Tensor-Transformer(1core)+PN (WT103) TPS calculator

Open weights University of California (UC) Berkeley 85.3M parameters March 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 · 432 tok/s

Fastest card

B200

39,721 tok/s · 180 GB

Which GPUs can run Tensor-Transformer(1core)+PN (WT103)?

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
39,721 tok/s 23,833–63,554 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	0.8 GB	Q8_0	Comfortable
39,721 tok/s 23,833–63,554 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	0.8 GB	Q8_0	Comfortable
31,719 tok/s 19,031–50,750 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	0.8 GB	Q8_0	Comfortable
31,719 tok/s 19,031–50,750 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	0.8 GB	Q8_0	Comfortable
25,367 tok/s 15,220–40,587 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
24,280 tok/s 14,568–38,848 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.8 GB	Q8_0	Comfortable
24,280 tok/s 14,568–38,848 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.8 GB	Q8_0	Comfortable
23,237 tok/s 13,942–37,179 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	0.8 GB	Q8_0	Comfortable
20,623 tok/s 12,374–32,997 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
20,623 tok/s 12,374–32,997 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
20,623 tok/s 12,374–32,997 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
19,563 tok/s 11,738–31,300 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
16,683 tok/s 10,010–26,693 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
16,683 tok/s 10,010–26,693 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	0.8 GB	Q8_0	Comfortable
16,683 tok/s 10,010–26,693 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
16,683 tok/s 10,010–26,693 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
16,683 tok/s 10,010–26,693 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
12,703 tok/s 7,622–20,325 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	0.8 GB	Q8_0	Comfortable
12,703 tok/s 7,622–20,325 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	0.8 GB	Q8_0	Comfortable
10,586 tok/s 6,351–16,937 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
10,360 tok/s 6,216–16,576 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
10,129 tok/s 6,077–16,206 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	0.8 GB	Q8_0	Comfortable
10,129 tok/s 6,077–16,206 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	0.8 GB	Q8_0	Comfortable
10,129 tok/s 6,077–16,206 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	0.8 GB	Q8_0	Comfortable
10,129 tok/s 6,077–16,206 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	0.8 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 California (UC) Berkeley
Organisation type: Academia
Country: United States of America
Published: 17 March 2020
Authors: Sheng Shen, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer

What it does

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

Domain: Language
Task: Language modeling
Numerical format: FP32

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: 85.3M
Training data: 103,000,000 tokens
Epochs: 30

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: 1.6 × 10¹⁸ 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: Open source

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: 60
Benchmark data: Tensor-Transformer(1core)+PN (WT103)

Sources

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

Reference: PowerNorm: Rethinking Batch Normalization in Transformers
Last updated: 11 February 2026

The extremes

The ten fastest GPUs that run Tensor-Transformer(1core)+PN (WT103)

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 Tensor-Transformer(1core)+PN (WT103)

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

0.8 GB

Fastest

39,721 tok/s

Tensor-Transformer(1core)+PN (WT103) reaches a parameter count of 85.3M. That is small enough that hardware is rarely the obstacle, including on cards several years old. The number of cards we track that can run it: 818.

At the low end it is handled by Tesla C1080, with a memory capacity of 4 GB, running it at a compression of Q8_0 and producing around 432 tokens per second.

At the other end sits B200, generating roughly 39,721 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

What this model is

Tensor-Transformer(1core)+PN (WT103) was published by University of California (UC) Berkeley, in the country recorded as United States of America, during March 2020. It comes out of an organisation categorised as academia.

It works in the domain of Language, and is recorded as performing the task of 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

The median result is around 1,115.4 tokens per second. Clearing the ten tokens per second that roughly matches reading speed: 818 of them.

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

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.

Training and provenance

Producing it required arithmetic totalling around 1.6 × 10¹⁸ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.

The training set ran to roughly 103,000,000 tokens of text.

Its inclusion criterion: sOTA improvement.

Step by step

How to choose a GPU for Tensor-Transformer(1core)+PN (WT103)

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 Tensor-Transformer(1core)+PN (WT103), needing around 0.8 GB at a compression of Q8_0. That figure, not the headline performance of a card, is what decides whether it runs.
02

Match the context to your actual use

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason a card that seemed fine stops fitting Tensor-Transformer(1core)+PN (WT103).
03

Choose how far you will compress it

Compression is what makes a model fit smaller cards, at some cost in accuracy, reaching a compression of Q8_0 on the smallest card that fits. Setting a minimum quality drops the cards that only manage it by squeezing further than you would want, and holds the comparison at one level.
04

Compare tokens per second, not specifications

Sort by speed to see how cards rank for Tensor-Transformer(1core)+PN (WT103). It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 39,721 tok/s.
05

Read the fit column last

The fit column separates cards that just manage it from those with room to spare, in the case of Tensor-Transformer(1core)+PN (WT103). Comfortable means you can grow the context later. That difference matters more than a few tokens per second, so buy for comfortable if you expect to.
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. A card is usually bought for more than one model, so it is worth a look before buying for Tensor-Transformer(1core)+PN (WT103).

Answers

Tensor-Transformer(1core)+PN (WT103) — common questions

Tensor-Transformer(1core)+PN (WT103)— where can I download it?

The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

Tensor-Transformer(1core)+PN (WT103)— how much compute was used to train it?

Training consumed around 1.6 × 10¹⁸ 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.

Tensor-Transformer(1core)+PN (WT103)— can I run it if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part held in system memory drags the whole thing down. Every figure here assumes the whole model is resident on the card.

Tensor-Transformer(1core)+PN (WT103)— would two GPUs run it faster?

Two cards buy memory rather than speed, which matters only if one card cannot hold it. The number that can: 818. So a second card is rarely the answer here.

Tensor-Transformer(1core)+PN (WT103)— why does the quantisation differ between cards?

A larger card holds a more accurate copy. The number of compression levels used across the cards that run it: 1. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

Tensor-Transformer(1core)+PN (WT103)— how accurate are these speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked, which is why each is published as a range rather than a single number. One example: 23,833–63,554 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Tensor-Transformer(1core)+PN (WT103)— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Tesla C1080, with a memory capacity of 4 GB. It runs the model at a compression of Q8_0 using about 0.8 GB, and produces roughly 432 tokens per second. The number of cards able to run it in total: 818.

Tensor-Transformer(1core)+PN (WT103)— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 39,721 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and the number of cards clearing that: 818.

Tensor-Transformer(1core)+PN (WT103)— how much VRAM does it need?

It needs about 0.8 GB at a compression of Q8_0, 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.

Tensor-Transformer(1core)+PN (WT103)— can I run it on a GPU holding 8 GB?

Yes. The card CMP 170HX 8 GB, holding 8 GB, runs it at a compression of Q8_0, using about 0.8 GB and generating roughly 7,398 tokens per second. The fit is comfortable.

Tensor-Transformer(1core)+PN (WT103)— can I run it on a GPU holding 12 GB?

Yes. The card GeForce RTX 3080 Ti, holding 12 GB, runs it at a compression of Q8_0, using about 0.8 GB and generating roughly 4,530 tokens per second. The fit is comfortable.

Tensor-Transformer(1core)+PN (WT103)— can I run it on a GPU holding 16 GB?

Yes. The card Tesla V100 SXM2 16 GB, holding 16 GB, runs it at a compression of Q8_0, using about 0.8 GB and generating roughly 5,611 tokens per second. The fit is comfortable.

Tensor-Transformer(1core)+PN (WT103)— can I run it on a GPU holding 24 GB?

Yes. The card GeForce RTX 5090 D V2, holding 24 GB, runs it at a compression of Q8_0, using about 0.8 GB and generating roughly 6,653 tokens per second. The fit is comfortable.

Tensor-Transformer(1core)+PN (WT103)— is it open source?

Its weights are published, so it 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.

Tensor-Transformer(1core)+PN (WT103)— how many parameters does it have?

It has a parameter count of 85.3M. six layers tensorized transformer core-1 for Wikitext-103, following (Ma et al., 2019). https://arxiv.org/abs/1906.09777. 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.

Tensor-Transformer(1core)+PN (WT103)— who created it?

It was published by University of California (UC) Berkeley, based in United States of America, an organisation categorised as academia.

Tensor-Transformer(1core)+PN (WT103)— when was it released?

It was published in March 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.

Tensor-Transformer(1core)+PN (WT103)— what is it used for?

It works in the domain of Language, and is recorded as handling the task of 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

The other direction

Looking at it from the other side?

This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.

All AI models