Gemma2 9B CPT Sahabat-AI TPS calculator

Open weights Indosat,Tech Mahindra,AI Singapore,GoTo 9B parameters November 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

509 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 5110P

8 GB · IQ4_XS · 24.0 tok/s

Fastest card

B200

376 tok/s · 180 GB

Which GPUs can run Gemma2 9B CPT Sahabat-AI?

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.

Search cards

Card memory

Also show cards that cannot run it

509 cards match

Calculating

					Needs	Quantisation	Fit
376 tok/s 320–452	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	11.7 GB	Q8_0	Comfortable
376 tok/s 320–452	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	11.7 GB	Q8_0	Comfortable
301 tok/s 180–481 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	11.7 GB	Q8_0	Comfortable
301 tok/s 180–481 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	11.7 GB	Q8_0	Comfortable
240 tok/s 144–385 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	11.7 GB	Q8_0	Comfortable
230 tok/s 196–276	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	11.7 GB	Q8_0	Comfortable
230 tok/s 196–276	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	11.7 GB	Q8_0	Comfortable
220 tok/s 132–352 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	11.7 GB	Q8_0	Comfortable
195 tok/s 117–313 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	11.7 GB	Q8_0	Comfortable
195 tok/s 117–313 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	11.7 GB	Q8_0	Comfortable
195 tok/s 117–313 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	11.7 GB	Q8_0	Comfortable
185 tok/s 158–222	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	11.7 GB	Q8_0	Comfortable
172 tok/s 146–207	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	7.0 GB	IQ4_XS	Tight
158 tok/s 134–190	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	11.7 GB	Q8_0	Comfortable
158 tok/s 134–190	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	11.7 GB	Q8_0	Comfortable
158 tok/s 134–190	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	11.7 GB	Q8_0	Comfortable
158 tok/s 134–190	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	11.7 GB	Q8_0	Comfortable
158 tok/s 134–190	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	11.7 GB	Q8_0	Comfortable
131 tok/s 111–157	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.6 GB	Q5_K_M	Tight
120 tok/s 72–193 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	11.7 GB	Q8_0	Comfortable
120 tok/s 72–193 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	11.7 GB	Q8_0	Comfortable
100 tok/s 60–161 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	11.7 GB	Q8_0	Comfortable
98.2 tok/s 59–157 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	11.7 GB	Q8_0	Comfortable
96.0 tok/s 82–115	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	11.7 GB	Q8_0	Comfortable
96.0 tok/s 82–115	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	11.7 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: Indosat,Tech Mahindra,AI Singapore,GoTo
Organisation type: Industry,Industry,Government,Government
Country: Indonesia, India, Singapore
Published: 14 November 2024
Authors: AI Singapore: Chan Adwin, Cheng Nicholas, Choa Esther, Huang Yuli, Lau Wayne, Lee Chwan Ren, Leong Wai Yi, Leong Wei Qi, Limkonchotiwat Peerat, Liu Bing Jie Darius, Montalan Jann Railey, Ng Boon Cheong Raymond, Ngui Jian Gang, Nguyen Thanh Ngan, Ong Brandon, Ong Tat-Wee David, Ong Zhi Hao, Rengarajan Hamsawardhini, Siow Bryan, Susanto Yosephine, Tai Ngee Chia, Tan Choon Meng, Teng Walter, Teo Eng…

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: Gemma 2 9B

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: 9B
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.

How it was established: Hardware
Fine-tuning compute: 5.1 × 10¹⁹ FLOP

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
Wall-clock time: 6 hours
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: GoToCompany

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: The First Large Language Model In Bahasa Indonesia Developed by Indonesians
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Gemma2 9B CPT Sahabat-AI

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 Gemma2 9B CPT Sahabat-AI

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

The hardware side

Minimum card

Xeon Phi 5110P

Memory needed

7.0 GB

Fastest

376 tok/s

Gemma2 9B CPT Sahabat-AI reaches a parameter count of 9B. 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: 509.

At the low end it is handled by Xeon Phi 5110P, with a memory capacity of 8 GB, running it at a compression of IQ4_XS and producing around 24.0 tokens per second.

The fastest we calculate for it is B200, generating roughly 376 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

About this model

Gemma2 9B CPT Sahabat-AI was published by Indosat,Tech Mahindra,AI Singapore,GoTo, in the country recorded as Indonesia, during November 2024. The category the publisher falls under is industry,Industry,Government,Government.

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

It builds on Gemma 2 9B. That is why it shares the base model's general shape and size.

The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold. On Hugging Face it is published under the organisation GoToCompany.

How fast it runs, and why

The median result is around 26.0 tokens per second. Exceeding reading speed outright: 473 of them.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

Its attention layout is on file, so the memory figures are computed exactly rather than approximated.

Step by step

How to choose a GPU for Gemma2 9B CPT Sahabat-AI

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

Every card here has been checked against Gemma2 9B CPT Sahabat-AI, needing around 7.0 GB at a compression of IQ4_XS. 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 Gemma2 9B CPT Sahabat-AI.
03

Set a quality floor

Compression is what makes a model fit smaller cards, at some cost in accuracy, 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

Compare tokens per second, not specifications

Sort by speed to see how cards rank for Gemma2 9B CPT Sahabat-AI. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 376 tok/s.
05

Check the fit verdict before buying

Tight means it loads and works with no room to raise the context later, in the case of Gemma2 9B CPT Sahabat-AI. 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 Gemma2 9B CPT Sahabat-AI.

Answers

Gemma2 9B CPT Sahabat-AI — common questions

Gemma2 9B CPT Sahabat-AI— can I run it if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part held in system memory drags the whole thing down. The nearest miss we calculate falls short by 2.2 GB. Every figure here assumes the whole model is resident on the card.

Gemma2 9B CPT Sahabat-AI— would two GPUs run it faster?

A second card roughly doubles the memory available but not the generation rate. The number already able to run it alone: 509. So a second card is rarely the answer here.

Gemma2 9B CPT Sahabat-AI— 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.

Gemma2 9B CPT Sahabat-AI— how accurate are these speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked, which is why each is published as a range rather than a single number. One example: 320–452 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Gemma2 9B CPT Sahabat-AI— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Xeon Phi 5110P, with a memory capacity of 8 GB. It runs the model at a compression of IQ4_XS using about 7.0 GB, and produces roughly 24.0 tokens per second. The number of cards able to run it in total: 509.

Gemma2 9B CPT Sahabat-AI— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 376 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: 473.

Gemma2 9B CPT Sahabat-AI— how much VRAM does it need?

It needs about 7.0 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.

Gemma2 9B CPT Sahabat-AI— 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 IQ4_XS, using about 7.0 GB and generating roughly 172 tokens per second. The fit is tight.

Gemma2 9B CPT Sahabat-AI— 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 Q6_K, using about 9.6 GB and generating roughly 62.4 tokens per second. The fit is tight.

Gemma2 9B CPT Sahabat-AI— 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 11.7 GB and generating roughly 53.2 tokens per second. The fit is comfortable.

Gemma2 9B CPT Sahabat-AI— 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 11.7 GB and generating roughly 63.1 tokens per second. The fit is comfortable.

Gemma2 9B CPT Sahabat-AI— 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.

Gemma2 9B CPT Sahabat-AI— how many parameters does it have?

It has a parameter count of 9B. 9B. 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.

Gemma2 9B CPT Sahabat-AI— who created it?

It was published by Indosat,Tech Mahindra,AI Singapore,GoTo, based in Indonesia, an organisation categorised as industry,Industry,Government,Government.

Gemma2 9B CPT Sahabat-AI— when was it released?

It was published in November 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.

Gemma2 9B CPT Sahabat-AI— what is it used for?

It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Gemma2 9B CPT Sahabat-AI— where can I download it?

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

Source

Original publication

Record last updated 28 November 2025

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