GLIDE TPS calculator

Open weights OpenAI 3.5B parameters December 2021

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

818 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla C1080

4 GB · Q5_K_M · 18.8 tok/s

Fastest card

B200

968 tok/s · 180 GB

Which GPUs can run GLIDE?

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

818 cards match

Calculating

					Needs	Quantisation	Fit
968 tok/s 581–1,549 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	4.4 GB	Q8_0	Comfortable
968 tok/s 581–1,549 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	4.4 GB	Q8_0	Comfortable
773 tok/s 464–1,237 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	4.4 GB	Q8_0	Comfortable
773 tok/s 464–1,237 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	4.4 GB	Q8_0	Comfortable
618 tok/s 371–989 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	4.4 GB	Q8_0	Comfortable
592 tok/s 355–947 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	4.4 GB	Q8_0	Comfortable
592 tok/s 355–947 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	4.4 GB	Q8_0	Comfortable
566 tok/s 340–906 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	4.4 GB	Q8_0	Comfortable
503 tok/s 302–804 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	4.4 GB	Q8_0	Comfortable
503 tok/s 302–804 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	4.4 GB	Q8_0	Comfortable
503 tok/s 302–804 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	4.4 GB	Q8_0	Comfortable
477 tok/s 286–763 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	4.4 GB	Q8_0	Comfortable
407 tok/s 244–651 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	4.4 GB	Q8_0	Comfortable
407 tok/s 244–651 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	4.4 GB	Q8_0	Comfortable
407 tok/s 244–651 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	4.4 GB	Q8_0	Comfortable
407 tok/s 244–651 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	4.4 GB	Q8_0	Comfortable
407 tok/s 244–651 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	4.4 GB	Q8_0	Comfortable
310 tok/s 186–495 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	4.4 GB	Q8_0	Comfortable
310 tok/s 186–495 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	4.4 GB	Q8_0	Comfortable
258 tok/s 155–413 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	4.4 GB	Q8_0	Comfortable
252 tok/s 151–404 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	4.4 GB	Q8_0	Comfortable
247 tok/s 148–395 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	4.4 GB	Q8_0	Comfortable
247 tok/s 148–395 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	4.4 GB	Q8_0	Comfortable
247 tok/s 148–395 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	4.4 GB	Q8_0	Comfortable
247 tok/s 148–395 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	4.4 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: OpenAI
Organisation type: Industry
Country: United States of America
Published: 20 December 2021
Authors: Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, Mark Chen

What it does

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

Domain: Image generation
Task: Image generation, Text-to-image
Numerical format: FP16

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: 3.5B
Training data: 7,602,176,000,000 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: 4.7 × 10²² FLOP
How it was established: Comparison with other models

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

How it is classified

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

Record confidence: Speculative
Citations: 4,722

Sources

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

Reference: GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run GLIDE

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 GLIDE

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

Tesla C1080

Memory needed

3.2 GB

Fastest

968 tok/s

GLIDE is small enough at 3.5B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

At the low end, a Tesla C1080 handles it — 4 GB, at Q5_K_M, for about 18.8 tokens per second.

The quickest result comes from a B200 at around 968 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

About this model

GLIDE was published by OpenAI, in United States of America, in December 2021. The organisation is categorised as industry.

It works in Image generation, and is recorded as doing image generation, Text-to-image.

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.

How fast it runs, and why

Across every card that can run it, the middle of the range is about 32.5 tokens per second, and 778 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.

Without the attention layout on record, the memory column is an approximation. It is close enough to choose hardware by, and least reliable at long context.

Training and provenance

Training it took roughly 4.7 × 10²² FLOP of computation — a measure of what producing the model cost, not of how fast it answers.

It was trained on about 7,602,176,000,000 tokens of text.

Step by step

How to choose a GPU for GLIDE

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

Look at what GLIDE actually needs — around 3.2 GB at Q5_K_M. 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 GLIDE stops fitting a card that seemed fine.
03

Set a quality floor

Each card runs the least-compressed copy it can hold — Q5_K_M on the smallest card that fits. Setting a floor drops the cards that only manage GLIDE by squeezing it further than you would want.
04

Rank by throughput rather than spec sheet

Ranking by tokens per second for GLIDE follows memory bandwidth, not core counts, which is why the B200 tops it at 968 tok/s.
05

Read the fit column last

A tight fit runs GLIDE but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

Open the card you have settled on

Following a card through to its own page shows every other model it can hold, which is the question that follows once GLIDE is settled.

Answers

GLIDE — common questions

How fast is GLIDE on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 968 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 778 of the cards that can run GLIDE clear that.

How much VRAM does GLIDE need?

About 3.2 GB at Q5_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 GLIDE on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 4.4 GB and generating roughly 180 tokens per second — a comfortable fit.

Can I run GLIDE on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 4.4 GB and generating roughly 110 tokens per second — a comfortable fit.

Can I run GLIDE on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 4.4 GB and generating roughly 137 tokens per second — a comfortable fit.

Can I run GLIDE on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 4.4 GB and generating roughly 162 tokens per second — a comfortable fit.

Is GLIDE open source?

Its weights are published, so GLIDE 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 GLIDE have?

GLIDE has 3.5B parameters. "Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking". 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 GLIDE?

GLIDE was published by OpenAI, based in United States of America, categorised as industry.

When was GLIDE released?

GLIDE was published in December 2021. 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 GLIDE used for?

GLIDE works in Image generation, and is recorded as handling image generation, Text-to-image. 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 GLIDE?

The weights for GLIDE are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

How much compute was used to train GLIDE?

Around 4.7 × 10²² 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 GLIDE 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 GLIDE is rarely worth using. Every figure here assumes the whole model is on the card.

Would two GPUs run GLIDE faster?

Capacity adds across cards; throughput does not. Since 818 of the cards we track already hold GLIDE on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for GLIDE?

Because capacity varies, so does how hard GLIDE has to be squeezed — 2 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these GLIDE speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 581–1,549 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 GLIDE?

The smallest card in our catalogue that holds GLIDE is the Tesla C1080, with 4 GB of memory. It runs the model at Q5_K_M using about 3.2 GB, and produces roughly 18.8 tokens per second. 818 cards in total can run it.

Source

Original publication

Record last updated 25 May 2026

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