OPT-IML (175B) TPS calculator

Open weights Meta AI 175B parameters December 2022

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

24 cards that can run it

818 cards we hold specifications for

Smallest card that fits

H100 PCIe 96 GB

96 GB · Q3_K_M · 21.9 tok/s

Fastest card

Radeon Instinct MI300

28.6 tok/s · 128 GB

Which GPUs can run OPT-IML (175B)?

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

24 cards match

Calculating

					Needs	Quantisation	Fit
28.6 tok/s 17–46 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	106.6 GB	Q4_K_M	Tight
28.1 tok/s 17–45 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	147.3 GB	Q6_K	Tight
27.3 tok/s 16–44 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	106.6 GB	Q4_K_M	Tight
27.3 tok/s 16–44 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	106.6 GB	Q4_K_M	Tight
23.2 tok/s 14–37 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	106.6 GB	Q4_K_M	Tight
21.9 tok/s 13–35 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	86.2 GB	Q3_K_M	Tight
21.9 tok/s 13–35 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	86.2 GB	Q3_K_M	Tight
19.4 tok/s 12–31 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	188.1 GB	Q8_0	Comfortable
15.5 tok/s 9–25 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	188.1 GB	Q8_0	Comfortable
15.5 tok/s 9–25 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	188.1 GB	Q8_0	Comfortable
14.6 tok/s 9–23 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	147.3 GB	Q6_K	Tight
14.6 tok/s 9–23 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	147.3 GB	Q6_K	Tight
14.3 tok/s 9–23 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	106.6 GB	Q4_K_M	Tight
14.3 tok/s 9–23 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	106.6 GB	Q4_K_M	Tight
11.9 tok/s 7–19 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	106.6 GB	Q4_K_M	Tight
11.7 tok/s 7–19 · low confidence	RTX PRO 6000 Blackwell NVIDIA	96 GB	1,790 GB/s	Mar 2025	86.2 GB	Q3_K_M	Tight
11.7 tok/s 7–19 · low confidence	RTX PRO 6000 Blackwell Max-Q NVIDIA	96 GB	1,790 GB/s	Mar 2025	86.2 GB	Q3_K_M	Tight
11.7 tok/s 7–19 · low confidence	RTX PRO 6000 Blackwell Server NVIDIA	96 GB	1,790 GB/s	Mar 2025	86.2 GB	Q3_K_M	Tight
11.7 tok/s 7–19 · low confidence	RTX PRO 6000D Blackwell Max-Q NVIDIA	96 GB	1,790 GB/s	Mar 2025	86.2 GB	Q3_K_M	Tight
11.7 tok/s 7–19 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	106.6 GB	Q4_K_M	Tight
11.3 tok/s 7–18 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	188.1 GB	Q8_0	Comfortable
10.4 tok/s 6–17 · low confidence	Data Center GPU Max 1350 Intel	96 GB	2,460 GB/s	Jan 2023	86.2 GB	Q3_K_M	Tight
1.5 tok/s 1–2 · low confidence	GB10 NVIDIA	128 GB	273 GB/s	Oct 2025	106.6 GB	Q4_K_M	Tight
1.5 tok/s 1–2 · low confidence	Jetson T5000 NVIDIA	128 GB	273 GB/s	Aug 2025	106.6 GB	Q4_K_M	Tight

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: Meta AI
Organisation type: Industry
Country: United States of America
Published: 22 December 2022
Authors: Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, Todor Mihaylov, Daniel Simig, Ping Yu, Kurt Shuster, Tianlu Wang, Qing Liu, Punit Singh Koura, Xian Li, Brian O'Horo, Gabriel Pereyra, Jeff Wang, Christopher Dewan, Asli Celikyilmaz, Luke Zettlemoyer, Ves Stoyanov

What it does

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

Domain: Language
Task: Language modeling
Base model: OPT-175B

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: 175B
Training data: 2,000,000,000 tokens
Batch size: 125

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: 4.3 × 10²³ FLOP
How it was established: Operation counting
Fine-tuning compute: 2.1 × 10²¹ 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 A100
Chips used: 128
Chip-hours: 9,216
Wall-clock time: 72 hours
Power draw: 102.3 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 (non-commercial)
Training code: Unreleased

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: Likely
Citations: 304

Sources

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

Reference: OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run OPT-IML (175B)

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 OPT-IML (175B)

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

H100 PCIe 96 GB

Memory needed

86.2 GB

Fastest

28.6 tok/s

OPT-IML (175B) sits at 175B parameters, which puts it above consumer hardware and into the range where a card is bought for this purpose rather than repurposed for it. 24 of the cards we track can hold it.

The smallest card that holds it is the H100 PCIe 96 GB with 96 GB, running it at Q3_K_M and producing around 21.9 tokens per second.

A Radeon Instinct MI300 is the fastest we calculate for it: about 28.6 tokens per second, from 6,550 GB/s of memory bandwidth.

Where it came from

OPT-IML (175B) was published by Meta AI, in United States of America, in December 2022. It comes out of industry.

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

It builds on OPT-175B, which is why it shares that model's general shape and size.

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

Understanding the speeds

The median result is around 14.5 tokens per second; 22 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.

Memory here is estimated from size rather than computed from the architecture, which is not recorded for this model — the numbers are indicative rather than exact.

What went into building it

The training run consumed about 4.3 × 10²³ FLOP, on NVIDIA A100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

The training set ran to roughly 2,000,000,000 tokens.

Step by step

How to choose a GPU for OPT-IML (175B)

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

The table lists every card that can hold OPT-IML (175B) — around 86.2 GB at Q3_K_M. That figure, not the card's headline performance, is what decides whether it runs.
02

Match the context to your actual use

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for OPT-IML (175B).
03

Choose how far you will compress it

Compression is what makes OPT-IML (175B) fit smaller cards, at some cost in accuracy — Q3_K_M on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Sort by speed

Sort by speed to see how cards rank for OPT-IML (175B). It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the Radeon Instinct MI300 tops it at 28.6 tok/s.
05

Check the fit verdict before buying

Tight means OPT-IML (175B) 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

Following a card through to its own page shows every other model it can hold, which is the question that follows once OPT-IML (175B) is settled.

Answers

OPT-IML (175B) — common questions

Is OPT-IML (175B) open source?

Its weights are published, so OPT-IML (175B) 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 OPT-IML (175B) have?

OPT-IML (175B) has 175B 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 OPT-IML (175B)?

OPT-IML (175B) was published by Meta AI, based in United States of America, categorised as industry.

When was OPT-IML (175B) released?

OPT-IML (175B) was published in December 2022. 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 OPT-IML (175B) used for?

OPT-IML (175B) works in Language, and is recorded as handling language modeling. 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 OPT-IML (175B)?

The weights for OPT-IML (175B) 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 OPT-IML (175B)?

Around 4.3 × 10²³ FLOP, on NVIDIA A100. 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 OPT-IML (175B) 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 22.0 GB. Our figures for OPT-IML (175B) assume it is fully resident.

Would two GPUs run OPT-IML (175B) faster?

A second card roughly doubles the memory available but not the generation rate. With 24 cards already able to run OPT-IML (175B) alone, the case for pairing is weak.

Why does the quantisation differ between cards for OPT-IML (175B)?

Because capacity varies, so does how hard OPT-IML (175B) has to be squeezed — 4 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these OPT-IML (175B) 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 17–46 tok/s on the Radeon Instinct MI300 rather than a single number.

What GPU do I need to run OPT-IML (175B)?

The smallest card in our catalogue that holds OPT-IML (175B) is the H100 PCIe 96 GB, with 96 GB of memory. It runs the model at Q3_K_M using about 86.2 GB, and produces roughly 21.9 tokens per second. 24 cards in total can run it.

How fast is OPT-IML (175B) on a GPU?

It depends on the card. The quickest we calculate is a Radeon Instinct MI300 at about 28.6 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 22 of the cards that can run OPT-IML (175B) clear that.

How much VRAM does OPT-IML (175B) need?

About 86.2 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.

Source

Original publication

Record last updated 25 May 2026

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