GLM-10B-bidirectional TPS calculator

Open weights Tsinghua University,Beijing Academy of Artificial Intelligence / BAAI,Massachusetts Institute of Technology (MIT) 10B parameters March 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

509 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 5110P

8 GB · Q4_K_M · 20.3 tok/s

Fastest card

B200

339 tok/s · 180 GB

Which GPUs can run GLM-10B-bidirectional?

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

509 cards match

Calculating

					Needs	Quantisation	Fit
339 tok/s 203–542 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	11.4 GB	Q8_0	Comfortable
339 tok/s 203–542 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	11.4 GB	Q8_0	Comfortable
271 tok/s 162–433 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	11.4 GB	Q8_0	Comfortable
271 tok/s 162–433 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	11.4 GB	Q8_0	Comfortable
216 tok/s 130–346 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	11.4 GB	Q8_0	Comfortable
207 tok/s 124–331 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	11.4 GB	Q8_0	Comfortable
207 tok/s 124–331 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	11.4 GB	Q8_0	Comfortable
198 tok/s 119–317 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	11.4 GB	Q8_0	Comfortable
176 tok/s 106–281 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	11.4 GB	Q8_0	Comfortable
176 tok/s 106–281 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	11.4 GB	Q8_0	Comfortable
176 tok/s 106–281 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	11.4 GB	Q8_0	Comfortable
167 tok/s 100–267 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	11.4 GB	Q8_0	Comfortable
146 tok/s 87–233 · low confidence	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	6.7 GB	Q4_K_M	Tight
142 tok/s 85–228 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	11.4 GB	Q8_0	Comfortable
142 tok/s 85–228 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	11.4 GB	Q8_0	Comfortable
142 tok/s 85–228 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	11.4 GB	Q8_0	Comfortable
142 tok/s 85–228 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	11.4 GB	Q8_0	Comfortable
142 tok/s 85–228 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	11.4 GB	Q8_0	Comfortable
118 tok/s 71–189 · low confidence	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	7.9 GB	Q5_K_M	Tight
108 tok/s 65–173 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	11.4 GB	Q8_0	Comfortable
108 tok/s 65–173 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	11.4 GB	Q8_0	Comfortable
90.3 tok/s 54–144 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	11.4 GB	Q8_0	Comfortable
88.4 tok/s 53–141 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	11.4 GB	Q8_0	Comfortable
86.4 tok/s 52–138 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	11.4 GB	Q8_0	Comfortable
86.4 tok/s 52–138 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	11.4 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: Tsinghua University,Beijing Academy of Artificial Intelligence / BAAI,Massachusetts Institute of Technology (MIT)
Organisation type: Academia,Academia,Academia
Country: China, United States of America
Published: 18 March 2021
Authors: Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, Jie Tang

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: 10B
Training data: tokens
Epochs: 1

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: Confident
Citations: 1,919
Benchmark data: GLM-10B-bidirectional

Sources

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

Reference: GLM: General Language Model Pretraining with Autoregressive Blank Infilling
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run GLM-10B-bidirectional

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-10B-bidirectional

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

Xeon Phi 5110P

Memory needed

6.7 GB

Fastest

339 tok/s

GLM-10B-bidirectional is small enough at 10B parameters that hardware is rarely the obstacle — 509 of the cards we track can run it, including cards several years old.

The smallest card that holds it is the Xeon Phi 5110P with 8 GB, running it at Q4_K_M and producing around 20.3 tokens per second.

The quickest result comes from a B200 at around 339 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

What this model is

GLM-10B-bidirectional was published by Tsinghua University,Beijing Academy of Artificial Intelligence / BAAI,Massachusetts Institute of Technology (MIT), in China, in March 2021. academia,Academia,Academia is the category the publisher falls under.

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

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.

What decides the speed

Half the cards that hold it manage more than 22.0 tokens per second, and 469 exceed reading speed outright.

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.

Step by step

How to choose a GPU for GLM-10B-bidirectional

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

Look at what GLM-10B-bidirectional actually needs — around 6.7 GB at Q4_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 GLM-10B-bidirectional stops fitting a card that seemed fine.
03

Set a quality floor

Each card runs the least-compressed copy it can hold — Q4_K_M on the smallest card that fits. Setting a floor drops the cards that only manage GLM-10B-bidirectional by squeezing it further than you would want.
04

Compare tokens per second, not specifications

Sort by speed to see how cards rank for GLM-10B-bidirectional. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 339 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage GLM-10B-bidirectional from those with room to spare. Buy for the second if the context might grow.
06

Check the card from the other side

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond GLM-10B-bidirectional.

Answers

GLM-10B-bidirectional — common questions

What is GLM-10B-bidirectional used for?

GLM-10B-bidirectional works in Language, and is recorded as handling language modeling/generation. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download GLM-10B-bidirectional?

The weights for GLM-10B-bidirectional are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

Can I run GLM-10B-bidirectional 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 GLM-10B-bidirectional is rarely worth using — the nearest miss we calculate is short by 1.3 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run GLM-10B-bidirectional faster?

Two cards buy memory rather than speed. That matters for GLM-10B-bidirectional only if one card cannot hold it — 509 can, so a second adds little.

Why does the quantisation differ between cards for GLM-10B-bidirectional?

Because capacity varies, so does how hard GLM-10B-bidirectional has to be squeezed — 4 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these GLM-10B-bidirectional speed estimates?

These are estimates with real error bars. The fastest result here, 203–542 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

What GPU do I need to run GLM-10B-bidirectional?

The smallest card in our catalogue that holds GLM-10B-bidirectional is the Xeon Phi 5110P, with 8 GB of memory. It runs the model at Q4_K_M using about 6.7 GB, and produces roughly 20.3 tokens per second. 509 cards in total can run it.

How fast is GLM-10B-bidirectional on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 339 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 469 of the cards that can run GLM-10B-bidirectional clear that.

How much VRAM does GLM-10B-bidirectional need?

About 6.7 GB at Q4_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 GLM-10B-bidirectional on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q4_K_M, using about 6.7 GB and generating roughly 146 tokens per second — a tight fit.

Can I run GLM-10B-bidirectional on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q6_K, using about 9.1 GB and generating roughly 56.2 tokens per second — a tight fit.

Can I run GLM-10B-bidirectional on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 11.4 GB and generating roughly 47.9 tokens per second — a comfortable fit.

Can I run GLM-10B-bidirectional on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 11.4 GB and generating roughly 56.8 tokens per second — a comfortable fit.

Is GLM-10B-bidirectional open source?

Its weights are published, so GLM-10B-bidirectional 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-10B-bidirectional have?

GLM-10B-bidirectional has 10B 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 GLM-10B-bidirectional?

GLM-10B-bidirectional was published by Tsinghua University,Beijing Academy of Artificial Intelligence / BAAI,Massachusetts Institute of Technology (MIT), based in China, categorised as academia,Academia,Academia.

When was GLM-10B-bidirectional released?

GLM-10B-bidirectional was published in March 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.

Source

Original publication

Record last updated 25 May 2026

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