Granite 20B TPS calculator

Open weights IBM Research 20B parameters May 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

293 of 818 cards that can run it

Smallest card that fits

Quadro K6000

12 GB · Q3_K_M · 14.0 tok/s

Fastest card

B200

169 tok/s · 180 GB

Which GPUs can run Granite 20B?

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

293 cards match

Calculating

					Needs	Quantisation	Fit
169 tok/s 102–271 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	22.1 GB	Q8_0	Comfortable
169 tok/s 102–271 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	22.1 GB	Q8_0	Comfortable
135 tok/s 81–216 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	22.1 GB	Q8_0	Comfortable
135 tok/s 81–216 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	22.1 GB	Q8_0	Comfortable
108 tok/s 65–173 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	22.1 GB	Q8_0	Comfortable
104 tok/s 62–166 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	22.1 GB	Q8_0	Comfortable
104 tok/s 62–166 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	22.1 GB	Q8_0	Comfortable
99.1 tok/s 59–159 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	22.1 GB	Q8_0	Comfortable
88.0 tok/s 53–141 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	22.1 GB	Q8_0	Comfortable
88.0 tok/s 53–141 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	22.1 GB	Q8_0	Comfortable
88.0 tok/s 53–141 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	22.1 GB	Q8_0	Comfortable
83.4 tok/s 50–134 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	22.1 GB	Q8_0	Comfortable
71.2 tok/s 43–114 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	22.1 GB	Q8_0	Comfortable
71.2 tok/s 43–114 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	22.1 GB	Q8_0	Comfortable
71.2 tok/s 43–114 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	22.1 GB	Q8_0	Comfortable
71.2 tok/s 43–114 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	22.1 GB	Q8_0	Comfortable
71.2 tok/s 43–114 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	22.1 GB	Q8_0	Comfortable
55.2 tok/s 33–88 · low confidence	Tesla V100 SXM2 16 GB NVIDIA	16 GB	1,130 GB/s	Nov 2019	12.8 GB	Q4_K_M	Tight
54.2 tok/s 33–87 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	22.1 GB	Q8_0	Comfortable
54.2 tok/s 33–87 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	22.1 GB	Q8_0	Comfortable
52.1 tok/s 31–83 · low confidence	GeForce RTX 3080 12 GB NVIDIA	12 GB	912 GB/s	Jan 2022	10.5 GB	Q3_K_M	Tight
52.1 tok/s 31–83 · low confidence	GeForce RTX 3080 Ti NVIDIA	12 GB	912 GB/s	May 2021	10.5 GB	Q3_K_M	Tight
46.9 tok/s 28–75 · low confidence	GeForce RTX 5080 NVIDIA	16 GB	960 GB/s	Jan 2025	12.8 GB	Q4_K_M	Tight
45.2 tok/s 27–72 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	22.1 GB	Q8_0	Comfortable
44.2 tok/s 27–71 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	22.1 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: IBM Research
Organisation type: Industry
Country: United States of America
Published: 31 May 2024
Authors: IBM Research

What it does

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

Domain: Language
Task: Language modeling/generation

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: 20B
Training data: 2,500,000,000,000 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: 3 × 10²³ FLOP
How it was established: Operation counting

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: Unreleased
Hugging Face: ibm-granite

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Likely above 10²³ FLOP: Yes
Record confidence: Confident

Sources

Where this record came from and when it was last checked.

Reference: Granite Foundation Models
Last updated: 28 November 2025

The extremes

The ten fastest GPUs for Granite 20B

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 Granite 20B

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

Quadro K6000

Memory needed

10.5 GB

Fastest

169 tok/s

With 20B parameters, Granite 20B lands in the range a serious desktop card can handle once the weights are compressed. 293 of the cards we track can run it.

At the low end, a Quadro K6000 handles it — 12 GB, at Q3_K_M, for about 14.0 tokens per second.

The quickest result comes from a B200 at around 169 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

About this model

Granite 20B was published by IBM Research, in United States of America, in May 2024. industry is the category the publisher falls under.

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

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 ibm-granite organisation on Hugging Face.

How fast it runs, and why

The median result is around 20.6 tokens per second; 248 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.

Training and provenance

Producing it required around 3 × 10²³ FLOP of arithmetic, which is a statement about the training budget rather than about inference.

It was trained on about 2,500,000,000,000 tokens of text.

Step by step

How to choose a GPU for Granite 20B

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 Granite 20B — around 10.5 GB at Q3_K_M. Capacity is the gate — a card either holds it or it does not.
02

Match the context to your actual use

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context Granite 20B can slip off a card that handles short questions easily.
03

Decide how much compression you will accept

Each card runs the least-compressed copy it can hold — Q3_K_M on the smallest card that fits. Setting a floor drops the cards that only manage Granite 20B by squeezing it further than you would want.
04

Rank by throughput rather than spec sheet

Ranking by tokens per second for Granite 20B follows memory bandwidth, not core counts, which is why the B200 tops it at 169 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs Granite 20B but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
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 Granite 20B alone — a card is usually bought for more than one model.

Answers

Granite 20B — common questions

When was Granite 20B released?

Granite 20B was published in May 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.

What is Granite 20B used for?

Granite 20B works in Language, and is recorded as handling language modeling/generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Granite 20B?

Its weights are published under the ibm-granite 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 Granite 20B?

Around 3 × 10²³ 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 Granite 20B 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 Granite 20B is rarely worth using — the nearest miss we calculate is short by 2.9 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run Granite 20B faster?

Capacity adds across cards; throughput does not. Since 293 of the cards we track already hold Granite 20B on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for Granite 20B?

Because capacity varies, so does how hard Granite 20B has to be squeezed — 4 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these Granite 20B 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 102–271 tok/s on the B200 rather than a single number.

What GPU do I need to run Granite 20B?

The smallest card in our catalogue that holds Granite 20B is the Quadro K6000, with 12 GB of memory. It runs the model at Q3_K_M using about 10.5 GB, and produces roughly 14.0 tokens per second. 293 cards in total can run it.

How fast is Granite 20B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 169 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 248 of the cards that can run Granite 20B clear that.

How much VRAM does Granite 20B need?

About 10.5 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.

Can I run Granite 20B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q3_K_M, using about 10.5 GB and generating roughly 52.1 tokens per second — a tight fit.

Can I run Granite 20B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q4_K_M, using about 12.8 GB and generating roughly 55.2 tokens per second — a tight fit.

Can I run Granite 20B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q6_K, using about 17.5 GB and generating roughly 41.2 tokens per second — a comfortable fit.

Is Granite 20B open source?

Its weights are published, so Granite 20B 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 Granite 20B have?

Granite 20B has 20B parameters. 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 Granite 20B?

Granite 20B was published by IBM Research, based in United States of America, categorised as industry.

Source

Original publication

Record last updated 28 November 2025

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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.

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