ConoDL TPS calculator

Open weights Chongqing University,Ministry of Natural Resources (China) 1.2B parameters September 2024

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 · 30.7 tok/s

Fastest card

B200

2,824 tok/s · 180 GB

Which GPUs can run ConoDL?

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
2,824 tok/s 1,694–4,518 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	2.0 GB	Q8_0	Comfortable
2,824 tok/s 1,694–4,518 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	2.0 GB	Q8_0	Comfortable
2,255 tok/s 1,353–3,607 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	2.0 GB	Q8_0	Comfortable
2,255 tok/s 1,353–3,607 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	2.0 GB	Q8_0	Comfortable
1,803 tok/s 1,082–2,885 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	2.0 GB	Q8_0	Comfortable
1,726 tok/s 1,036–2,761 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.0 GB	Q8_0	Comfortable
1,726 tok/s 1,036–2,761 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.0 GB	Q8_0	Comfortable
1,652 tok/s 991–2,643 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	2.0 GB	Q8_0	Comfortable
1,466 tok/s 880–2,346 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	2.0 GB	Q8_0	Comfortable
1,466 tok/s 880–2,346 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	2.0 GB	Q8_0	Comfortable
1,466 tok/s 880–2,346 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	2.0 GB	Q8_0	Comfortable
1,391 tok/s 834–2,225 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	2.0 GB	Q8_0	Comfortable
1,186 tok/s 712–1,897 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.0 GB	Q8_0	Comfortable
1,186 tok/s 712–1,897 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	2.0 GB	Q8_0	Comfortable
1,186 tok/s 712–1,897 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	2.0 GB	Q8_0	Comfortable
1,186 tok/s 712–1,897 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.0 GB	Q8_0	Comfortable
1,186 tok/s 712–1,897 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	2.0 GB	Q8_0	Comfortable
903 tok/s 542–1,445 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	2.0 GB	Q8_0	Comfortable
903 tok/s 542–1,445 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	2.0 GB	Q8_0	Comfortable
752 tok/s 451–1,204 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	2.0 GB	Q8_0	Comfortable
736 tok/s 442–1,178 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	2.0 GB	Q8_0	Comfortable
720 tok/s 432–1,152 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	2.0 GB	Q8_0	Comfortable
720 tok/s 432–1,152 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	2.0 GB	Q8_0	Comfortable
720 tok/s 432–1,152 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	2.0 GB	Q8_0	Comfortable
720 tok/s 432–1,152 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	2.0 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: Chongqing University,Ministry of Natural Resources (China)
Organisation type: Academia,Government
Country: China
Published: 28 September 2024
Authors: Menghan Guo, Zengpeng Li, Xuejin Deng, Ding Luo, Jingyi Yang, Yingjun Chen, Weiwei Xue

What it does

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

Domain: Biology
Task: Toxin prediction
Base model: ProGen

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: 1.2B
Training data: 108,651 tokens
Epochs: 15

The training run

What it physically took to train: which chips, how many, for how long, and what that drew from the wall.

Training hardware: NVIDIA GeForce RTX 3080
Chips used: 1
Power draw: 346 W

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.

Record confidence: Confident

Sources

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

Reference: ConoDL: A Deep Learning Framework for Rapid Generation and Prediction of Conotoxins
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run ConoDL

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 ConoDL

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

Hardware requirements in practice

Minimum card

Tesla C1080

Memory needed

2.0 GB

Fastest

2,824 tok/s

ConoDL is small enough at 1.2B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The entry point is the Tesla C1080: 4 GB of memory, Q8_0 compression, roughly 30.7 tokens per second.

A B200 is the fastest we calculate for it: about 2,824 tokens per second, from 8,000 GB/s of memory bandwidth.

Background

ConoDL was published by Chongqing University,Ministry of Natural Resources (China), in China, in September 2024. The organisation is categorised as academia,Government.

It works in Biology, and is recorded as doing toxin prediction.

It builds on ProGen, which is why it shares that model's general shape and size.

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

Across every card that can run it, the middle of the range is about 79.3 tokens per second, and 799 of them clear the ten tokens per second that roughly matches reading speed.

It is a dense model, so every parameter is read for every token produced. That makes speed track memory bandwidth almost exactly — a card with twice the bandwidth generates roughly twice as fast.

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.

What went into building it

The training set ran to roughly 108,651 tokens.

Step by step

How to choose a GPU for ConoDL

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

01

Read the memory figure first

The table lists every card that can hold ConoDL — around 2.0 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

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason ConoDL stops fitting a card that seemed fine.
03

Decide how much compression you will accept

Compression is what makes ConoDL 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

Sort by speed

Sort by speed to see how cards rank for ConoDL. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 2,824 tok/s.
05

Read the fit column last

The fit column separates cards that just manage ConoDL from those with room to spare. Buy for the second if the context might grow.
06

Open the card you have settled on

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

Answers

ConoDL — common questions

How accurate are these ConoDL speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 1,694–4,518 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 ConoDL?

The smallest card in our catalogue that holds ConoDL is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 2.0 GB, and produces roughly 30.7 tokens per second. 818 cards in total can run it.

How fast is ConoDL on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 2,824 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 799 of the cards that can run ConoDL clear that.

How much VRAM does ConoDL need?

About 2.0 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 ConoDL on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 2.0 GB and generating roughly 526 tokens per second — a comfortable fit.

Can I run ConoDL on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 2.0 GB and generating roughly 322 tokens per second — a comfortable fit.

Can I run ConoDL on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 2.0 GB and generating roughly 399 tokens per second — a comfortable fit.

Can I run ConoDL on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 2.0 GB and generating roughly 473 tokens per second — a comfortable fit.

Is ConoDL open source?

Its weights are published, so ConoDL 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 ConoDL have?

ConoDL has 1.2B parameters. "ConoGen is a conotoxin generation model that adopts an architecture consistent with the pre-training model ProGen (Figure 2A) 15 . ConoGen is also constructed using a Transformer-based neural network architecture. One major advantage of the Transformer lies in its self-attention mechanism, which enables the model to encode distant signals of information when making sequence predictions, thereby affording it the capability to comprehend complex semantics. Its Transformer architecture consists of 36 layers, each comprising 8 self-attention heads, totaling 1.2 billion trainable parameters to ensure the training and optimization of the model.". 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 ConoDL?

ConoDL was published by Chongqing University,Ministry of Natural Resources (China), based in China, categorised as academia,Government.

When was ConoDL released?

ConoDL was published in September 2024. 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 ConoDL used for?

ConoDL works in Biology, and is recorded as handling toxin prediction. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.

Where can I download ConoDL?

The weights for ConoDL 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 ConoDL 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 ConoDL is rarely worth using. Every figure here assumes the whole model is on the card.

Would two GPUs run ConoDL faster?

A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run ConoDL alone, the case for pairing is weak.

Why does the quantisation differ between cards for ConoDL?

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

Source

Original publication

Record last updated 28 November 2025

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