ERNIE-Doc (247M) TPS calculator

Open weights Baidu 247M parameters December 2020

Each card below is assessed against this model at the context length and minimum quality you choose. Speed is an estimate for a single request, calculated from the card's memory bandwidth and the size of the model once compressed.

Calculated for this model

818 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla C1080

4 GB · Q8_0 · 149 tok/s

Fastest card

B200

13,718 tok/s · 180 GB

Which GPUs can run ERNIE-Doc (247M)?

Set the inputs, read the answer

Context length tokens

A longer conversation needs more memory, which can push this model off smaller cards.

Minimum quality

Hides cards that would only fit the model by compressing it below this point.

Search cards

Card memory

Also show cards that cannot run it

818 cards match

Calculating

					Needs	Quantisation	Fit
13,718 tok/s 8,231–21,948 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.0 GB	Q8_0	Comfortable
13,718 tok/s 8,231–21,948 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.0 GB	Q8_0	Comfortable
10,954 tok/s 6,572–17,526 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.0 GB	Q8_0	Comfortable
10,954 tok/s 6,572–17,526 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.0 GB	Q8_0	Comfortable
8,760 tok/s 5,256–14,017 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.0 GB	Q8_0	Comfortable
8,385 tok/s 5,031–13,416 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.0 GB	Q8_0	Comfortable
8,385 tok/s 5,031–13,416 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.0 GB	Q8_0	Comfortable
8,025 tok/s 4,815–12,840 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.0 GB	Q8_0	Comfortable
7,122 tok/s 4,273–11,395 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.0 GB	Q8_0	Comfortable
7,122 tok/s 4,273–11,395 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.0 GB	Q8_0	Comfortable
7,122 tok/s 4,273–11,395 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.0 GB	Q8_0	Comfortable
6,756 tok/s 4,054–10,809 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.0 GB	Q8_0	Comfortable
5,761 tok/s 3,457–9,218 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.0 GB	Q8_0	Comfortable
5,761 tok/s 3,457–9,218 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.0 GB	Q8_0	Comfortable
5,761 tok/s 3,457–9,218 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.0 GB	Q8_0	Comfortable
5,761 tok/s 3,457–9,218 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.0 GB	Q8_0	Comfortable
5,761 tok/s 3,457–9,218 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.0 GB	Q8_0	Comfortable
4,387 tok/s 2,632–7,019 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.0 GB	Q8_0	Comfortable
4,387 tok/s 2,632–7,019 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.0 GB	Q8_0	Comfortable
3,656 tok/s 2,193–5,849 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.0 GB	Q8_0	Comfortable
3,578 tok/s 2,147–5,724 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.0 GB	Q8_0	Comfortable
3,498 tok/s 2,099–5,597 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.0 GB	Q8_0	Comfortable
3,498 tok/s 2,099–5,597 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.0 GB	Q8_0	Comfortable
3,498 tok/s 2,099–5,597 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.0 GB	Q8_0	Comfortable
3,498 tok/s 2,099–5,597 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	1.0 GB	Q8_0	Comfortable

Speeds are estimates for a single request — one conversation at a time — calculated from memory bandwidth, model size and quantisation. Real throughput varies with the inference runtime and its version. Figures published by hardware vendors measure many simultaneous requests and are much higher.

On record

Full specification

Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.

Origin

Who built this model, where, and when it was published.

Organisation: Baidu
Organisation type: Industry
Country: China
Published: 31 December 2020
Authors: Siyu Ding, Junyuan Shang, Shuohuan Wang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

What it does

The problem areas the model was built for. A model can carry several of each.

Domain: Language
Task: Language modeling, Language modeling/generation

Size

How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.

Parameters: 247M
Training data: 103,000,000 tokens
Epochs: 190.88

Training compute

The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.

Training compute: 3 × 10¹⁹ FLOP
How it was established: Operation counting

Availability

Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.

Weights: Open — downloadable
Model access: Open weights (unrestricted)
Training code: Unreleased

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Why it is tracked: SOTA improvement
Record confidence: Confident
Citations: 64
Benchmark data: ERNIE-Doc (247M)

Sources

Where this record came from and when it was last checked.

Reference: ERNIE-Doc: A Retrospective Long-Document Modeling Transformer
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run ERNIE-Doc (247M)

Ranked by estimated tokens per second, newest card first where speeds tie. Because generation is bound by memory bandwidth, this ordering follows bandwidth rather than any gaming benchmark.

The smallest GPUs that still run ERNIE-Doc (247M)

The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.

What the numbers mean

What you need to run it

Minimum card

Tesla C1080

Memory needed

1.0 GB

Fastest

13,718 tok/s

ERNIE-Doc (247M) is small enough at 247M parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The least hardware that works is a Tesla C1080. Its 4 GB is enough at Q8_0 compression, giving roughly 149 tokens per second.

The quickest result comes from a B200 at around 13,718 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Background

ERNIE-Doc (247M) was published by Baidu, in China, in December 2020. The organisation is categorised as industry.

It works in Language, and is recorded as doing language modeling, Language modeling/generation.

Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all.

Reading the throughput figures

The median result is around 385.2 tokens per second; 818 cards produce text faster than most people read it.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

Without the attention layout on record, the memory column is an approximation. It is close enough to choose hardware by, and least reliable at long context.

What went into building it

The training run consumed about 3 × 10¹⁹ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

Around 103,000,000 tokens went into training it.

It is tracked in the underlying dataset for one reason in particular: sOTA improvement.

Step by step

How to choose a GPU for ERNIE-Doc (247M)

The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.

01

Start from the memory column

Every card here has been checked against ERNIE-Doc (247M) — around 1.0 GB at Q8_0. Capacity is the gate — a card either holds it or it does not.
02

Set the context length you will work at

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason ERNIE-Doc (247M) stops fitting a card that seemed fine.
03

Choose how far you will compress it

Compression is what makes ERNIE-Doc (247M) fit smaller cards, at some cost in accuracy — Q8_0 on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Compare tokens per second, not specifications

Sort by speed to see how cards rank for ERNIE-Doc (247M). It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 13,718 tok/s.
05

Read the fit column last

A tight fit runs ERNIE-Doc (247M) but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

See what else that card runs

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for ERNIE-Doc (247M) alone — a card is usually bought for more than one model.

Answers

ERNIE-Doc (247M) — common questions

Why does the quantisation differ between cards for ERNIE-Doc (247M)?

Each card is shown running the least-compressed copy it can hold, and ERNIE-Doc (247M) appears at 1 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these ERNIE-Doc (247M) speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 8,231–21,948 tok/s on the B200, for instance. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

What GPU do I need to run ERNIE-Doc (247M)?

The smallest card in our catalogue that holds ERNIE-Doc (247M) is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.0 GB, and produces roughly 149 tokens per second. 818 cards in total can run it.

How fast is ERNIE-Doc (247M) on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 13,718 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 818 of the cards that can run ERNIE-Doc (247M) clear that.

How much VRAM does ERNIE-Doc (247M) need?

About 1.0 GB at Q8_0 compression, which is what the smallest card that runs it uses. Less compression needs more: the figures in the memory column above are recalculated for each card, because each one holds the least-compressed version it can.

Can I run ERNIE-Doc (247M) on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 1.0 GB and generating roughly 2,555 tokens per second — a comfortable fit.

Can I run ERNIE-Doc (247M) on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 1.0 GB and generating roughly 1,564 tokens per second — a comfortable fit.

Can I run ERNIE-Doc (247M) on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 1.0 GB and generating roughly 1,938 tokens per second — a comfortable fit.

Can I run ERNIE-Doc (247M) on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 1.0 GB and generating roughly 2,298 tokens per second — a comfortable fit.

Is ERNIE-Doc (247M) open source?

Its weights are published, so ERNIE-Doc (247M) can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.

How many parameters does ERNIE-Doc (247M) have?

ERNIE-Doc (247M) has 247M parameters. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.

Who created ERNIE-Doc (247M)?

ERNIE-Doc (247M) was published by Baidu, based in China, categorised as industry.

When was ERNIE-Doc (247M) released?

ERNIE-Doc (247M) was published in December 2020. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

What is ERNIE-Doc (247M) used for?

ERNIE-Doc (247M) works in Language, and is recorded as handling language modeling, Language modeling/generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download ERNIE-Doc (247M)?

The weights for ERNIE-Doc (247M) are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

How much compute was used to train ERNIE-Doc (247M)?

Around 3 × 10¹⁹ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.

Can I run ERNIE-Doc (247M) if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part in system memory drags the whole thing down. Our figures for ERNIE-Doc (247M) assume it is fully resident.

Would two GPUs run ERNIE-Doc (247M) faster?

A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run ERNIE-Doc (247M) alone, the case for pairing is weak.

Source

Original publication

Record last updated 25 May 2026

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