OpenThaiGPT 1.6 / OTG-1.6 (72B) 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
Smallest card that fits
A100 PCIe 40 GB
40 GB · Q3_K_M · 24.8 tok/s
Fastest card
B200
47.1 tok/s · 180 GB
Which GPUs can run OpenThaiGPT 1.6 / OTG-1.6 (72B)?
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.
61 cards match
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
47.1
tok/s
28–75 · low confidence |
B200 NVIDIA | 180 GB | 8,000 GB/s | Jan 2024 | 77.8 GB | Q8_0 | Comfortable |
|
47.1
tok/s
28–75 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 77.8 GB | Q8_0 | Comfortable |
|
37.6
tok/s
23–60 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 77.8 GB | Q8_0 | Comfortable |
|
37.6
tok/s
23–60 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 77.8 GB | Q8_0 | Comfortable |
|
30.1
tok/s
18–48 · low confidence |
Radeon Instinct MI300 AMD | 128 GB | 6,550 GB/s | Jan 2023 | 77.8 GB | Q8_0 | Comfortable |
|
28.8
tok/s
17–46 · low confidence |
H200 NVL NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 77.8 GB | Q8_0 | Comfortable |
|
28.8
tok/s
17–46 · low confidence |
H200 SXM 141 GB NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 77.8 GB | Q8_0 | Comfortable |
|
28.7
tok/s
17–46 · low confidence |
H100 SXM5 80 GB NVIDIA | 80 GB | 3,360 GB/s | Oct 2022 | 61.0 GB | Q6_K | Tight |
|
28.7
tok/s
17–46 · low confidence |
H800 SXM5 NVIDIA | 80 GB | 3,360 GB/s | Mar 2023 | 61.0 GB | Q6_K | Tight |
|
27.5
tok/s
17–44 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 77.8 GB | Q8_0 | Comfortable |
|
27.0
tok/s
16–43 · low confidence |
GRID A100B NVIDIA | 48 GB | 1,870 GB/s | May 2020 | 40.1 GB | IQ4_XS | Tight |
|
24.8
tok/s
15–40 · low confidence |
A100 PCIe 40 GB NVIDIA | 40 GB | 1,560 GB/s | Jun 2020 | 35.9 GB | Q3_K_M | Tight |
|
24.8
tok/s
15–40 · low confidence |
A100 SXM4 40 GB NVIDIA | 40 GB | 1,560 GB/s | May 2020 | 35.9 GB | Q3_K_M | Tight |
|
24.8
tok/s
15–40 · low confidence |
A800 PCIe 40 GB NVIDIA | 40 GB | 1,560 GB/s | Nov 2022 | 35.9 GB | Q3_K_M | Tight |
|
24.4
tok/s
15–39 · low confidence |
Radeon Instinct MI300A AMD | 128 GB | 5,325 GB/s | Dec 2023 | 77.8 GB | Q8_0 | Comfortable |
|
24.4
tok/s
15–39 · low confidence |
Radeon Instinct MI300X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 77.8 GB | Q8_0 | Comfortable |
|
24.4
tok/s
15–39 · low confidence |
Radeon Instinct MI308X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 77.8 GB | Q8_0 | Comfortable |
|
23.2
tok/s
14–37 · low confidence |
H100 NVL 94 GB NVIDIA | 94 GB | 3,940 GB/s | Mar 2023 | 77.8 GB | Q8_0 | Tight |
|
21.2
tok/s
13–34 · low confidence |
H100 SXM5 64 GB NVIDIA | 64 GB | 2,020 GB/s | Mar 2023 | 52.6 GB | Q5_K_M | Tight |
|
19.8
tok/s
12–32 · low confidence |
H100 PCIe 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 77.8 GB | Q8_0 | Tight |
|
19.8
tok/s
12–32 · low confidence |
H100 SXM5 94 GB NVIDIA | 94 GB | 3,360 GB/s | Mar 2023 | 77.8 GB | Q8_0 | Tight |
|
19.8
tok/s
12–32 · low confidence |
H100 SXM5 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 77.8 GB | Q8_0 | Tight |
|
19.4
tok/s
12–31 · low confidence |
RTX PRO 5000 Blackwell NVIDIA | 48 GB | 1,340 GB/s | Mar 2025 | 40.1 GB | IQ4_XS | Tight |
|
17.4
tok/s
10–28 · low confidence |
A100 SXM4 80 GB NVIDIA | 80 GB | 2,040 GB/s | Nov 2020 | 61.0 GB | Q6_K | Tight |
|
17.4
tok/s
10–28 · low confidence |
A100X NVIDIA | 80 GB | 2,040 GB/s | Jun 2021 | 61.0 GB | Q6_K | Tight |
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
- Mahidol University,AI Entrepreneurs Association of Thailand
- Organisation type
- Academia
- Country
- Thailand
- Published
- 2 April 2025
- Authors
- Sumeth Yuenyong, Thodsaporn Chay-intr, Kobkrit Viriyayudhakorn
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Question answering
- Base model
- Qwen2.5 Instruct (72B)
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
- 72B
- Training data
- tokens
- Epochs
- 3
72b
Batch Size per Device 4 8 GPUs Maximum Length (Initial Training) 2400 tokens
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 H100 SXM5 80GB
- Chips used
- 8
- Power draw
- 11.0 kW
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 (restricted use)
- Training code
- Unreleased
- Hugging Face
- openthaigpt
qwen license https://huggingface.co/openthaigpt/openthaigpt-1.6-72b-instruct Qwen license (restriction on >100m monthly users)
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
- OpenThaiGPT 1.6 and R1: Thai-Centric Open Source and Reasoning Large Language Models
- Last updated
- 11 February 2026
The extremes
The ten fastest GPUs for OpenThaiGPT 1.6 / OTG-1.6 (72B)
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 47.1 tok/s
- 02 B200 180 GB · 8,000 GB/s · Q8_0 47.1 tok/s
- 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q8_0 37.6 tok/s
- 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q8_0 37.6 tok/s
- 05 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q8_0 30.1 tok/s
- 06 H200 NVL 141 GB · 4,890 GB/s · Q8_0 28.8 tok/s
- 07 H200 SXM 141 GB 141 GB · 4,890 GB/s · Q8_0 28.8 tok/s
- 08 H800 SXM5 80 GB · 3,360 GB/s · Q6_K 28.7 tok/s
- 09 H100 SXM5 80 GB 80 GB · 3,360 GB/s · Q6_K 28.7 tok/s
- 10 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q8_0 27.5 tok/s
The smallest GPUs that still run OpenThaiGPT 1.6 / OTG-1.6 (72B)
The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.
- 01 A800 PCIe 40 GB 40 GB · needs 35.9 GB · Q3_K_M · tight 24.8 tok/s
- 02 A100 PCIe 40 GB 40 GB · needs 35.9 GB · Q3_K_M · tight 24.8 tok/s
- 03 A100 SXM4 40 GB 40 GB · needs 35.9 GB · Q3_K_M · tight 24.8 tok/s
- 04 Radeon PRO W7900D 48 GB · needs 40.1 GB · IQ4_XS · tight 9.7 tok/s
- 05 RTX PRO 5000 Blackwell 48 GB · needs 40.1 GB · IQ4_XS · tight 19.4 tok/s
- 06 RTX 5880 Ada Generation 48 GB · needs 40.1 GB · IQ4_XS · tight 12.5 tok/s
- 07 L20 48 GB · needs 40.1 GB · IQ4_XS · tight 12.5 tok/s
- 08 Radeon PRO W7800 48 GB 48 GB · needs 40.1 GB · IQ4_XS · tight 9.7 tok/s
- 09 Radeon PRO W7900 48 GB · needs 40.1 GB · IQ4_XS · tight 9.7 tok/s
- 10 Data Center GPU Max 1100 48 GB · needs 40.1 GB · IQ4_XS · tight 11.6 tok/s
What the numbers mean
What it takes to run this model
Minimum card
A100 PCIe 40 GB
Memory needed
35.9 GB
Fastest
47.1 tok/s
OpenThaiGPT 1.6 / OTG-1.6 (72B) sits at 72B parameters, which puts it above consumer hardware and into the range where a card is bought for this purpose rather than repurposed for it. 61 of the cards we track can hold it.
The smallest card that holds it is the A100 PCIe 40 GB with 40 GB, running it at Q3_K_M and producing around 24.8 tokens per second.
The quickest result comes from a B200 at around 47.1 tokens per second — its 8,000 GB/s of bandwidth is what buys that.
Where it came from
OpenThaiGPT 1.6 / OTG-1.6 (72B) was published by Mahidol University,AI Entrepreneurs Association of Thailand, in Thailand, in April 2025. academia is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling/generation, Question answering.
It builds on Qwen2.5 Instruct (72B), which is why it shares that model's general shape and size.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. It is published under the openthaigpt organisation on Hugging Face.
Understanding the speeds
Across every card that can run it, the middle of the range is about 16.6 tokens per second, and 51 of them clear the ten tokens per second that roughly matches reading speed.
Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.
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.
Step by step
How to choose a GPU for OpenThaiGPT 1.6 / OTG-1.6 (72B)
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
Every card here has been checked against OpenThaiGPT 1.6 / OTG-1.6 (72B) — around 35.9 GB at Q3_K_M. Capacity is the gate — a card either holds it or it does not.
-
02
Set the context length you will work at
Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for OpenThaiGPT 1.6 / OTG-1.6 (72B).
-
03
Decide how much compression you will accept
The quantisation column varies by card, because a bigger card holds a more accurate copy of OpenThaiGPT 1.6 / OTG-1.6 (72B) — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
-
04
Sort by speed
Sort by speed to see how cards rank for OpenThaiGPT 1.6 / OTG-1.6 (72B). It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 47.1 tok/s.
-
05
Check the fit verdict before buying
Tight means OpenThaiGPT 1.6 / OTG-1.6 (72B) 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
Check the card from the other side
Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for OpenThaiGPT 1.6 / OTG-1.6 (72B) alone — a card is usually bought for more than one model.
Answers
OpenThaiGPT 1.6 / OTG-1.6 (72B) — common questions
What is OpenThaiGPT 1.6 / OTG-1.6 (72B) used for?
OpenThaiGPT 1.6 / OTG-1.6 (72B) works in Language, and is recorded as handling language modeling/generation, Question answering. 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 OpenThaiGPT 1.6 / OTG-1.6 (72B)?
Its weights are published under the openthaigpt organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
Can I run OpenThaiGPT 1.6 / OTG-1.6 (72B) if it does not fit in my GPU?
Only by offloading, which is usually a false economy: the part in system memory drags the whole thing down — the nearest miss we calculate is short by 15.5 GB. Our figures for OpenThaiGPT 1.6 / OTG-1.6 (72B) assume it is fully resident.
Would two GPUs run OpenThaiGPT 1.6 / OTG-1.6 (72B) faster?
A second card roughly doubles the memory available but not the generation rate. With 61 cards already able to run OpenThaiGPT 1.6 / OTG-1.6 (72B) alone, the case for pairing is weak.
Why does the quantisation differ between cards for OpenThaiGPT 1.6 / OTG-1.6 (72B)?
Each card is shown running the least-compressed copy it can hold, and OpenThaiGPT 1.6 / OTG-1.6 (72B) appears at 5 different compression levels across the cards that fit it. Bigger cards get the more accurate version.
How accurate are these OpenThaiGPT 1.6 / OTG-1.6 (72B) 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 28–75 tok/s on the B200 rather than a single number.
What GPU do I need to run OpenThaiGPT 1.6 / OTG-1.6 (72B)?
The smallest card in our catalogue that holds OpenThaiGPT 1.6 / OTG-1.6 (72B) is the A100 PCIe 40 GB, with 40 GB of memory. It runs the model at Q3_K_M using about 35.9 GB, and produces roughly 24.8 tokens per second. 61 cards in total can run it.
How fast is OpenThaiGPT 1.6 / OTG-1.6 (72B) on a GPU?
It depends on the card. The quickest we calculate is a B200 at about 47.1 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 51 of the cards that can run OpenThaiGPT 1.6 / OTG-1.6 (72B) clear that.
How much VRAM does OpenThaiGPT 1.6 / OTG-1.6 (72B) need?
About 35.9 GB at Q3_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.
Is OpenThaiGPT 1.6 / OTG-1.6 (72B) open source?
Its weights are published, so OpenThaiGPT 1.6 / OTG-1.6 (72B) 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 OpenThaiGPT 1.6 / OTG-1.6 (72B) have?
OpenThaiGPT 1.6 / OTG-1.6 (72B) has 72B parameters. 72b. 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 OpenThaiGPT 1.6 / OTG-1.6 (72B)?
OpenThaiGPT 1.6 / OTG-1.6 (72B) was published by Mahidol University,AI Entrepreneurs Association of Thailand, based in Thailand, categorised as academia.
When was OpenThaiGPT 1.6 / OTG-1.6 (72B) released?
OpenThaiGPT 1.6 / OTG-1.6 (72B) was published in April 2025.
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.