OpenThaiGPT v1.0.0 (7B) TPS calculator

Open weights Mahidol University,AI Entrepreneurs Association of Thailand 6.8B parameters April 2024

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

589 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla K20c

5 GB · IQ4_XS · 27.0 tok/s

Fastest card

B200

498 tok/s · 180 GB

Which GPUs can run OpenThaiGPT v1.0.0 (7B)?

Set the inputs, read the answer

Context length tokens

A longer conversation needs more memory, which can push this model off smaller cards.

Minimum quality

Hides cards that would only fit the model by compressing it below this point.

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Card memory

Also show cards that cannot run it

589 cards match

Calculating

					Needs	Quantisation	Fit
498 tok/s 299–796 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	8.0 GB	Q8_0	Comfortable
498 tok/s 299–796 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	8.0 GB	Q8_0	Comfortable
397 tok/s 238–636 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	8.0 GB	Q8_0	Comfortable
397 tok/s 238–636 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	8.0 GB	Q8_0	Comfortable
318 tok/s 191–508 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	8.0 GB	Q8_0	Comfortable
304 tok/s 182–487 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	8.0 GB	Q8_0	Comfortable
304 tok/s 182–487 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	8.0 GB	Q8_0	Comfortable
291 tok/s 175–466 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	8.0 GB	Q8_0	Comfortable
258 tok/s 155–413 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	8.0 GB	Q8_0	Comfortable
258 tok/s 155–413 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	8.0 GB	Q8_0	Comfortable
258 tok/s 155–413 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	8.0 GB	Q8_0	Comfortable
245 tok/s 147–392 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	8.0 GB	Q8_0	Comfortable
209 tok/s 125–334 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	8.0 GB	Q8_0	Comfortable
209 tok/s 125–334 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	8.0 GB	Q8_0	Comfortable
209 tok/s 125–334 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	8.0 GB	Q8_0	Comfortable
209 tok/s 125–334 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	8.0 GB	Q8_0	Comfortable
209 tok/s 125–334 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	8.0 GB	Q8_0	Comfortable
159 tok/s 95–255 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	8.0 GB	Q8_0	Comfortable
159 tok/s 95–255 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	8.0 GB	Q8_0	Comfortable
135 tok/s 81–215 · low confidence	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	6.4 GB	Q6_K	Tight
133 tok/s 80–212 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	8.0 GB	Q8_0	Comfortable
130 tok/s 78–208 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	8.0 GB	Q8_0	Comfortable
127 tok/s 76–203 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	8.0 GB	Q8_0	Comfortable
127 tok/s 76–203 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	8.0 GB	Q8_0	Comfortable
127 tok/s 76–203 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	8.0 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: Mahidol University,AI Entrepreneurs Association of Thailand
Organisation type: Academia
Country: Thailand
Published: 8 April 2024
Authors: Kobkrit Viriyayudhakorn, Sumeth Yuenyong, Thaweewat Rugsujarit, Jillaphat Jaroenkantasima, Norapat Buppodom, Koravich Sangkaew, Peerawat Rojratchadakorn, Surapon Nonesung, Chanon Utupon, Sadhis Wongprayoon, Nucharee Thongthungwong, Chawakorn Phiantham, Patteera Triamamornwooth. Nattarika Juntarapaoraya, Kriangkrai Saetan, Pitikorn Khlaisamniang

What it does

The problem areas the model was built for. A model can carry several of each.

Domain: Language
Task: Chat, Language modeling/generation, Question answering, Retrieval-augmented generation
Base model: Llama 2-7B

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: 6.8B
Training data: tokens

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: 2.7 × 10²¹ FLOP
How it was established: Operation counting
Fine-tuning compute: 4.1 × 10¹⁶ FLOP

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

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 7b 1.0.0
Last updated: 11 February 2026

The extremes

The ten fastest GPUs that run OpenThaiGPT v1.0.0 (7B)

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.

The smallest GPUs that still run OpenThaiGPT v1.0.0 (7B)

The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.

What the numbers mean

What it takes to run this model

Minimum card

Tesla K20c

Memory needed

4.4 GB

Fastest

498 tok/s

OpenThaiGPT v1.0.0 (7B) reaches a parameter count of 6.8B. That is small enough that hardware is rarely the obstacle, including on cards several years old. The number of cards we track that can run it: 589.

The smallest card that holds it is Tesla K20c, with a memory capacity of 5 GB, running it at a compression of IQ4_XS and producing around 27.0 tokens per second.

The fastest we calculate for it is B200, generating roughly 498 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

What this model is

OpenThaiGPT v1.0.0 (7B) was published by Mahidol University,AI Entrepreneurs Association of Thailand, in the country recorded as Thailand, during April 2024. The category the publisher falls under is academia.

It works in the domain of Language, and is recorded as performing the task of chat, Language modeling/generation, Question answering, Retrieval-augmented generation.

It builds on Llama 2-7B. That is the usual way a specialised model is produced.

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. On Hugging Face it is published under the organisation openthaigpt.

What decides the speed

Half the cards that hold it manage more than 26.9 tokens per second. Producing text faster than most people read it: 562 of them.

It is a dense model, so every parameter is read for every token produced. That makes speed track memory bandwidth almost exactly — a card with twice the bandwidth generates roughly twice as fast.

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.

How it was trained

Producing it required arithmetic totalling around 2.7 × 10²¹ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.

Step by step

How to choose a GPU for OpenThaiGPT v1.0.0 (7B)

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

Every card here has been checked against OpenThaiGPT v1.0.0 (7B), needing around 4.4 GB at a compression of IQ4_XS. No amount of processing power compensates for a card that cannot hold it.
02

Decide how long your conversations run

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason a card that seemed fine stops fitting OpenThaiGPT v1.0.0 (7B).
03

Choose how far you will compress it

Each card runs the least-compressed copy it can hold, reaching a compression of IQ4_XS on the smallest card that fits. Setting a minimum quality drops the cards that only manage it by squeezing further than you would want, and holds the comparison at one level.
04

Sort by speed

The speed ordering is effectively an ordering by memory bandwidth, for OpenThaiGPT v1.0.0 (7B). It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 498 tok/s.
05

Check the fit verdict before buying

A tight fit runs, but leaves nothing spare for a longer conversation, in the case of OpenThaiGPT v1.0.0 (7B). Comfortable means you can grow the context later. That difference matters more than a few tokens per second, so buy for comfortable if you expect to.
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. A card is usually bought for more than one model, so it is worth a look before buying for OpenThaiGPT v1.0.0 (7B).

Answers

OpenThaiGPT v1.0.0 (7B) — common questions

OpenThaiGPT v1.0.0 (7B)— what is it used for?

It works in the domain of Language, and is recorded as handling the task of chat, Language modeling/generation, Question answering, Retrieval-augmented generation. 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.

OpenThaiGPT v1.0.0 (7B)— where can I download it?

Its weights are published on Hugging Face, under the organisation openthaigpt. We do not host model files — this site calculates what hardware is needed to run them.

OpenThaiGPT v1.0.0 (7B)— how much compute was used to train it?

Training consumed around 2.7 × 10²¹ 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.

OpenThaiGPT v1.0.0 (7B)— can I run it if it does not fit in my GPU?

It can be split between the card and system memory, but it generates painfully slowly that way. The nearest miss we calculate falls short by 1.2 GB. Every figure here assumes the whole model is resident on the card.

OpenThaiGPT v1.0.0 (7B)— would two GPUs run it faster?

Two cards buy memory rather than speed, which matters only if one card cannot hold it. The number that can: 589. So a second card is rarely the answer here.

OpenThaiGPT v1.0.0 (7B)— why does the quantisation differ between cards?

Because capacity varies, so does how hard it has to be squeezed. The number of distinct levels in the table above: 4. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

OpenThaiGPT v1.0.0 (7B)— how accurate are these speed estimates?

These are estimates with real error bars, and any of them could reasonably land anywhere in its published range depending on which runtime you use. The fastest result here: 299–796 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

OpenThaiGPT v1.0.0 (7B)— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Tesla K20c, with a memory capacity of 5 GB. It runs the model at a compression of IQ4_XS using about 4.4 GB, and produces roughly 27.0 tokens per second. The number of cards able to run it in total: 589.

OpenThaiGPT v1.0.0 (7B)— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 498 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and the number of cards clearing that: 562.

OpenThaiGPT v1.0.0 (7B)— how much VRAM does it need?

It needs about 4.4 GB at a compression of IQ4_XS, 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.

OpenThaiGPT v1.0.0 (7B)— can I run it on a GPU holding 8 GB?

Yes. The card CMP 170HX 8 GB, holding 8 GB, runs it at a compression of Q6_K, using about 6.4 GB and generating roughly 135 tokens per second. The fit is tight.

OpenThaiGPT v1.0.0 (7B)— can I run it on a GPU holding 12 GB?

Yes. The card GeForce RTX 3080 Ti, holding 12 GB, runs it at a compression of Q8_0, using about 8.0 GB and generating roughly 56.7 tokens per second. The fit is comfortable.

OpenThaiGPT v1.0.0 (7B)— can I run it on a GPU holding 16 GB?

Yes. The card Tesla V100 SXM2 16 GB, holding 16 GB, runs it at a compression of Q8_0, using about 8.0 GB and generating roughly 70.3 tokens per second. The fit is comfortable.

OpenThaiGPT v1.0.0 (7B)— can I run it on a GPU holding 24 GB?

Yes. The card GeForce RTX 5090 D V2, holding 24 GB, runs it at a compression of Q8_0, using about 8.0 GB and generating roughly 83.3 tokens per second. The fit is comfortable.

OpenThaiGPT v1.0.0 (7B)— is it open source?

Its weights are published, so it 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.

OpenThaiGPT v1.0.0 (7B)— how many parameters does it have?

It has a parameter count of 6.8B. https://huggingface.co/openthaigpt/openthaigpt-1.0.0-7b-chat. 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.

OpenThaiGPT v1.0.0 (7B)— who created it?

It was published by Mahidol University,AI Entrepreneurs Association of Thailand, based in Thailand, an organisation categorised as academia.

OpenThaiGPT v1.0.0 (7B)— when was it released?

It was published in April 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

Source

Original publication

Record last updated 11 February 2026

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