AlexNet + coordinating filters TPS calculator

Open weights University of Pittsburgh,Duke University 60M parameters March 2017

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 · 614 tok/s

Fastest card

B200

56,471 tok/s · 180 GB

Which GPUs can run AlexNet + coordinating filters?

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
56,471 tok/s 33,882–90,353 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	0.8 GB	Q8_0	Comfortable
56,471 tok/s 33,882–90,353 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	0.8 GB	Q8_0	Comfortable
45,093 tok/s 27,056–72,149 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	0.8 GB	Q8_0	Comfortable
45,093 tok/s 27,056–72,149 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	0.8 GB	Q8_0	Comfortable
36,064 tok/s 21,638–57,702 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
34,518 tok/s 20,711–55,228 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.8 GB	Q8_0	Comfortable
34,518 tok/s 20,711–55,228 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.8 GB	Q8_0	Comfortable
33,035 tok/s 19,821–52,856 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	0.8 GB	Q8_0	Comfortable
29,319 tok/s 17,591–46,910 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
29,319 tok/s 17,591–46,910 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
29,319 tok/s 17,591–46,910 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
27,812 tok/s 16,687–44,499 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
23,718 tok/s 14,231–37,948 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
23,718 tok/s 14,231–37,948 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	0.8 GB	Q8_0	Comfortable
23,718 tok/s 14,231–37,948 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
23,718 tok/s 14,231–37,948 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
23,718 tok/s 14,231–37,948 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
18,059 tok/s 10,836–28,895 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	0.8 GB	Q8_0	Comfortable
18,059 tok/s 10,836–28,895 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	0.8 GB	Q8_0	Comfortable
15,049 tok/s 9,030–24,079 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
14,728 tok/s 8,837–23,565 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
14,400 tok/s 8,640–23,040 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	0.8 GB	Q8_0	Comfortable
14,400 tok/s 8,640–23,040 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	0.8 GB	Q8_0	Comfortable
14,400 tok/s 8,640–23,040 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	0.8 GB	Q8_0	Comfortable
14,400 tok/s 8,640–23,040 · 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 Pittsburgh,Duke University
Organisation type: Academia,Academia
Country: United States of America
Published: 28 March 2017
Authors: Wei Wen, Cong Xu, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li

What it does

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

Domain: Vision
Task: Image classification

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: 60M
Training data: tokens

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: 4.7 × 10¹⁷ FLOP
How it was established: Comparison with other models

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: 172

Sources

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

Reference: Coordinating Filters for Faster Deep Neural Networks
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run AlexNet + coordinating filters

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 AlexNet + coordinating filters

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 it takes to run this model

Minimum card

Tesla C1080

Memory needed

0.8 GB

Fastest

56,471 tok/s

AlexNet + coordinating filters is small enough at 60M parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The smallest card that holds it is the Tesla C1080 with 4 GB, running it at Q8_0 and producing around 614 tokens per second.

At the other end, a B200 generates roughly 56,471 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

What this model is

AlexNet + coordinating filters was published by University of Pittsburgh,Duke University, in United States of America, in March 2017. academia,Academia is the category the publisher falls under.

It works in Vision, and is recorded as doing image classification.

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 1,585.7 tokens per second; 818 cards produce text faster than most people read it.

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.

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.

How it was trained

The training run consumed about 4.7 × 10¹⁷ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

Step by step

How to choose a GPU for AlexNet + coordinating filters

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

The table lists every card that can hold AlexNet + coordinating filters — around 0.8 GB at Q8_0. That figure, not the card's headline performance, is what decides whether it runs.
02

Match the context to your actual use

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for AlexNet + coordinating filters.
03

Choose how far you will compress it

The quantisation column varies by card, because a bigger card holds a more accurate copy of AlexNet + coordinating filters — Q8_0 on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Compare tokens per second, not specifications

Sort by speed to see how cards rank for AlexNet + coordinating filters. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 56,471 tok/s.
05

Look at the headroom, not just the fit

Tight means AlexNet + coordinating filters 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

Check the card from the other side

Following a card through to its own page shows every other model it can hold, which is the question that follows once AlexNet + coordinating filters is settled.

Answers

AlexNet + coordinating filters — common questions

Can I run AlexNet + coordinating filters 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 AlexNet + coordinating filters is rarely worth using. Every figure here assumes the whole model is on the card.

Would two GPUs run AlexNet + coordinating filters faster?

Capacity adds across cards; throughput does not. Since 818 of the cards we track already hold AlexNet + coordinating filters on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for AlexNet + coordinating filters?

A larger card holds a more accurate copy. Across the cards that run AlexNet + coordinating filters, 1 compression levels are used; the floor control above pins it to one.

How accurate are these AlexNet + coordinating filters speed estimates?

These are estimates with real error bars. The fastest result here, 33,882–90,353 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

What GPU do I need to run AlexNet + coordinating filters?

The smallest card in our catalogue that holds AlexNet + coordinating filters is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 0.8 GB, and produces roughly 614 tokens per second. 818 cards in total can run it.

How fast is AlexNet + coordinating filters on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 56,471 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 AlexNet + coordinating filters clear that.

How much VRAM does AlexNet + coordinating filters need?

About 0.8 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 AlexNet + coordinating filters on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 0.8 GB and generating roughly 10,518 tokens per second — a comfortable fit.

Can I run AlexNet + coordinating filters on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 0.8 GB and generating roughly 6,440 tokens per second — a comfortable fit.

Can I run AlexNet + coordinating filters on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 0.8 GB and generating roughly 7,976 tokens per second — a comfortable fit.

Can I run AlexNet + coordinating filters on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 0.8 GB and generating roughly 9,459 tokens per second — a comfortable fit.

Is AlexNet + coordinating filters open source?

Its weights are published, so AlexNet + coordinating filters 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 AlexNet + coordinating filters have?

AlexNet + coordinating filters has 60M parameters. The paper reimplements ResNet-20, AlexNet and GoogLeNet. The largest from these models is AlexNet. (data for these models taken from this db) AlexNet has 60000000.00 params. GoogLeNet has 6797700.00 params. So max(60000000, 6797700) = 60000000 source "The effectiveness of our approach is comprehensively evaluated in ResNets, AlexNet, and GoogLeNet. In AlexNet, for example, Force Regularization gains 2x speedup on modern GPU without accuracy loss and 4.05x speedup on CPU by paying small accuracy degradation. ". 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 AlexNet + coordinating filters?

AlexNet + coordinating filters was published by University of Pittsburgh,Duke University, based in United States of America, categorised as academia,Academia.

When was AlexNet + coordinating filters released?

AlexNet + coordinating filters was published in March 2017. 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 AlexNet + coordinating filters used for?

AlexNet + coordinating filters works in Vision, and is recorded as handling image classification. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download AlexNet + coordinating filters?

The weights for AlexNet + coordinating filters 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 AlexNet + coordinating filters?

Around 4.7 × 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.

Source

Original publication

Record last updated 28 November 2025

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