TinyLlama-1.1B (3T token checkpoint) TPS calculator

Open weights Singapore University of Technology & Design 1.1B parameters October 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 of 818 cards that can run it

Smallest card that fits

Tesla C1080

4 GB · Q8_0 · 33.5 tok/s

Fastest card

B200

3,080 tok/s · 180 GB

Which GPUs can run TinyLlama-1.1B (3T token checkpoint)?

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

818 cards match

Calculating

					Needs	Quantisation	Fit
3,080 tok/s 1,848–4,928 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.9 GB	Q8_0	Comfortable
3,080 tok/s 1,848–4,928 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.9 GB	Q8_0	Comfortable
2,460 tok/s 1,476–3,935 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.9 GB	Q8_0	Comfortable
2,460 tok/s 1,476–3,935 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.9 GB	Q8_0	Comfortable
1,967 tok/s 1,180–3,147 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.9 GB	Q8_0	Comfortable
1,883 tok/s 1,130–3,012 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.9 GB	Q8_0	Comfortable
1,883 tok/s 1,130–3,012 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.9 GB	Q8_0	Comfortable
1,802 tok/s 1,081–2,883 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.9 GB	Q8_0	Comfortable
1,599 tok/s 960–2,559 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.9 GB	Q8_0	Comfortable
1,599 tok/s 960–2,559 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.9 GB	Q8_0	Comfortable
1,599 tok/s 960–2,559 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.9 GB	Q8_0	Comfortable
1,517 tok/s 910–2,427 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
1,294 tok/s 776–2,070 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
1,294 tok/s 776–2,070 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.9 GB	Q8_0	Comfortable
1,294 tok/s 776–2,070 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
1,294 tok/s 776–2,070 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
1,294 tok/s 776–2,070 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
985 tok/s 591–1,576 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.9 GB	Q8_0	Comfortable
985 tok/s 591–1,576 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.9 GB	Q8_0	Comfortable
821 tok/s 493–1,313 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.9 GB	Q8_0	Comfortable
803 tok/s 482–1,285 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.9 GB	Q8_0	Comfortable
785 tok/s 471–1,257 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.9 GB	Q8_0	Comfortable
785 tok/s 471–1,257 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.9 GB	Q8_0	Comfortable
785 tok/s 471–1,257 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.9 GB	Q8_0	Comfortable
785 tok/s 471–1,257 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	1.9 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: Singapore University of Technology & Design
Organisation type: Academia
Country: Singapore
Published: 1 October 2023
Authors: Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, Wei Lu

What it does

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

Domain: Language
Task: Chat, Language modeling/generation, Translation, Question answering
Approach: Self-supervised learning

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: 1.1B
Training data: tokens
Epochs: 3
Batch size: 2,000,000

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: 2.2 × 10²² FLOP
How it was established: Hardware,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: NVIDIA A100 SXM4 40 GB
Chips used: 16
Chip-hours: 34,560
Wall-clock time: 2,160 hours (90 days)
Hardware utilisation: MFU 56.0%
Power draw: 12.7 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 (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: 722

Sources

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

Reference: TinyLlama: An Open-Source Small Language Model
Last updated: 25 May 2026

The extremes

The ten fastest GPUs for TinyLlama-1.1B (3T token checkpoint)

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 TinyLlama-1.1B (3T token checkpoint)

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

Hardware requirements in practice

Minimum card

Tesla C1080

Memory needed

1.9 GB

Fastest

3,080 tok/s

TinyLlama-1.1B (3T token checkpoint) is small enough at 1.1B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The entry point is the Tesla C1080: 4 GB of memory, Q8_0 compression, roughly 33.5 tokens per second.

The quickest result comes from a B200 at around 3,080 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

About this model

TinyLlama-1.1B (3T token checkpoint) was published by Singapore University of Technology & Design, in Singapore, in October 2023. academia is the category the publisher falls under.

It works in Language, and is recorded as doing chat, Language modeling/generation, Translation, Question answering.

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

How fast it runs, and why

Half the cards that hold it manage more than 86.5 tokens per second, and 799 exceed reading speed outright.

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.

How it was trained

Training it took roughly 2.2 × 10²² FLOP of computation, on NVIDIA A100 SXM4 40 GB — a measure of what producing the model cost, not of how fast it answers.

Step by step

How to choose a GPU for TinyLlama-1.1B (3T token checkpoint)

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 TinyLlama-1.1B (3T token checkpoint) actually needs — around 1.9 GB at Q8_0. No amount of processing power compensates for a card that cannot hold it.
02

Set the context length you will work at

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for TinyLlama-1.1B (3T token checkpoint).
03

Choose how far you will compress it

Compression is what makes TinyLlama-1.1B (3T token checkpoint) fit smaller cards, at some cost in accuracy — Q8_0 on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Compare tokens per second, not specifications

The speed ordering for TinyLlama-1.1B (3T token checkpoint) is effectively an ordering by memory bandwidth, which is why the B200 tops it at 3,080 tok/s.
05

Check the fit verdict before buying

Tight means TinyLlama-1.1B (3T token checkpoint) 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

Open the card you have settled on

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for TinyLlama-1.1B (3T token checkpoint) alone — a card is usually bought for more than one model.

Answers

TinyLlama-1.1B (3T token checkpoint) — common questions

How many parameters does TinyLlama-1.1B (3T token checkpoint) have?

TinyLlama-1.1B (3T token checkpoint) has 1.1B parameters. 1.1B. 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 TinyLlama-1.1B (3T token checkpoint)?

TinyLlama-1.1B (3T token checkpoint) was published by Singapore University of Technology & Design, based in Singapore, categorised as academia.

When was TinyLlama-1.1B (3T token checkpoint) released?

TinyLlama-1.1B (3T token checkpoint) was published in October 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 TinyLlama-1.1B (3T token checkpoint) used for?

TinyLlama-1.1B (3T token checkpoint) works in Language, and is recorded as handling chat, Language modeling/generation, Translation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download TinyLlama-1.1B (3T token checkpoint)?

The weights for TinyLlama-1.1B (3T token checkpoint) 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 TinyLlama-1.1B (3T token checkpoint)?

Around 2.2 × 10²² FLOP, on NVIDIA A100 SXM4 40 GB. 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 TinyLlama-1.1B (3T token checkpoint) 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. Our figures for TinyLlama-1.1B (3T token checkpoint) assume it is fully resident.

Would two GPUs run TinyLlama-1.1B (3T token checkpoint) faster?

Capacity adds across cards; throughput does not. Since 818 of the cards we track already hold TinyLlama-1.1B (3T token checkpoint) on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for TinyLlama-1.1B (3T token checkpoint)?

A larger card holds a more accurate copy. Across the cards that run TinyLlama-1.1B (3T token checkpoint), 1 compression levels are used; the floor control above pins it to one.

How accurate are these TinyLlama-1.1B (3T token checkpoint) speed estimates?

These are estimates with real error bars. The fastest result here, 1,848–4,928 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 TinyLlama-1.1B (3T token checkpoint)?

The smallest card in our catalogue that holds TinyLlama-1.1B (3T token checkpoint) is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.9 GB, and produces roughly 33.5 tokens per second. 818 cards in total can run it.

How fast is TinyLlama-1.1B (3T token checkpoint) on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 3,080 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 799 of the cards that can run TinyLlama-1.1B (3T token checkpoint) clear that.

How much VRAM does TinyLlama-1.1B (3T token checkpoint) need?

About 1.9 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 TinyLlama-1.1B (3T token checkpoint) on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 1.9 GB and generating roughly 574 tokens per second — a comfortable fit.

Can I run TinyLlama-1.1B (3T token checkpoint) on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 1.9 GB and generating roughly 351 tokens per second — a comfortable fit.

Can I run TinyLlama-1.1B (3T token checkpoint) on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 1.9 GB and generating roughly 435 tokens per second — a comfortable fit.

Can I run TinyLlama-1.1B (3T token checkpoint) on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 1.9 GB and generating roughly 516 tokens per second — a comfortable fit.

Is TinyLlama-1.1B (3T token checkpoint) open source?

Its weights are published, so TinyLlama-1.1B (3T token checkpoint) 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.

Source

Original publication

Record last updated 25 May 2026

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