GLM-4-9B-0414 TPS calculator

Open weights Tsinghua University 9B parameters April 2025

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

582 of 818 cards that can run it

Smallest card that fits

Quadro 6000

6 GB · Q3_K_M · 15.5 tok/s

Fastest card

B200

376 tok/s · 180 GB

Which GPUs can run GLM-4-9B-0414?

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

582 cards match

Calculating

					Needs	Quantisation	Fit
376 tok/s 226–602 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	10.3 GB	Q8_0	Comfortable
376 tok/s 226–602 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	10.3 GB	Q8_0	Comfortable
301 tok/s 180–481 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	10.3 GB	Q8_0	Comfortable
301 tok/s 180–481 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	10.3 GB	Q8_0	Comfortable
240 tok/s 144–385 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	10.3 GB	Q8_0	Comfortable
230 tok/s 138–368 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	10.3 GB	Q8_0	Comfortable
230 tok/s 138–368 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	10.3 GB	Q8_0	Comfortable
220 tok/s 132–352 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	10.3 GB	Q8_0	Comfortable
195 tok/s 117–313 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	10.3 GB	Q8_0	Comfortable
195 tok/s 117–313 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	10.3 GB	Q8_0	Comfortable
195 tok/s 117–313 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	10.3 GB	Q8_0	Comfortable
185 tok/s 111–297 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
158 tok/s 95–253 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
158 tok/s 95–253 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	10.3 GB	Q8_0	Comfortable
158 tok/s 95–253 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
158 tok/s 95–253 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
158 tok/s 95–253 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
125 tok/s 75–200 · low confidence	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	7.2 GB	Q5_K_M	Tight
120 tok/s 72–193 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	10.3 GB	Q8_0	Comfortable
120 tok/s 72–193 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	10.3 GB	Q8_0	Comfortable
107 tok/s 64–171 · low confidence	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.2 GB	Q6_K	Tight
100 tok/s 60–161 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	10.3 GB	Q8_0	Comfortable
98.2 tok/s 59–157 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	10.3 GB	Q8_0	Comfortable
96.0 tok/s 58–154 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	10.3 GB	Q8_0	Comfortable
96.0 tok/s 58–154 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	10.3 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: Tsinghua University
Organisation type: Academia
Country: China
Published: 14 April 2025

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, Quantitative reasoning, Code 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: 9B
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: 8.1 × 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: THUDM

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

Sources

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

Reference: GLM-4-9B-0414
Last updated: 28 November 2025

The extremes

The ten fastest GPUs for GLM-4-9B-0414

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 GLM-4-9B-0414

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 you need to run it

Minimum card

Quadro 6000

Memory needed

5.1 GB

Fastest

376 tok/s

GLM-4-9B-0414 is small enough at 9B parameters that hardware is rarely the obstacle — 582 of the cards we track can run it, including cards several years old.

The least hardware that works is a Quadro 6000. Its 6 GB is enough at Q3_K_M compression, giving roughly 15.5 tokens per second.

At the other end, a B200 generates roughly 376 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

What this model is

GLM-4-9B-0414 was published by Tsinghua University, in China, in April 2025. It comes out of academia.

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

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

What decides the speed

The median result is around 21.1 tokens per second; 541 cards produce text faster than most people read it.

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.

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.

Training and provenance

The training run consumed about 8.1 × 10²³ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

Step by step

How to choose a GPU for GLM-4-9B-0414

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 GLM-4-9B-0414 — around 5.1 GB at Q3_K_M. That figure, not the card's headline performance, is what decides whether it runs.
02

Set the context length you will work at

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason GLM-4-9B-0414 stops fitting a card that seemed fine.
03

Choose how far you will compress it

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 GLM-4-9B-0414 by squeezing it further than you would want.
04

Rank by throughput rather than spec sheet

The speed ordering for GLM-4-9B-0414 is effectively an ordering by memory bandwidth, which is why the B200 tops it at 376 tok/s.
05

Read the fit column last

The fit column separates cards that just manage GLM-4-9B-0414 from those with room to spare. Buy for the second if the context might grow.
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 GLM-4-9B-0414 is settled.

Answers

GLM-4-9B-0414 — common questions

Can I run GLM-4-9B-0414 on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q5_K_M, using about 7.2 GB and generating roughly 125 tokens per second — a tight fit.

Can I run GLM-4-9B-0414 on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 10.3 GB and generating roughly 42.9 tokens per second — a tight fit.

Can I run GLM-4-9B-0414 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 10.3 GB and generating roughly 53.2 tokens per second — a comfortable fit.

Can I run GLM-4-9B-0414 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 10.3 GB and generating roughly 63.1 tokens per second — a comfortable fit.

Is GLM-4-9B-0414 open source?

Its weights are published, so GLM-4-9B-0414 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 GLM-4-9B-0414 have?

GLM-4-9B-0414 has 9B parameters. 9B. 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 GLM-4-9B-0414?

GLM-4-9B-0414 was published by Tsinghua University, based in China, categorised as academia.

When was GLM-4-9B-0414 released?

GLM-4-9B-0414 was published in April 2025.

What is GLM-4-9B-0414 used for?

GLM-4-9B-0414 works in Language, and is recorded as handling language modeling/generation, Question answering, Quantitative reasoning, Code generation. 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 GLM-4-9B-0414?

Its weights are published under the THUDM 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 GLM-4-9B-0414?

Around 8.1 × 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 GLM-4-9B-0414 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 1.6 GB. Our figures for GLM-4-9B-0414 assume it is fully resident.

Would two GPUs run GLM-4-9B-0414 faster?

Two cards buy memory rather than speed. That matters for GLM-4-9B-0414 only if one card cannot hold it — 582 can, so a second adds little.

Why does the quantisation differ between cards for GLM-4-9B-0414?

A larger card holds a more accurate copy. Across the cards that run GLM-4-9B-0414, 4 compression levels are used; the floor control above pins it to one.

How accurate are these GLM-4-9B-0414 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 226–602 tok/s on the B200 rather than a single number.

What GPU do I need to run GLM-4-9B-0414?

The smallest card in our catalogue that holds GLM-4-9B-0414 is the Quadro 6000, with 6 GB of memory. It runs the model at Q3_K_M using about 5.1 GB, and produces roughly 15.5 tokens per second. 582 cards in total can run it.

How fast is GLM-4-9B-0414 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 376 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 541 of the cards that can run GLM-4-9B-0414 clear that.

How much VRAM does GLM-4-9B-0414 need?

About 5.1 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 28 November 2025

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