XLMR-XXL 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 · Q4_K_M · 19.0 tok/s
Fastest card
B200
317 tok/s · 180 GB
Which GPUs can run XLMR-XXL?
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 | |||||
|---|---|---|---|---|---|---|---|
|
317
tok/s
190–507 · low confidence |
B200 NVIDIA | 180 GB | 8,000 GB/s | Jan 2024 | 12.2 GB | Q8_0 | Comfortable |
|
317
tok/s
190–507 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 12.2 GB | Q8_0 | Comfortable |
|
253
tok/s
152–405 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 12.2 GB | Q8_0 | Comfortable |
|
253
tok/s
152–405 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 12.2 GB | Q8_0 | Comfortable |
|
202
tok/s
121–324 · low confidence |
Radeon Instinct MI300 AMD | 128 GB | 6,550 GB/s | Jan 2023 | 12.2 GB | Q8_0 | Comfortable |
|
194
tok/s
116–310 · low confidence |
H200 NVL NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 12.2 GB | Q8_0 | Comfortable |
|
194
tok/s
116–310 · low confidence |
H200 SXM 141 GB NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 12.2 GB | Q8_0 | Comfortable |
|
185
tok/s
111–296 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 12.2 GB | Q8_0 | Comfortable |
|
164
tok/s
99–263 · low confidence |
Radeon Instinct MI300A AMD | 128 GB | 5,325 GB/s | Dec 2023 | 12.2 GB | Q8_0 | Comfortable |
|
164
tok/s
99–263 · low confidence |
Radeon Instinct MI300X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 12.2 GB | Q8_0 | Comfortable |
|
164
tok/s
99–263 · low confidence |
Radeon Instinct MI308X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 12.2 GB | Q8_0 | Comfortable |
|
156
tok/s
94–250 · low confidence |
H100 NVL 94 GB NVIDIA | 94 GB | 3,940 GB/s | Mar 2023 | 12.2 GB | Q8_0 | Comfortable |
|
136
tok/s
82–218 · low confidence |
CMP 170HX 8 GB NVIDIA | 8 GB | 1,490 GB/s | Sep 2021 | 7.2 GB | Q4_K_M | Tight |
|
133
tok/s
80–213 · low confidence |
H100 PCIe 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 12.2 GB | Q8_0 | Comfortable |
|
133
tok/s
80–213 · low confidence |
H100 SXM5 80 GB NVIDIA | 80 GB | 3,360 GB/s | Oct 2022 | 12.2 GB | Q8_0 | Comfortable |
|
133
tok/s
80–213 · low confidence |
H100 SXM5 94 GB NVIDIA | 94 GB | 3,360 GB/s | Mar 2023 | 12.2 GB | Q8_0 | Comfortable |
|
133
tok/s
80–213 · low confidence |
H100 SXM5 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 12.2 GB | Q8_0 | Comfortable |
|
133
tok/s
80–213 · low confidence |
H800 SXM5 NVIDIA | 80 GB | 3,360 GB/s | Mar 2023 | 12.2 GB | Q8_0 | Comfortable |
|
110
tok/s
66–176 · low confidence |
CMP 170HX 10 GB NVIDIA | 10 GB | 1,560 GB/s | Sep 2021 | 8.4 GB | Q5_K_M | Tight |
|
101
tok/s
61–162 · low confidence |
Radeon Instinct MI250 AMD | 128 GB | 3,280 GB/s | Nov 2021 | 12.2 GB | Q8_0 | Comfortable |
|
101
tok/s
61–162 · low confidence |
Radeon Instinct MI250X AMD | 128 GB | 3,280 GB/s | Nov 2021 | 12.2 GB | Q8_0 | Comfortable |
|
84.4
tok/s
51–135 · low confidence |
Data Center GPU Max 1550 Intel | 128 GB | 3,280 GB/s | Jan 2023 | 12.2 GB | Q8_0 | Comfortable |
|
82.6
tok/s
50–132 · low confidence |
Data Center GPU Max Subsystem Intel | 128 GB | 3,210 GB/s | Jan 2023 | 12.2 GB | Q8_0 | Comfortable |
|
80.8
tok/s
48–129 · low confidence |
A100 SXM4 80 GB NVIDIA | 80 GB | 2,040 GB/s | Nov 2020 | 12.2 GB | Q8_0 | Comfortable |
|
80.8
tok/s
48–129 · low confidence |
A100X NVIDIA | 80 GB | 2,040 GB/s | Jun 2021 | 12.2 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
- Facebook AI Research
- Organisation type
- Industry
- Country
- United States of America, France
- Published
- 17 August 2021
- Authors
- Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Translation, Language modeling/generation
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
- 10.7B
- Training data
- 167,000,000,000 tokens
- Epochs
- 3.14
- Batch size
- 1,048,576
Section 2.1: " ...XLM-RXXL (L= 48, H = 4096, A = 32, 10.7B params)"
"We pretrain the models on the CC100 dataset, which corresponds to 167B tokens in 100 languages."
Batches of 2048 with sequence length of 512
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
- 3.4 × 10²² FLOP
- How it was established
- Operation counting
Trained for 500k steps at a batch size of 2048 with sequence length of 512 = 524,288,000,000 tokens seen. 6 * 10700000000 * 524,288,000,000 = 3.366e22
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
https://github.com/facebookresearch/fairseq/tree/main/examples/xlmr
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
- 159
Abstract: "Our model also outperforms the RoBERTa-Large model on several English tasks of the GLUE benchmark by 0.3% on average while handling 99 more languages."
Sources
Where this record came from and when it was last checked.
- Reference
- Larger-Scale Transformers for Multilingual Masked Language Modeling
- Last updated
- 25 May 2026
The extremes
The ten fastest GPUs that run XLMR-XXL
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_0 317 tok/s
- 02 B200 180 GB · 8,000 GB/s · Q8_0 317 tok/s
- 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q8_0 253 tok/s
- 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q8_0 253 tok/s
- 05 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q8_0 202 tok/s
- 06 H200 NVL 141 GB · 4,890 GB/s · Q8_0 194 tok/s
- 07 H200 SXM 141 GB 141 GB · 4,890 GB/s · Q8_0 194 tok/s
- 08 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q8_0 185 tok/s
- 09 Radeon Instinct MI300A 128 GB · 5,325 GB/s · Q8_0 164 tok/s
- 10 Radeon Instinct MI300X 192 GB · 5,325 GB/s · Q8_0 164 tok/s
The smallest GPUs that still run XLMR-XXL
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 7.2 GB · Q4_K_M · tight 20.5 tok/s
- 02 Radeon RX 9060 8 GB · needs 7.2 GB · Q4_K_M · tight 23.0 tok/s
- 03 GeForce RTX 5050 8 GB · needs 7.2 GB · Q4_K_M · tight 29.2 tok/s
- 04 GeForce RTX 5050 Mobile 8 GB · needs 7.2 GB · Q4_K_M · tight 35.1 tok/s
- 05 Radeon RX 9060 XT 8 GB 8 GB · needs 7.2 GB · Q4_K_M · tight 23.0 tok/s
- 06 GeForce RTX 5060 Mobile 8 GB · needs 7.2 GB · Q4_K_M · tight 35.1 tok/s
- 07 GeForce RTX 5060 8 GB · needs 7.2 GB · Q4_K_M · tight 40.9 tok/s
- 08 GeForce RTX 5060 Ti 8 GB 8 GB · needs 7.2 GB · Q4_K_M · tight 40.9 tok/s
- 09 GeForce RTX 5070 Mobile 8 GB · needs 7.2 GB · Q4_K_M · tight 35.1 tok/s
- 10 Radeon RX 7650 GRE 8 GB · needs 7.2 GB · Q4_K_M · tight 20.5 tok/s
What the numbers mean
The hardware side
Minimum card
Xeon Phi 5110P
Memory needed
7.2 GB
Fastest
317 tok/s
XLMR-XXL is small enough at 10.7B 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 Q4_K_M, for about 19.0 tokens per second.
A B200 is the fastest we calculate for it: about 317 tokens per second, from 8,000 GB/s of memory bandwidth.
About this model
XLMR-XXL was published by Facebook AI Research, in United States of America, in August 2021. The organisation is categorised as industry.
It works in Language, and is recorded as doing translation, Language modeling/generation.
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.
How fast it runs, and why
Half the cards that hold it manage more than 20.5 tokens per second, and 461 exceed reading speed outright.
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.
Its internal architecture is not on file, so memory is approximated from the parameter count and marked accordingly. Expect the real figure to differ, more so at long context.
Training and provenance
Training it took roughly 3.4 × 10²² FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
It was trained on about 167,000,000,000 tokens of text.
Its inclusion criterion is sOTA improvement.
Step by step
How to choose a GPU for XLMR-XXL
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 XLMR-XXL — around 7.2 GB at Q4_K_M. 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 XLMR-XXL stops fitting a card that seemed fine.
-
03
Choose how far you will compress it
The quantisation column varies by card, because a bigger card holds a more accurate copy of XLMR-XXL — Q4_K_M 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 XLMR-XXL. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 317 tok/s.
-
05
Check the fit verdict before buying
Tight means XLMR-XXL 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 XLMR-XXL alone — a card is usually bought for more than one model.
Answers
XLMR-XXL — common questions
How much compute was used to train XLMR-XXL?
Around 3.4 × 10²² 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.
Can I run XLMR-XXL if it does not fit in my GPU?
It can be split between the card and system memory, but XLMR-XXL generates painfully slowly that way — the nearest miss we calculate is short by 1.8 GB. Nothing on this page assumes offloading.
Would two GPUs run XLMR-XXL faster?
Capacity adds across cards; throughput does not. Since 509 of the cards we track already hold XLMR-XXL on their own, a second card is rarely the answer here.
Why does the quantisation differ between cards for XLMR-XXL?
A larger card holds a more accurate copy. Across the cards that run XLMR-XXL, 4 compression levels are used; the floor control above pins it to one.
How accurate are these XLMR-XXL speed estimates?
Every figure is derived from memory bandwidth and model size, not benchmarked. That is why each is published as a range such as 190–507 tok/s on the B200 rather than a single number.
What GPU do I need to run XLMR-XXL?
The smallest card in our catalogue that holds XLMR-XXL is the Xeon Phi 5110P, with 8 GB of memory. It runs the model at Q4_K_M using about 7.2 GB, and produces roughly 19.0 tokens per second. 509 cards in total can run it.
How fast is XLMR-XXL on a GPU?
It depends on the card. The quickest we calculate is a B200 at about 317 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 461 of the cards that can run XLMR-XXL clear that.
How much VRAM does XLMR-XXL need?
About 7.2 GB at Q4_K_M 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 XLMR-XXL on a 8 GB GPU?
Yes. A CMP 170HX 8 GB with 8 GB runs it at Q4_K_M, using about 7.2 GB and generating roughly 136 tokens per second — a tight fit.
Can I run XLMR-XXL on a 12 GB GPU?
Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q6_K, using about 9.7 GB and generating roughly 52.5 tokens per second — a tight fit.
Can I run XLMR-XXL on a 16 GB GPU?
Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 12.2 GB and generating roughly 44.7 tokens per second — a tight fit.
Can I run XLMR-XXL on a 24 GB GPU?
Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 12.2 GB and generating roughly 53.0 tokens per second — a comfortable fit.
Is XLMR-XXL open source?
Its weights are published, so XLMR-XXL 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 XLMR-XXL have?
XLMR-XXL has 10.7B parameters. Section 2.1: " ...XLM-RXXL (L= 48, H = 4096, A = 32, 10.7B params)". 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 XLMR-XXL?
XLMR-XXL was published by Facebook AI Research, based in United States of America, categorised as industry.
When was XLMR-XXL released?
XLMR-XXL was published in August 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 XLMR-XXL used for?
XLMR-XXL works in Language, and is recorded as handling translation, Language modeling/generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download XLMR-XXL?
The weights for XLMR-XXL are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
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.