MADLAD-400 10B 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 MADLAD-400 10B?
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
- Google DeepMind,Google Research
- Organisation type
- Industry,Industry
- Country
- United States of America
- Published
- 9 September 2023
- Authors
- Sneha Kudugunta, Isaac Caswell, Biao Zhang, Xavier Garcia, Christopher A. Choquette-Choo, Katherine Lee, Derrick Xin, Aditya Kusupati, Romi Stella, Ankur Bapna, Orhan Firat
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Translation
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
- 250,000,000,000 tokens
- Epochs
- 1
10.7B from appendix A.8
MADLAD-400, dataset released with paper, is 3T tokens: 'We introduce MADLAD-400, a manually audited, general domain 3T token monolingual dataset based on CommonCrawl, spanning 419 languages.' However, model in question was trained on only 250B tokens: "We then train and release a 10.7B-parameter multilingual machine translation model on 250 billion tokens covering over 450 languages using publicly available data"
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
- 1.6 × 10²² FLOP
- How it was established
- Operation counting
6ND = 10.7B * 250B = 1.6e22 'MADLAD-400-10B-MT is a multilingual machine translation model based on the T5 architecture that was trained on 250 billion tokens covering over 450 languages using publicly available data. ' 10.7B params from appendix A.8
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)
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
- 229
Sources
Where this record came from and when it was last checked.
- Reference
- MADLAD-400: A Multilingual And Document-Level Large Audited Dataset
- Last updated
- 25 May 2026
The extremes
The ten fastest GPUs that run MADLAD-400 10B
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 MADLAD-400 10B
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
What it takes to run this model
Minimum card
Xeon Phi 5110P
Memory needed
7.2 GB
Fastest
317 tok/s
MADLAD-400 10B 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.
The smallest card that holds it is the Xeon Phi 5110P with 8 GB, running it at Q4_K_M and producing around 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.
Where it came from
MADLAD-400 10B was published by Google DeepMind,Google Research, in United States of America, in September 2023. It comes out of industry,Industry.
It works in Language, and is recorded as doing translation.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.
Understanding the speeds
The median result is around 20.5 tokens per second; 461 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.
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.
What went into building it
Training it took roughly 1.6 × 10²² FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
Around 250,000,000,000 tokens went into training it.
Step by step
How to choose a GPU for MADLAD-400 10B
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
Look at what MADLAD-400 10B actually needs — around 7.2 GB at Q4_K_M. No amount of processing power compensates for a card that cannot hold it.
-
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 MADLAD-400 10B.
-
03
Choose how far you will compress it
Each card runs the least-compressed copy it can hold — Q4_K_M on the smallest card that fits. Setting a floor drops the cards that only manage MADLAD-400 10B by squeezing it further than you would want.
-
04
Sort by speed
Sort by speed to see how cards rank for MADLAD-400 10B. 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
Read the fit column last
Tight means MADLAD-400 10B 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
See what else that card runs
Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for MADLAD-400 10B alone — a card is usually bought for more than one model.
Answers
MADLAD-400 10B — common questions
Why does the quantisation differ between cards for MADLAD-400 10B?
Each card is shown running the least-compressed copy it can hold, and MADLAD-400 10B appears at 4 different compression levels across the cards that fit it. Bigger cards get the more accurate version.
How accurate are these MADLAD-400 10B speed estimates?
These are estimates with real error bars. The fastest result here, 190–507 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 MADLAD-400 10B?
The smallest card in our catalogue that holds MADLAD-400 10B 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 MADLAD-400 10B 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 MADLAD-400 10B clear that.
How much VRAM does MADLAD-400 10B 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 MADLAD-400 10B 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 MADLAD-400 10B 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 MADLAD-400 10B 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 MADLAD-400 10B 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 MADLAD-400 10B open source?
Its weights are published, so MADLAD-400 10B 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 MADLAD-400 10B have?
MADLAD-400 10B has 10.7B parameters. 10.7B from appendix A.8. 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 MADLAD-400 10B?
MADLAD-400 10B was published by Google DeepMind,Google Research, based in United States of America, categorised as industry,Industry.
When was MADLAD-400 10B released?
MADLAD-400 10B was published in September 2023. 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 MADLAD-400 10B used for?
MADLAD-400 10B works in Language, and is recorded as handling translation. 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 MADLAD-400 10B?
The weights for MADLAD-400 10B 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 MADLAD-400 10B?
Around 1.6 × 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 MADLAD-400 10B 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 MADLAD-400 10B is rarely worth using — the nearest miss we calculate is short by 1.8 GB. Every figure here assumes the whole model is on the card.
Would two GPUs run MADLAD-400 10B faster?
Two cards buy memory rather than speed. That matters for MADLAD-400 10B only if one card cannot hold it — 509 can, so a second adds little.
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.