BTLM-3B TPS calculator

Open weights Cerebras Systems 2.6B parameters September 2023

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 · Q8_0 · 14.2 tok/s

Fastest card

B200

1,303 tok/s · 180 GB

Which GPUs can run BTLM-3B?

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
1,303 tok/s 782–2,085 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	3.5 GB	Q8_0	Comfortable
1,303 tok/s 782–2,085 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	3.5 GB	Q8_0	Comfortable
1,041 tok/s 624–1,665 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	3.5 GB	Q8_0	Comfortable
1,041 tok/s 624–1,665 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	3.5 GB	Q8_0	Comfortable
832 tok/s 499–1,332 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	3.5 GB	Q8_0	Comfortable
797 tok/s 478–1,275 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.5 GB	Q8_0	Comfortable
797 tok/s 478–1,275 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.5 GB	Q8_0	Comfortable
762 tok/s 457–1,220 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	3.5 GB	Q8_0	Comfortable
677 tok/s 406–1,083 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	3.5 GB	Q8_0	Comfortable
677 tok/s 406–1,083 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	3.5 GB	Q8_0	Comfortable
677 tok/s 406–1,083 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	3.5 GB	Q8_0	Comfortable
642 tok/s 385–1,027 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	3.5 GB	Q8_0	Comfortable
547 tok/s 328–876 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.5 GB	Q8_0	Comfortable
547 tok/s 328–876 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	3.5 GB	Q8_0	Comfortable
547 tok/s 328–876 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	3.5 GB	Q8_0	Comfortable
547 tok/s 328–876 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.5 GB	Q8_0	Comfortable
547 tok/s 328–876 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	3.5 GB	Q8_0	Comfortable
417 tok/s 250–667 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	3.5 GB	Q8_0	Comfortable
417 tok/s 250–667 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	3.5 GB	Q8_0	Comfortable
347 tok/s 208–556 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	3.5 GB	Q8_0	Comfortable
340 tok/s 204–544 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	3.5 GB	Q8_0	Comfortable
332 tok/s 199–532 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	3.5 GB	Q8_0	Comfortable
332 tok/s 199–532 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	3.5 GB	Q8_0	Comfortable
332 tok/s 199–532 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	3.5 GB	Q8_0	Comfortable
332 tok/s 199–532 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	3.5 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: Cerebras Systems
Organisation type: Industry
Country: United States of America
Published: 20 September 2023
Authors: Nolan Dey, Daria Soboleva, Faisal Al-Khateeb, Bowen Yang, Ribhu Pathria, Hemant Khachane, Shaheer Muhammad, Zhiming (Charles) Chen, Robert Myers, Jacob Robert Steeves, Natalia Vassilieva, Marvin Tom, Joel Hestness

What it does

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

Domain: Language
Task: Language generation, 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: 2.6B
Training data: 627,000,000,000 tokens
Epochs: 1
Batch size: 3,932,160

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: 9.8 × 10²¹ FLOP
How it was established: Operation counting

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: Cerebras CS-2

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: Confident

Sources

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

Reference: BTLM-3B-8K: 7B Parameter Performance in a 3B Parameter Model
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run BTLM-3B

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 BTLM-3B

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

Tesla C1080

Memory needed

3.5 GB

Fastest

1,303 tok/s

BTLM-3B is small enough at 2.6B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The least hardware that works is a Tesla C1080. Its 4 GB is enough at Q8_0 compression, giving roughly 14.2 tokens per second.

At the other end, a B200 generates roughly 1,303 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

Where it came from

BTLM-3B was published by Cerebras Systems, in United States of America, in September 2023. industry is the category the publisher falls under.

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

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

Understanding the speeds

Across every card that can run it, the middle of the range is about 36.6 tokens per second, and 778 of them clear the ten tokens per second that roughly matches reading speed.

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.

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.

What went into building it

Producing it required around 9.8 × 10²¹ FLOP of arithmetic, on Cerebras CS-2, which is a statement about the training budget rather than about inference.

Around 627,000,000,000 tokens went into training it.

Step by step

How to choose a GPU for BTLM-3B

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

Every card here has been checked against BTLM-3B — around 3.5 GB at Q8_0. Capacity is the gate — a card either holds it or it does not.
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 BTLM-3B.
03

Choose how far you will compress it

Each card runs the least-compressed copy it can hold — Q8_0 on the smallest card that fits. Setting a floor drops the cards that only manage BTLM-3B by squeezing it further than you would want.
04

Rank by throughput rather than spec sheet

Ranking by tokens per second for BTLM-3B follows memory bandwidth, not core counts, which is why the B200 tops it at 1,303 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs BTLM-3B but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
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 BTLM-3B.

Answers

BTLM-3B — common questions

Can I run BTLM-3B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 3.5 GB and generating roughly 184 tokens per second — a comfortable fit.

Can I run BTLM-3B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 3.5 GB and generating roughly 218 tokens per second — a comfortable fit.

Is BTLM-3B open source?

Its weights are published, so BTLM-3B 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 BTLM-3B have?

BTLM-3B has 2.6B parameters. 2.6B, per paper. 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 BTLM-3B?

BTLM-3B was published by Cerebras Systems, based in United States of America, categorised as industry.

When was BTLM-3B released?

BTLM-3B was published in September 2023. 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 BTLM-3B used for?

BTLM-3B works in Language, and is recorded as handling language generation, Code generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download BTLM-3B?

The weights for BTLM-3B 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 BTLM-3B?

Around 9.8 × 10²¹ FLOP, on Cerebras CS-2. 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 BTLM-3B 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 BTLM-3B is rarely worth using. Every figure here assumes the whole model is on the card.

Would two GPUs run BTLM-3B faster?

Two cards buy memory rather than speed. That matters for BTLM-3B only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for BTLM-3B?

Because capacity varies, so does how hard BTLM-3B has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these BTLM-3B speed estimates?

These are estimates with real error bars. The fastest result here, 782–2,085 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 BTLM-3B?

The smallest card in our catalogue that holds BTLM-3B is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 3.5 GB, and produces roughly 14.2 tokens per second. 818 cards in total can run it.

How fast is BTLM-3B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 1,303 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 BTLM-3B clear that.

How much VRAM does BTLM-3B need?

About 3.5 GB at Q8_0 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 BTLM-3B on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 3.5 GB and generating roughly 243 tokens per second — a comfortable fit.

Can I run BTLM-3B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 3.5 GB and generating roughly 149 tokens per second — a comfortable fit.

Source

Original publication

Record last updated 28 November 2025

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