) on 2026-05-26 after measuring per-page cost. The 4090 is 2–4× faster per page than the A5000 and cheaper per page despite the higher hourly rate. See Choosing a GPU for the full math and when to opt up to 48 GB.

How do I keep my RunPod endpoint warm to avoid cold starts?

Set workers_min=1 on the endpoint. You pay for the always-on worker around the clock (~$0.000306/s on the 4090 default, so ~$26/day or ~$800/month). Worth it if your traffic is steady enough that the warm worker stays busy, or if your latency SLO can’t tolerate the cold-start window. For bursty traffic, workers_min=0 with FlashBoot enabled is usually cheaper.

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Tags:

• PDF
• RunPod
• Serverless
• MinerU
• RAG