AWD-LSTM-DOC (fin) (37M) TPS calculator

Open weights NTT Communication Science Laboratories,Tohoku University 37M parameters August 2018

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 · 996 tok/s

Fastest card

B200

91,574 tok/s · 180 GB

Which GPUs can run AWD-LSTM-DOC (fin) (37M)?

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
91,574 tok/s 54,944–146,518 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	0.7 GB	Q8_0	Comfortable
91,574 tok/s 54,944–146,518 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	0.7 GB	Q8_0	Comfortable
73,124 tok/s 43,874–116,999 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	0.7 GB	Q8_0	Comfortable
73,124 tok/s 43,874–116,999 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	0.7 GB	Q8_0	Comfortable
58,481 tok/s 35,089–93,570 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	0.7 GB	Q8_0	Comfortable
55,975 tok/s 33,585–89,559 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.7 GB	Q8_0	Comfortable
55,975 tok/s 33,585–89,559 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.7 GB	Q8_0	Comfortable
53,571 tok/s 32,142–85,713 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	0.7 GB	Q8_0	Comfortable
47,544 tok/s 28,526–76,070 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	0.7 GB	Q8_0	Comfortable
47,544 tok/s 28,526–76,070 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	0.7 GB	Q8_0	Comfortable
47,544 tok/s 28,526–76,070 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	0.7 GB	Q8_0	Comfortable
45,100 tok/s 27,060–72,160 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
38,461 tok/s 23,077–61,538 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
38,461 tok/s 23,077–61,538 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	0.7 GB	Q8_0	Comfortable
38,461 tok/s 23,077–61,538 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
38,461 tok/s 23,077–61,538 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
38,461 tok/s 23,077–61,538 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
29,285 tok/s 17,571–46,857 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	0.7 GB	Q8_0	Comfortable
29,285 tok/s 17,571–46,857 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	0.7 GB	Q8_0	Comfortable
24,404 tok/s 14,643–39,047 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	0.7 GB	Q8_0	Comfortable
23,884 tok/s 14,330–38,214 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	0.7 GB	Q8_0	Comfortable
23,351 tok/s 14,011–37,362 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	0.7 GB	Q8_0	Comfortable
23,351 tok/s 14,011–37,362 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	0.7 GB	Q8_0	Comfortable
23,351 tok/s 14,011–37,362 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	0.7 GB	Q8_0	Comfortable
23,351 tok/s 14,011–37,362 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	0.7 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: NTT Communication Science Laboratories,Tohoku University
Organisation type: Industry,Academia
Country: Japan
Published: 30 August 2018
Authors: Sho Takase, Jun Suzuki, Masaaki Nagata

What it does

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

Domain: Language
Task: Language modeling

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: 37M
Training data: tokens
Epochs: 300

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: Open source

How it is classified

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

Record confidence: Confident
Citations: 37
Benchmark data: AWD-LSTM-DOC (fin) (37M)

Sources

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

Reference: Direct Output Connection for a High-Rank Language Model
Last updated: 11 February 2026

The extremes

The ten fastest GPUs that run AWD-LSTM-DOC (fin) (37M)

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 AWD-LSTM-DOC (fin) (37M)

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

The hardware side

Minimum card

Tesla C1080

Memory needed

0.7 GB

Fastest

91,574 tok/s

AWD-LSTM-DOC (fin) (37M) is small enough at 37M parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

At the low end, a Tesla C1080 handles it — 4 GB, at Q8_0, for about 996 tokens per second.

Top of the range is the B200, at roughly 91,574 tokens per second thanks to 8,000 GB/s of bandwidth.

About this model

AWD-LSTM-DOC (fin) (37M) was published by NTT Communication Science Laboratories,Tohoku University, in Japan, in August 2018. industry,Academia is the category the publisher falls under.

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

The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.

How fast it runs, and why

Across every card that can run it, the middle of the range is about 2,571.4 tokens per second, and 818 of them clear the ten tokens per second that roughly matches reading speed.

Being dense, it reads all of itself per token, which is why the ordering by speed below follows the ordering by memory bandwidth so closely.

Memory here is estimated from size rather than computed from the architecture, which is not recorded for this model — the numbers are indicative rather than exact.

Step by step

How to choose a GPU for AWD-LSTM-DOC (fin) (37M)

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

Look at what AWD-LSTM-DOC (fin) (37M) actually needs — around 0.7 GB at Q8_0. No amount of processing power compensates for a card that cannot hold it.
02

Set the context length you will work at

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context AWD-LSTM-DOC (fin) (37M) can slip off a card that handles short questions easily.
03

Decide how much compression you will accept

Each card runs the least-compressed copy it can hold — Q8_0 on the smallest card that fits. Setting a floor drops the cards that only manage AWD-LSTM-DOC (fin) (37M) by squeezing it further than you would want.
04

Rank by throughput rather than spec sheet

The speed ordering for AWD-LSTM-DOC (fin) (37M) is effectively an ordering by memory bandwidth, which is why the B200 tops it at 91,574 tok/s.
05

Read the fit column last

Tight means AWD-LSTM-DOC (fin) (37M) loads and works, with no room to raise the context later. Comfortable means you can. The difference matters more than a few tokens per second.
06

See what else that card runs

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond AWD-LSTM-DOC (fin) (37M).

Answers

AWD-LSTM-DOC (fin) (37M) — common questions

Where can I download AWD-LSTM-DOC (fin) (37M)?

The weights for AWD-LSTM-DOC (fin) (37M) are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

Can I run AWD-LSTM-DOC (fin) (37M) if it does not fit in my GPU?

Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded AWD-LSTM-DOC (fin) (37M) is rarely worth using. Every figure here assumes the whole model is on the card.

Would two GPUs run AWD-LSTM-DOC (fin) (37M) faster?

Two cards buy memory rather than speed. That matters for AWD-LSTM-DOC (fin) (37M) only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for AWD-LSTM-DOC (fin) (37M)?

Because capacity varies, so does how hard AWD-LSTM-DOC (fin) (37M) has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these AWD-LSTM-DOC (fin) (37M) speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 54,944–146,518 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 AWD-LSTM-DOC (fin) (37M)?

The smallest card in our catalogue that holds AWD-LSTM-DOC (fin) (37M) is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 0.7 GB, and produces roughly 996 tokens per second. 818 cards in total can run it.

How fast is AWD-LSTM-DOC (fin) (37M) on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 91,574 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 AWD-LSTM-DOC (fin) (37M) clear that.

How much VRAM does AWD-LSTM-DOC (fin) (37M) need?

About 0.7 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 AWD-LSTM-DOC (fin) (37M) on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 0.7 GB and generating roughly 17,056 tokens per second — a comfortable fit.

Can I run AWD-LSTM-DOC (fin) (37M) on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 0.7 GB and generating roughly 10,444 tokens per second — a comfortable fit.

Can I run AWD-LSTM-DOC (fin) (37M) on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 0.7 GB and generating roughly 12,935 tokens per second — a comfortable fit.

Can I run AWD-LSTM-DOC (fin) (37M) on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 0.7 GB and generating roughly 15,339 tokens per second — a comfortable fit.

Is AWD-LSTM-DOC (fin) (37M) open source?

Its weights are published, so AWD-LSTM-DOC (fin) (37M) 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 AWD-LSTM-DOC (fin) (37M) have?

AWD-LSTM-DOC (fin) (37M) has 37M 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 AWD-LSTM-DOC (fin) (37M)?

AWD-LSTM-DOC (fin) (37M) was published by NTT Communication Science Laboratories,Tohoku University, based in Japan, categorised as industry,Academia.

When was AWD-LSTM-DOC (fin) (37M) released?

AWD-LSTM-DOC (fin) (37M) was published in August 2018. 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 AWD-LSTM-DOC (fin) (37M) used for?

AWD-LSTM-DOC (fin) (37M) works in Language, and is recorded as handling language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Source

Original publication

Record last updated 11 February 2026

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