ProtT5-XXL-BFD 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
Xeon Phi 5110P
8 GB · IQ4_XS · 19.7 tok/s
Fastest card
B200
308 tok/s · 180 GB
Which GPUs can run ProtT5-XXL-BFD?
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.
509 cards match
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
308
tok/s
185–493 · low confidence |
B200 NVIDIA | 180 GB | 8,000 GB/s | Jan 2024 | 12.5 GB | Q8_0 | Comfortable |
|
308
tok/s
185–493 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 12.5 GB | Q8_0 | Comfortable |
|
246
tok/s
148–394 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 12.5 GB | Q8_0 | Comfortable |
|
246
tok/s
148–394 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 12.5 GB | Q8_0 | Comfortable |
|
197
tok/s
118–315 · low confidence |
Radeon Instinct MI300 AMD | 128 GB | 6,550 GB/s | Jan 2023 | 12.5 GB | Q8_0 | Comfortable |
|
188
tok/s
113–301 · low confidence |
H200 NVL NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 12.5 GB | Q8_0 | Comfortable |
|
188
tok/s
113–301 · low confidence |
H200 SXM 141 GB NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 12.5 GB | Q8_0 | Comfortable |
|
180
tok/s
108–288 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 12.5 GB | Q8_0 | Comfortable |
|
160
tok/s
96–256 · low confidence |
Radeon Instinct MI300A AMD | 128 GB | 5,325 GB/s | Dec 2023 | 12.5 GB | Q8_0 | Comfortable |
|
160
tok/s
96–256 · low confidence |
Radeon Instinct MI300X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 12.5 GB | Q8_0 | Comfortable |
|
160
tok/s
96–256 · low confidence |
Radeon Instinct MI308X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 12.5 GB | Q8_0 | Comfortable |
|
152
tok/s
91–243 · low confidence |
H100 NVL 94 GB NVIDIA | 94 GB | 3,940 GB/s | Mar 2023 | 12.5 GB | Q8_0 | Comfortable |
|
141
tok/s
85–225 · low confidence |
CMP 170HX 8 GB NVIDIA | 8 GB | 1,490 GB/s | Sep 2021 | 6.7 GB | IQ4_XS | Tight |
|
129
tok/s
78–207 · low confidence |
H100 PCIe 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 12.5 GB | Q8_0 | Comfortable |
|
129
tok/s
78–207 · low confidence |
H100 SXM5 80 GB NVIDIA | 80 GB | 3,360 GB/s | Oct 2022 | 12.5 GB | Q8_0 | Comfortable |
|
129
tok/s
78–207 · low confidence |
H100 SXM5 94 GB NVIDIA | 94 GB | 3,360 GB/s | Mar 2023 | 12.5 GB | Q8_0 | Comfortable |
|
129
tok/s
78–207 · low confidence |
H100 SXM5 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 12.5 GB | Q8_0 | Comfortable |
|
129
tok/s
78–207 · low confidence |
H800 SXM5 NVIDIA | 80 GB | 3,360 GB/s | Mar 2023 | 12.5 GB | Q8_0 | Comfortable |
|
107
tok/s
64–172 · low confidence |
CMP 170HX 10 GB NVIDIA | 10 GB | 1,560 GB/s | Sep 2021 | 8.6 GB | Q5_K_M | Tight |
|
98.5
tok/s
59–158 · low confidence |
Radeon Instinct MI250 AMD | 128 GB | 3,280 GB/s | Nov 2021 | 12.5 GB | Q8_0 | Comfortable |
|
98.5
tok/s
59–158 · low confidence |
Radeon Instinct MI250X AMD | 128 GB | 3,280 GB/s | Nov 2021 | 12.5 GB | Q8_0 | Comfortable |
|
82.1
tok/s
49–131 · low confidence |
Data Center GPU Max 1550 Intel | 128 GB | 3,280 GB/s | Jan 2023 | 12.5 GB | Q8_0 | Comfortable |
|
80.3
tok/s
48–129 · low confidence |
Data Center GPU Max Subsystem Intel | 128 GB | 3,210 GB/s | Jan 2023 | 12.5 GB | Q8_0 | Comfortable |
|
78.6
tok/s
47–126 · low confidence |
A100 SXM4 80 GB NVIDIA | 80 GB | 2,040 GB/s | Nov 2020 | 12.5 GB | Q8_0 | Comfortable |
|
78.6
tok/s
47–126 · low confidence |
A100X NVIDIA | 80 GB | 2,040 GB/s | Jun 2021 | 12.5 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
- Technical University of Munich,Med AI Technology,NVIDIA,Oak Ridge National Laboratory,Google,Seoul National University
- Organisation type
- Academia,Industry,Government,Industry,Academia
- Country
- Germany, China, United States of America, Korea (Republic of)
- Published
- 4 May 2021
- Authors
- Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, Debsindhu Bhowmik, Burkhard Rost
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Proteins, Protein or nucleotide language model (pLM/nLM), Protein representation learning
- Approach
- Self-supervised learning
- 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
- 11B
- Training data
- 393,000,000,000 tokens
- Epochs
- 5
Table 2
Table 1: 2122M proteins, 393B amino acids, 572 GB
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
- 3.7 × 10²² FLOP
- How it was established
- Operation counting
FLOP = 11B*2*(920k*512*4096) + 11B*4*(920k*512*4096), 920k steps using seq length 512 batch size 4096,
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
- Google TPU v3
- Chips used
- 512
- Power draw
- 466.5 kW
- Compute cost
- $43,025
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
- Unreleased
Licensed under the Academic Free License version 3.0 The ProtTrans project is a open source project supported by various partner companies and research institutions. We are committed to share all our pre-trained models and knowledge. https://github.com/agemagician/ProtTrans
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
- ProtTrans:Towards Cracking the Language of Life's Code Through Self-Supervised Learning
- Last updated
- 28 November 2025
The extremes
The ten fastest GPUs that run ProtT5-XXL-BFD
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_0 308 tok/s
- 02 B200 180 GB · 8,000 GB/s · Q8_0 308 tok/s
- 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q8_0 246 tok/s
- 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q8_0 246 tok/s
- 05 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q8_0 197 tok/s
- 06 H200 NVL 141 GB · 4,890 GB/s · Q8_0 188 tok/s
- 07 H200 SXM 141 GB 141 GB · 4,890 GB/s · Q8_0 188 tok/s
- 08 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q8_0 180 tok/s
- 09 Radeon Instinct MI300A 128 GB · 5,325 GB/s · Q8_0 160 tok/s
- 10 Radeon Instinct MI300X 192 GB · 5,325 GB/s · Q8_0 160 tok/s
The smallest GPUs that still run ProtT5-XXL-BFD
The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.
- 01 Radeon RX 7400 8 GB · needs 6.7 GB · IQ4_XS · tight 21.2 tok/s
- 02 Radeon RX 9060 8 GB · needs 6.7 GB · IQ4_XS · tight 23.8 tok/s
- 03 GeForce RTX 5050 8 GB · needs 6.7 GB · IQ4_XS · tight 30.3 tok/s
- 04 GeForce RTX 5050 Mobile 8 GB · needs 6.7 GB · IQ4_XS · tight 36.3 tok/s
- 05 Radeon RX 9060 XT 8 GB 8 GB · needs 6.7 GB · IQ4_XS · tight 23.8 tok/s
- 06 GeForce RTX 5060 Mobile 8 GB · needs 6.7 GB · IQ4_XS · tight 36.3 tok/s
- 07 GeForce RTX 5060 8 GB · needs 6.7 GB · IQ4_XS · tight 42.4 tok/s
- 08 GeForce RTX 5060 Ti 8 GB 8 GB · needs 6.7 GB · IQ4_XS · tight 42.4 tok/s
- 09 GeForce RTX 5070 Mobile 8 GB · needs 6.7 GB · IQ4_XS · tight 36.3 tok/s
- 10 Radeon RX 7650 GRE 8 GB · needs 6.7 GB · IQ4_XS · tight 21.2 tok/s
What the numbers mean
The hardware side
Minimum card
Xeon Phi 5110P
Memory needed
6.7 GB
Fastest
308 tok/s
ProtT5-XXL-BFD is small enough at 11B parameters that hardware is rarely the obstacle — 509 of the cards we track can run it, including cards several years old.
At the low end, a Xeon Phi 5110P handles it — 8 GB, at IQ4_XS, for about 19.7 tokens per second.
A B200 is the fastest we calculate for it: about 308 tokens per second, from 8,000 GB/s of memory bandwidth.
Where it came from
ProtT5-XXL-BFD was published by Technical University of Munich,Med AI Technology,NVIDIA,Oak Ridge National Laboratory,Google,Seoul National University, in Germany, in May 2021. It comes out of academia,Industry,Government,Industry,Academia.
It works in Biology, and is recorded as doing proteins, Protein or nucleotide language model (pLM/nLM), Protein representation learning.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.
Understanding the speeds
Half the cards that hold it manage more than 21.2 tokens per second, and 460 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.
Training and provenance
The training run consumed about 3.7 × 10²² FLOP, on Google TPU v3. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Around 393,000,000,000 tokens went into training it.
Step by step
How to choose a GPU for ProtT5-XXL-BFD
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 ProtT5-XXL-BFD — around 6.7 GB at IQ4_XS. That figure, not the card's headline performance, is what decides whether it runs.
-
02
Set the context length you will work at
Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason ProtT5-XXL-BFD stops fitting a card that seemed fine.
-
03
Choose how far you will compress it
Each card runs the least-compressed copy it can hold — IQ4_XS on the smallest card that fits. Setting a floor drops the cards that only manage ProtT5-XXL-BFD by squeezing it further than you would want.
-
04
Compare tokens per second, not specifications
Sort by speed to see how cards rank for ProtT5-XXL-BFD. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 308 tok/s.
-
05
Read the fit column last
Tight means ProtT5-XXL-BFD 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
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 ProtT5-XXL-BFD alone — a card is usually bought for more than one model.
Answers
ProtT5-XXL-BFD — common questions
How many parameters does ProtT5-XXL-BFD have?
ProtT5-XXL-BFD has 11B parameters. 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 ProtT5-XXL-BFD?
ProtT5-XXL-BFD was published by Technical University of Munich,Med AI Technology,NVIDIA,Oak Ridge National Laboratory,Google,Seoul National University, based in Germany, categorised as academia,Industry,Government,Industry,Academia.
When was ProtT5-XXL-BFD released?
ProtT5-XXL-BFD was published in May 2021. 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 ProtT5-XXL-BFD used for?
ProtT5-XXL-BFD works in Biology, and is recorded as handling proteins, Protein or nucleotide language model (pLM/nLM), Protein representation learning. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download ProtT5-XXL-BFD?
The weights for ProtT5-XXL-BFD 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 ProtT5-XXL-BFD?
Around 3.7 × 10²² FLOP, on Google TPU v3. 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 ProtT5-XXL-BFD if it does not fit in my GPU?
It can be split between the card and system memory, but ProtT5-XXL-BFD generates painfully slowly that way — the nearest miss we calculate is short by 2.0 GB. Nothing on this page assumes offloading.
Would two GPUs run ProtT5-XXL-BFD faster?
Two cards buy memory rather than speed. That matters for ProtT5-XXL-BFD only if one card cannot hold it — 509 can, so a second adds little.
Why does the quantisation differ between cards for ProtT5-XXL-BFD?
A larger card holds a more accurate copy. Across the cards that run ProtT5-XXL-BFD, 4 compression levels are used; the floor control above pins it to one.
How accurate are these ProtT5-XXL-BFD speed estimates?
These are estimates with real error bars. The fastest result here, 185–493 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 ProtT5-XXL-BFD?
The smallest card in our catalogue that holds ProtT5-XXL-BFD is the Xeon Phi 5110P, with 8 GB of memory. It runs the model at IQ4_XS using about 6.7 GB, and produces roughly 19.7 tokens per second. 509 cards in total can run it.
How fast is ProtT5-XXL-BFD on a GPU?
It depends on the card. The quickest we calculate is a B200 at about 308 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 460 of the cards that can run ProtT5-XXL-BFD clear that.
How much VRAM does ProtT5-XXL-BFD need?
About 6.7 GB at IQ4_XS 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 ProtT5-XXL-BFD on a 8 GB GPU?
Yes. A CMP 170HX 8 GB with 8 GB runs it at IQ4_XS, using about 6.7 GB and generating roughly 141 tokens per second — a tight fit.
Can I run ProtT5-XXL-BFD on a 12 GB GPU?
Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q6_K, using about 9.9 GB and generating roughly 51.0 tokens per second — a tight fit.
Can I run ProtT5-XXL-BFD on a 16 GB GPU?
Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 12.5 GB and generating roughly 43.5 tokens per second — a tight fit.
Can I run ProtT5-XXL-BFD on a 24 GB GPU?
Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 12.5 GB and generating roughly 51.6 tokens per second — a comfortable fit.
Is ProtT5-XXL-BFD open source?
Its weights are published, so ProtT5-XXL-BFD 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.
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.