TrellisNet-MoS (1.4x larger) PTB TPS calculator
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 we hold specifications for
Smallest card that fits
Tesla C1080
4 GB · Q8_0 · 1,084 tok/s
Fastest card
B200
99,654 tok/s · 180 GB
Which GPUs can run TrellisNet-MoS (1.4x larger) PTB?
Set the inputs, read the answer
A longer conversation needs more memory, which can push this model off smaller cards.
Hides cards that would only fit the model by compressing it below this point.
818 cards match
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
99,654
tok/s
59,792–159,446 · low confidence |
B200 NVIDIA | 180 GB | 8,000 GB/s | Jan 2024 | 0.7 GB | Q8_0 | Comfortable |
|
99,654
tok/s
59,792–159,446 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 0.7 GB | Q8_0 | Comfortable |
|
79,576
tok/s
47,746–127,322 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 0.7 GB | Q8_0 | Comfortable |
|
79,576
tok/s
47,746–127,322 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 0.7 GB | Q8_0 | Comfortable |
|
63,642
tok/s
38,185–101,826 · low confidence |
Radeon Instinct MI300 AMD | 128 GB | 6,550 GB/s | Jan 2023 | 0.7 GB | Q8_0 | Comfortable |
|
60,913
tok/s
36,548–97,462 · low confidence |
H200 NVL NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 0.7 GB | Q8_0 | Comfortable |
|
60,913
tok/s
36,548–97,462 · low confidence |
H200 SXM 141 GB NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 0.7 GB | Q8_0 | Comfortable |
|
58,298
tok/s
34,979–93,276 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 0.7 GB | Q8_0 | Comfortable |
|
51,739
tok/s
31,043–82,783 · low confidence |
Radeon Instinct MI300A AMD | 128 GB | 5,325 GB/s | Dec 2023 | 0.7 GB | Q8_0 | Comfortable |
|
51,739
tok/s
31,043–82,783 · low confidence |
Radeon Instinct MI300X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 0.7 GB | Q8_0 | Comfortable |
|
51,739
tok/s
31,043–82,783 · low confidence |
Radeon Instinct MI308X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 0.7 GB | Q8_0 | Comfortable |
|
49,080
tok/s
29,448–78,527 · low confidence |
H100 NVL 94 GB NVIDIA | 94 GB | 3,940 GB/s | Mar 2023 | 0.7 GB | Q8_0 | Comfortable |
|
41,855
tok/s
25,113–66,967 · low confidence |
H100 PCIe 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 0.7 GB | Q8_0 | Comfortable |
|
41,855
tok/s
25,113–66,967 · low confidence |
H100 SXM5 80 GB NVIDIA | 80 GB | 3,360 GB/s | Oct 2022 | 0.7 GB | Q8_0 | Comfortable |
|
41,855
tok/s
25,113–66,967 · low confidence |
H100 SXM5 94 GB NVIDIA | 94 GB | 3,360 GB/s | Mar 2023 | 0.7 GB | Q8_0 | Comfortable |
|
41,855
tok/s
25,113–66,967 · low confidence |
H100 SXM5 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 0.7 GB | Q8_0 | Comfortable |
|
41,855
tok/s
25,113–66,967 · low confidence |
H800 SXM5 NVIDIA | 80 GB | 3,360 GB/s | Mar 2023 | 0.7 GB | Q8_0 | Comfortable |
|
31,869
tok/s
19,122–50,991 · low confidence |
Radeon Instinct MI250 AMD | 128 GB | 3,280 GB/s | Nov 2021 | 0.7 GB | Q8_0 | Comfortable |
|
31,869
tok/s
19,122–50,991 · low confidence |
Radeon Instinct MI250X AMD | 128 GB | 3,280 GB/s | Nov 2021 | 0.7 GB | Q8_0 | Comfortable |
|
26,558
tok/s
15,935–42,492 · low confidence |
Data Center GPU Max 1550 Intel | 128 GB | 3,280 GB/s | Jan 2023 | 0.7 GB | Q8_0 | Comfortable |
|
25,991
tok/s
15,595–41,586 · low confidence |
Data Center GPU Max Subsystem Intel | 128 GB | 3,210 GB/s | Jan 2023 | 0.7 GB | Q8_0 | Comfortable |
|
25,412
tok/s
15,247–40,659 · low confidence |
A100 SXM4 80 GB NVIDIA | 80 GB | 2,040 GB/s | Nov 2020 | 0.7 GB | Q8_0 | Comfortable |
|
25,412
tok/s
15,247–40,659 · low confidence |
A100X NVIDIA | 80 GB | 2,040 GB/s | Jun 2021 | 0.7 GB | Q8_0 | Comfortable |
|
25,412
tok/s
15,247–40,659 · low confidence |
A800 SXM4 80 GB NVIDIA | 80 GB | 2,040 GB/s | Aug 2022 | 0.7 GB | Q8_0 | Comfortable |
|
25,412
tok/s
15,247–40,659 · 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
- Carnegie Mellon University (CMU),Intel Labs,Bosch Center for Artificial Intelligence
- Organisation type
- Academia,Industry,Industry
- Country
- United States of America, Germany
- Published
- 15 October 2018
- Authors
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
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
- 34M
- Training data
- tokens
34M, table 1
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
MIT license for code. Weights available for PTB: https://github.com/locuslab/trellisnet/blob/master/README.md
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
- 164
- Benchmark data
- TrellisNet-MoS (1.4x larger)
Sources
Where this record came from and when it was last checked.
- Reference
- Trellis Networks for Sequence Modeling
- Last updated
- 25 May 2026
The extremes
The ten fastest GPUs that run TrellisNet-MoS (1.4x larger) PTB
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.
- 01 B300 288 GB · 8,000 GB/s · Q8_0 99,654 tok/s
- 02 B200 180 GB · 8,000 GB/s · Q8_0 99,654 tok/s
- 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q8_0 79,576 tok/s
- 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q8_0 79,576 tok/s
- 05 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q8_0 63,642 tok/s
- 06 H200 NVL 141 GB · 4,890 GB/s · Q8_0 60,913 tok/s
- 07 H200 SXM 141 GB 141 GB · 4,890 GB/s · Q8_0 60,913 tok/s
- 08 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q8_0 58,298 tok/s
- 09 Radeon Instinct MI300A 128 GB · 5,325 GB/s · Q8_0 51,739 tok/s
- 10 Radeon Instinct MI300X 192 GB · 5,325 GB/s · Q8_0 51,739 tok/s
The smallest GPUs that still run TrellisNet-MoS (1.4x larger) PTB
The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.
- 01 GeForce RTX 4010 4 GB · needs 0.7 GB · Q8_0 · comfortable 1,196 tok/s
- 02 RTX A400 4 GB · needs 0.7 GB · Q8_0 · comfortable 1,196 tok/s
- 03 RTX 500 Mobile Ada Generation 4 GB · needs 0.7 GB · Q8_0 · comfortable 1,594 tok/s
- 04 GeForce RTX 3050 A Mobile 4 GB · needs 0.7 GB · Q8_0 · comfortable 2,392 tok/s
- 05 Jetson Orin Nano 4 GB 4 GB · needs 0.7 GB · Q8_0 · comfortable 425 tok/s
- 06 Radeon RX 6450M 4 GB · needs 0.7 GB · Q8_0 · comfortable 1,244 tok/s
- 07 Radeon RX 6550M 4 GB · needs 0.7 GB · Q8_0 · comfortable 1,399 tok/s
- 08 Radeon RX 6550S 4 GB · needs 0.7 GB · Q8_0 · comfortable 1,244 tok/s
- 09 Arc A310 4 GB · needs 0.7 GB · Q8_0 · comfortable 1,004 tok/s
- 10 Arc Pro A30M 4 GB · needs 0.7 GB · Q8_0 · comfortable 1,036 tok/s
What the numbers mean
What you need to run it
Minimum card
Tesla C1080
Memory needed
0.7 GB
Fastest
99,654 tok/s
TrellisNet-MoS (1.4x larger) PTB is small enough at 34M 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 1,084 tokens per second.
Top of the range is the B200, at roughly 99,654 tokens per second thanks to 8,000 GB/s of bandwidth.
Background
TrellisNet-MoS (1.4x larger) PTB was published by Carnegie Mellon University (CMU),Intel Labs,Bosch Center for Artificial Intelligence, in United States of America, in October 2018. The organisation is categorised as academia,Industry,Industry.
It works in Language, and is recorded as doing language modeling.
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
Half the cards that hold it manage more than 2,798.3 tokens per second, and 818 exceed reading speed outright.
Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.
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.
Step by step
How to choose a GPU for TrellisNet-MoS (1.4x larger) PTB
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 TrellisNet-MoS (1.4x larger) PTB — around 0.7 GB at Q8_0. Capacity is the gate — a card either holds it or it does not.
-
02
Match the context to your actual use
The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context TrellisNet-MoS (1.4x larger) PTB can slip off a card that handles short questions easily.
-
03
Decide how much compression you will accept
Compression is what makes TrellisNet-MoS (1.4x larger) PTB 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 TrellisNet-MoS (1.4x larger) PTB. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 99,654 tok/s.
-
05
Check the fit verdict before buying
A tight fit runs TrellisNet-MoS (1.4x larger) PTB but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
-
06
Open the card you have settled on
Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for TrellisNet-MoS (1.4x larger) PTB alone — a card is usually bought for more than one model.
Answers
TrellisNet-MoS (1.4x larger) PTB — common questions
Where can I download TrellisNet-MoS (1.4x larger) PTB?
The weights for TrellisNet-MoS (1.4x larger) PTB 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 TrellisNet-MoS (1.4x larger) PTB 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 TrellisNet-MoS (1.4x larger) PTB is rarely worth using. Every figure here assumes the whole model is on the card.
Would two GPUs run TrellisNet-MoS (1.4x larger) PTB faster?
Two cards buy memory rather than speed. That matters for TrellisNet-MoS (1.4x larger) PTB only if one card cannot hold it — 818 can, so a second adds little.
Why does the quantisation differ between cards for TrellisNet-MoS (1.4x larger) PTB?
A larger card holds a more accurate copy. Across the cards that run TrellisNet-MoS (1.4x larger) PTB, 1 compression levels are used; the floor control above pins it to one.
How accurate are these TrellisNet-MoS (1.4x larger) PTB speed estimates?
These are estimates with real error bars. The fastest result here, 59,792–159,446 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 TrellisNet-MoS (1.4x larger) PTB?
The smallest card in our catalogue that holds TrellisNet-MoS (1.4x larger) PTB is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 0.7 GB, and produces roughly 1,084 tokens per second. 818 cards in total can run it.
How fast is TrellisNet-MoS (1.4x larger) PTB on a GPU?
It depends on the card. The quickest we calculate is a B200 at about 99,654 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 TrellisNet-MoS (1.4x larger) PTB clear that.
How much VRAM does TrellisNet-MoS (1.4x larger) PTB 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 TrellisNet-MoS (1.4x larger) PTB 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 18,561 tokens per second — a comfortable fit.
Can I run TrellisNet-MoS (1.4x larger) PTB 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 11,366 tokens per second — a comfortable fit.
Can I run TrellisNet-MoS (1.4x larger) PTB 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 14,076 tokens per second — a comfortable fit.
Can I run TrellisNet-MoS (1.4x larger) PTB 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 16,692 tokens per second — a comfortable fit.
Is TrellisNet-MoS (1.4x larger) PTB open source?
Its weights are published, so TrellisNet-MoS (1.4x larger) PTB 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 TrellisNet-MoS (1.4x larger) PTB have?
TrellisNet-MoS (1.4x larger) PTB has 34M parameters. 34M, table 1. 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 TrellisNet-MoS (1.4x larger) PTB?
TrellisNet-MoS (1.4x larger) PTB was published by Carnegie Mellon University (CMU),Intel Labs,Bosch Center for Artificial Intelligence, based in United States of America, categorised as academia,Industry,Industry.
When was TrellisNet-MoS (1.4x larger) PTB released?
TrellisNet-MoS (1.4x larger) PTB was published in October 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 TrellisNet-MoS (1.4x larger) PTB used for?
TrellisNet-MoS (1.4x larger) PTB 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.
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.