Amber TPS calculator

Open weights Mohamed bin Zayed University of Artificial Intelligence (MBZUAI),Petuum,University of Southern California,Carnegie Mellon University (CMU),University of Illinois Urbana-Champaign (UIUC),University of California San Diego,LLM360 6.7B parameters December 2023

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 · 27.4 tok/s

Fastest card

B200

506 tok/s · 180 GB

Which GPUs can run Amber?

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

589 cards match

Calculating

					Needs	Quantisation	Fit
506 tok/s 303–809 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	7.9 GB	Q8_0	Comfortable
506 tok/s 303–809 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	7.9 GB	Q8_0	Comfortable
404 tok/s 242–646 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	7.9 GB	Q8_0	Comfortable
404 tok/s 242–646 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	7.9 GB	Q8_0	Comfortable
323 tok/s 194–517 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	7.9 GB	Q8_0	Comfortable
309 tok/s 185–495 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	7.9 GB	Q8_0	Comfortable
309 tok/s 185–495 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	7.9 GB	Q8_0	Comfortable
296 tok/s 178–473 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	7.9 GB	Q8_0	Comfortable
263 tok/s 158–420 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	7.9 GB	Q8_0	Comfortable
263 tok/s 158–420 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	7.9 GB	Q8_0	Comfortable
263 tok/s 158–420 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	7.9 GB	Q8_0	Comfortable
249 tok/s 149–399 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	7.9 GB	Q8_0	Comfortable
212 tok/s 127–340 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	7.9 GB	Q8_0	Comfortable
212 tok/s 127–340 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	7.9 GB	Q8_0	Comfortable
212 tok/s 127–340 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	7.9 GB	Q8_0	Comfortable
212 tok/s 127–340 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	7.9 GB	Q8_0	Comfortable
212 tok/s 127–340 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	7.9 GB	Q8_0	Comfortable
162 tok/s 97–259 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	7.9 GB	Q8_0	Comfortable
162 tok/s 97–259 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	7.9 GB	Q8_0	Comfortable
137 tok/s 82–219 · low confidence	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	6.3 GB	Q6_K	Tight
135 tok/s 81–216 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	7.9 GB	Q8_0	Comfortable
132 tok/s 79–211 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	7.9 GB	Q8_0	Comfortable
129 tok/s 77–206 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	7.9 GB	Q8_0	Comfortable
129 tok/s 77–206 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	7.9 GB	Q8_0	Comfortable
129 tok/s 77–206 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	7.9 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: Mohamed bin Zayed University of Artificial Intelligence (MBZUAI),Petuum,University of Southern California,Carnegie Mellon University (CMU),University of Illinois Urbana-Champaign (UIUC),University of California San Diego,LLM360
Organisation type: Academia,Industry,Academia,Academia,Academia,Academia,Research collective
Country: United Arab Emirates, United States of America
Published: 11 December 2023
Authors: Zhengzhong Liu, Aurick Qiao, Willie Neiswanger, Hongyi Wang, Bowen Tan, Tianhua Tao, Junbo Li, Yuqi Wang, Suqi Sun, Omkar Pangarkar, Richard Fan, Yi Gu, Victor Miller, Yonghao Zhuang, Guowei He, Haonan Li, Fajri Koto, Liping Tang, Nikhil Ranjan, Zhiqiang Shen, Xuguang Ren, Roberto Iriondo, Cun Mu, Zhiting Hu, Mark Schulze, Preslav Nakov, Tim Baldwin, Eric P. Xing

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
Numerical format: BF16

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: 6.7B
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: 4.8 × 10²² FLOP
How it was established: Operation counting,Hardware

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 A100 SXM4 80 GB
Chips used: 224
Wall-clock time: 601 hours (25 days)
Power draw: 177.6 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 (unrestricted)
Training code: Open source
Hugging Face: LLM360

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: LLM360: Towards Fully Transparent Open-Source LLMs
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Amber

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 Amber

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

506 tok/s

Amber is small enough at 6.7B parameters that hardware is rarely the obstacle — 589 of the cards we track can run it, including cards several years old.

The smallest card that holds it is the Tesla K20c with 5 GB, running it at IQ4_XS and producing around 27.4 tokens per second.

At the other end, a B200 generates roughly 506 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

Background

Amber was published by Mohamed bin Zayed University of Artificial Intelligence (MBZUAI),Petuum,University of Southern California,Carnegie Mellon University (CMU),University of Illinois Urbana-Champaign (UIUC),University of California San Diego,LLM360, in United Arab Emirates, in December 2023. The organisation is categorised as academia,Industry,Academia,Academia,Academia,Academia,Research collective.

It works in Language, and is recorded as doing language modeling/generation, Question answering.

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. It is published under the LLM360 organisation on Hugging Face.

Reading the throughput figures

Across every card that can run it, the middle of the range is about 27.3 tokens per second, and 562 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.

How it was trained

Training it took roughly 4.8 × 10²² FLOP of computation, on NVIDIA A100 SXM4 80 GB — a measure of what producing the model cost, not of how fast it answers.

Step by step

How to choose a GPU for Amber

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 Amber actually needs — around 4.4 GB at IQ4_XS. No amount of processing power compensates for a card that cannot hold it.
02

Match the context to your actual use

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason Amber stops fitting a card that seemed fine.
03

Choose how far you will compress it

Compression is what makes Amber fit smaller cards, at some cost in accuracy — IQ4_XS on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Rank by throughput rather than spec sheet

Sort by speed to see how cards rank for Amber. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 506 tok/s.
05

Check the fit verdict before buying

Tight means Amber 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 Amber alone — a card is usually bought for more than one model.

Answers

Amber — common questions

How much VRAM does Amber need?

About 4.4 GB at IQ4_XS 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 Amber on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q6_K, using about 6.3 GB and generating roughly 137 tokens per second — a tight fit.

Can I run Amber on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 7.9 GB and generating roughly 57.7 tokens per second — a comfortable fit.

Can I run Amber on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 7.9 GB and generating roughly 71.4 tokens per second — a comfortable fit.

Can I run Amber on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 7.9 GB and generating roughly 84.7 tokens per second — a comfortable fit.

Is Amber open source?

Its weights are published, so Amber 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 Amber have?

Amber has 6.7B parameters. 6.7B We used the exact same model architecture as LLaMA 7B. 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 Amber?

Amber was published by Mohamed bin Zayed University of Artificial Intelligence (MBZUAI),Petuum,University of Southern California,Carnegie Mellon University (CMU),University of Illinois Urbana-Champaign (UIUC),University of California San Diego,LLM360, based in United Arab Emirates, categorised as academia,Industry,Academia,Academia,Academia,Academia,Research collective.

When was Amber released?

Amber was published in December 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 Amber used for?

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

Where can I download Amber?

Its weights are published under the LLM360 organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.

How much compute was used to train Amber?

Around 4.8 × 10²² FLOP, on NVIDIA A100 SXM4 80 GB. 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 Amber 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 1.2 GB. Our figures for Amber assume it is fully resident.

Would two GPUs run Amber faster?

Capacity adds across cards; throughput does not. Since 589 of the cards we track already hold Amber on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for Amber?

Because capacity varies, so does how hard Amber has to be squeezed — 4 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these Amber speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 303–809 tok/s on the B200, for instance. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

What GPU do I need to run Amber?

The smallest card in our catalogue that holds Amber is the Tesla K20c, with 5 GB of memory. It runs the model at IQ4_XS using about 4.4 GB, and produces roughly 27.4 tokens per second. 589 cards in total can run it.

How fast is Amber on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 506 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 562 of the cards that can run Amber clear that.

Source

Original publication

Record last updated 28 November 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.

All AI models