AWD-LSTM + MoS + Partial Shuffled TPS calculator

Open weights University of Texas at Austin 35M parameters June 2019

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 · 1,053 tok/s

Fastest card

B200

96,807 tok/s · 180 GB

Which GPUs can run AWD-LSTM + MoS + Partial Shuffled?

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.

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Card memory

Also show cards that cannot run it

818 cards match

Calculating

					Needs	Quantisation	Fit
96,807 tok/s 58,084–154,891 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	0.7 GB	Q8_0	Comfortable
96,807 tok/s 58,084–154,891 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	0.7 GB	Q8_0	Comfortable
77,303 tok/s 46,382–123,684 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	0.7 GB	Q8_0	Comfortable
77,303 tok/s 46,382–123,684 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	0.7 GB	Q8_0	Comfortable
61,823 tok/s 37,094–98,917 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	0.7 GB	Q8_0	Comfortable
59,173 tok/s 35,504–94,677 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.7 GB	Q8_0	Comfortable
59,173 tok/s 35,504–94,677 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.7 GB	Q8_0	Comfortable
56,632 tok/s 33,979–90,611 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	0.7 GB	Q8_0	Comfortable
50,261 tok/s 30,157–80,417 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	0.7 GB	Q8_0	Comfortable
50,261 tok/s 30,157–80,417 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	0.7 GB	Q8_0	Comfortable
50,261 tok/s 30,157–80,417 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	0.7 GB	Q8_0	Comfortable
47,677 tok/s 28,606–76,284 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
40,659 tok/s 24,395–65,054 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
40,659 tok/s 24,395–65,054 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	0.7 GB	Q8_0	Comfortable
40,659 tok/s 24,395–65,054 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
40,659 tok/s 24,395–65,054 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
40,659 tok/s 24,395–65,054 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
30,959 tok/s 18,575–49,534 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	0.7 GB	Q8_0	Comfortable
30,959 tok/s 18,575–49,534 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	0.7 GB	Q8_0	Comfortable
25,799 tok/s 15,479–41,278 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	0.7 GB	Q8_0	Comfortable
25,248 tok/s 15,149–40,397 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	0.7 GB	Q8_0	Comfortable
24,686 tok/s 14,811–39,497 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	0.7 GB	Q8_0	Comfortable
24,686 tok/s 14,811–39,497 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	0.7 GB	Q8_0	Comfortable
24,686 tok/s 14,811–39,497 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	0.7 GB	Q8_0	Comfortable
24,686 tok/s 14,811–39,497 · 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: University of Texas at Austin
Organisation type: Academia
Country: United States of America
Published: 10 June 2019
Authors: Dilin Wang, Chengyue Gong, Qiang Liu

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: 35M
Training data: 2,000,000 tokens
Epochs: 750

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: 3.2 × 10¹⁷ 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 (non-commercial)
Training code: Open (non-commercial)

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: 126
Benchmark data: AWD-LSTM + MoS + Partial Shuffled

Sources

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

Reference: Improving Neural Language Modeling via Adversarial Training
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run AWD-LSTM + MoS + Partial Shuffled

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 + MoS + Partial Shuffled

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

0.7 GB

Fastest

96,807 tok/s

AWD-LSTM + MoS + Partial Shuffled is small enough at 35M 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 1,053 tokens per second.

The quickest result comes from a B200 at around 96,807 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

What this model is

AWD-LSTM + MoS + Partial Shuffled was published by University of Texas at Austin, in United States of America, in June 2019. It comes out of academia.

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

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.

What decides the speed

Across every card that can run it, the middle of the range is about 2,718.3 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.

Its internal architecture is not on file, so memory is approximated from the parameter count and marked accordingly. Expect the real figure to differ, more so at long context.

How it was trained

The training run consumed about 3.2 × 10¹⁷ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

Around 2,000,000 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 AWD-LSTM + MoS + Partial Shuffled

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

01

Check what it needs before anything else

The table lists every card that can hold AWD-LSTM + MoS + Partial Shuffled — around 0.7 GB at Q8_0. That figure, not the card's headline performance, is what decides whether it runs.
02

Decide how long your conversations run

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for AWD-LSTM + MoS + Partial Shuffled.
03

Decide how much compression you will accept

Compression is what makes AWD-LSTM + MoS + Partial Shuffled 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 AWD-LSTM + MoS + Partial Shuffled. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 96,807 tok/s.
05

Read the fit column last

Tight means AWD-LSTM + MoS + Partial Shuffled 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

Following a card through to its own page shows every other model it can hold, which is the question that follows once AWD-LSTM + MoS + Partial Shuffled is settled.

Answers

AWD-LSTM + MoS + Partial Shuffled — common questions

When was AWD-LSTM + MoS + Partial Shuffled released?

AWD-LSTM + MoS + Partial Shuffled was published in June 2019. 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 + MoS + Partial Shuffled used for?

AWD-LSTM + MoS + Partial Shuffled 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.

Where can I download AWD-LSTM + MoS + Partial Shuffled?

The weights for AWD-LSTM + MoS + Partial Shuffled 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 AWD-LSTM + MoS + Partial Shuffled?

Around 3.2 × 10¹⁷ 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 AWD-LSTM + MoS + Partial Shuffled if it does not fit in my GPU?

It can be split between the card and system memory, but AWD-LSTM + MoS + Partial Shuffled generates painfully slowly that way. Nothing on this page assumes offloading.

Would two GPUs run AWD-LSTM + MoS + Partial Shuffled faster?

Capacity adds across cards; throughput does not. Since 818 of the cards we track already hold AWD-LSTM + MoS + Partial Shuffled on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for AWD-LSTM + MoS + Partial Shuffled?

A larger card holds a more accurate copy. Across the cards that run AWD-LSTM + MoS + Partial Shuffled, 1 compression levels are used; the floor control above pins it to one.

How accurate are these AWD-LSTM + MoS + Partial Shuffled speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked. That is why each is published as a range such as 58,084–154,891 tok/s on the B200 rather than a single number.

What GPU do I need to run AWD-LSTM + MoS + Partial Shuffled?

The smallest card in our catalogue that holds AWD-LSTM + MoS + Partial Shuffled is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 0.7 GB, and produces roughly 1,053 tokens per second. 818 cards in total can run it.

How fast is AWD-LSTM + MoS + Partial Shuffled on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 96,807 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 + MoS + Partial Shuffled clear that.

How much VRAM does AWD-LSTM + MoS + Partial Shuffled 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 + MoS + Partial Shuffled 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 18,030 tokens per second — a comfortable fit.

Can I run AWD-LSTM + MoS + Partial Shuffled 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 11,041 tokens per second — a comfortable fit.

Can I run AWD-LSTM + MoS + Partial Shuffled 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 13,674 tokens per second — a comfortable fit.

Can I run AWD-LSTM + MoS + Partial Shuffled 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 16,215 tokens per second — a comfortable fit.

Is AWD-LSTM + MoS + Partial Shuffled open source?

Its weights are published, so AWD-LSTM + MoS + Partial Shuffled 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 + MoS + Partial Shuffled have?

AWD-LSTM + MoS + Partial Shuffled has 35M parameters. 35M (Table 2). 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 + MoS + Partial Shuffled?

AWD-LSTM + MoS + Partial Shuffled was published by University of Texas at Austin, based in United States of America, categorised as academia.

Source

Original publication

Record last updated 25 May 2026

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