BLOOMZ-176B TPS calculator

Open weights Hugging Face 176B parameters November 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

17 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Radeon Instinct MI250

128 GB · Q4_K_M · 14.2 tok/s

Fastest card

Radeon Instinct MI300

28.4 tok/s · 128 GB

Which GPUs can run BLOOMZ-176B?

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

17 cards match

Calculating

					Needs	Quantisation	Fit
28.4 tok/s 17–45 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	107.2 GB	Q4_K_M	Tight
28.0 tok/s 17–45 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	148.2 GB	Q6_K	Tight
27.2 tok/s 16–43 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	107.2 GB	Q4_K_M	Tight
27.2 tok/s 16–43 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	107.2 GB	Q4_K_M	Tight
23.1 tok/s 14–37 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	107.2 GB	Q4_K_M	Tight
19.3 tok/s 12–31 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	189.1 GB	Q8_0	Comfortable
15.4 tok/s 9–25 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	189.1 GB	Q8_0	Comfortable
15.4 tok/s 9–25 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	189.1 GB	Q8_0	Comfortable
14.5 tok/s 9–23 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	148.2 GB	Q6_K	Tight
14.5 tok/s 9–23 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	148.2 GB	Q6_K	Tight
14.2 tok/s 9–23 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	107.2 GB	Q4_K_M	Tight
14.2 tok/s 9–23 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	107.2 GB	Q4_K_M	Tight
11.8 tok/s 7–19 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	107.2 GB	Q4_K_M	Tight
11.6 tok/s 7–19 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	107.2 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	189.1 GB	Q8_0	Comfortable
1.5 tok/s 1–2 · low confidence	GB10 NVIDIA	128 GB	273 GB/s	Oct 2025	107.2 GB	Q4_K_M	Tight
1.5 tok/s 1–2 · low confidence	Jetson T5000 NVIDIA	128 GB	273 GB/s	Aug 2025	107.2 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: Hugging Face
Organisation type: Industry
Country: United States of America
Published: 3 November 2022
Authors: Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M Saiful Bari, Sheng Shen, Zheng-Xin Yong, Hailey Schoelkopf, Xiangru Tang, Dragomir Radev, Alham Fikri Aji, Khalid Almubarak, Samuel Albanie, Zaid Alyafeai, Albert Webson, Edward Raff, Colin Raffel

What it does

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

Domain: Language
Task: Translation, Language modeling/generation
Base model: BLOOM-176B

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: 176B
Training data: 20,000,000,000 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.

Fine-tuning compute: 1.4 × 10²² FLOP

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

How it is classified

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

Record confidence: Likely
Citations: 242

Sources

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

Reference: Crosslingual Generalization through Multitask Finetuning
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run BLOOMZ-176B

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 BLOOMZ-176B

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

Radeon Instinct MI250

Memory needed

107.2 GB

Fastest

28.4 tok/s

BLOOMZ-176B reaches a parameter count of 176B. That puts it above consumer hardware, into the range where a card is bought for this purpose rather than repurposed for it. The number of cards we track that can hold it: 17.

The smallest card that holds it is Radeon Instinct MI250, with a memory capacity of 128 GB, running it at a compression of Q4_K_M and producing around 14.2 tokens per second.

The fastest we calculate for it is Radeon Instinct MI300, generating roughly 28.4 tokens per second on the strength of a memory bandwidth of 6,550 GB/s.

Background

BLOOMZ-176B was published by Hugging Face, in the country recorded as United States of America, during November 2022. It comes out of an organisation categorised as industry.

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

Rather than being trained from scratch, it is derived from BLOOM-176B. Most models at this scale are adapted from an existing base rather than built from nothing.

Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all.

Reading the throughput figures

Half the cards that hold it manage more than 14.5 tokens per second. Producing text faster than most people read it: 15 of them.

It is a dense model, so every parameter is read for every token produced. That makes speed track memory bandwidth almost exactly — a card with twice the bandwidth generates roughly twice as fast.

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.

What went into building it

It was trained on a corpus of about 20,000,000,000 tokens of text.

Step by step

How to choose a GPU for BLOOMZ-176B

The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.

01

Read the memory figure first

The table lists every card able to hold BLOOMZ-176B, needing around 107.2 GB at a compression of Q4_K_M. That figure, not the headline performance of a card, is what decides whether it runs.
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 a card that seemed fine stops fitting BLOOMZ-176B.
03

Decide how much compression you will accept

Compression is what makes a model fit smaller cards, at some cost in accuracy, reaching a compression of Q4_K_M 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

The speed ordering is effectively an ordering by memory bandwidth, for BLOOMZ-176B. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is Radeon Instinct MI300, at 28.4 tok/s.
05

Look at the headroom, not just the fit

Tight means it loads and works with no room to raise the context later, in the case of BLOOMZ-176B. 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

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 you have settled on BLOOMZ-176B.

Answers

BLOOMZ-176B — common questions

BLOOMZ-176B— 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.

BLOOMZ-176B— how many parameters does it have?

It has a parameter count of 176B. 176B. 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.

BLOOMZ-176B— who created it?

It was published by Hugging Face, based in United States of America, an organisation categorised as industry.

BLOOMZ-176B— when was it released?

It was published in November 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.

BLOOMZ-176B— what is it used for?

It works in the domain of Language, and is recorded as handling the task of translation, Language modeling/generation. These are the areas it was designed around; they describe intent rather than a hard boundary.

BLOOMZ-176B— where can I download it?

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

BLOOMZ-176B— can I run it if it does not fit in my GPU?

It can be split between the card and system memory, but it generates painfully slowly that way. The nearest miss we calculate falls short by 20.8 GB. Every figure here assumes the whole model is resident on the card.

BLOOMZ-176B— 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: 17. So a second card is rarely the answer here.

BLOOMZ-176B— 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: 3. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

BLOOMZ-176B— 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: 17–45 tok/s on Radeon Instinct MI300. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

BLOOMZ-176B— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Radeon Instinct MI250, with a memory capacity of 128 GB. It runs the model at a compression of Q4_K_M using about 107.2 GB, and produces roughly 14.2 tokens per second. The number of cards able to run it in total: 17.

BLOOMZ-176B— how fast is it on a GPU?

It depends on the card. The quickest we calculate is Radeon Instinct MI300, at about 28.4 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: 15.

BLOOMZ-176B— how much VRAM does it need?

It needs about 107.2 GB at a compression of Q4_K_M, 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 28 November 2025

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