LLaMA-3-Instruct-8B TPS calculator

Open weights Meta AI 8B parameters April 2024

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 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Quadro 6000

6 GB · Q3_K_M · 17.4 tok/s

Fastest card

B200

424 tok/s · 180 GB

Which GPUs can run LLaMA-3-Instruct-8B?

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
424 tok/s 360–508	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	9.8 GB	Q8_0	Comfortable
424 tok/s 360–508	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	9.8 GB	Q8_0	Comfortable
338 tok/s 203–541 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	9.8 GB	Q8_0	Comfortable
338 tok/s 203–541 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	9.8 GB	Q8_0	Comfortable
270 tok/s 162–433 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	9.8 GB	Q8_0	Comfortable
259 tok/s 220–311	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	9.8 GB	Q8_0	Comfortable
259 tok/s 220–311	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	9.8 GB	Q8_0	Comfortable
248 tok/s 149–396 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	9.8 GB	Q8_0	Comfortable
220 tok/s 132–352 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	9.8 GB	Q8_0	Comfortable
220 tok/s 132–352 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	9.8 GB	Q8_0	Comfortable
220 tok/s 132–352 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	9.8 GB	Q8_0	Comfortable
209 tok/s 177–250	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	9.8 GB	Q8_0	Comfortable
178 tok/s 151–213	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	9.8 GB	Q8_0	Comfortable
178 tok/s 151–213	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	9.8 GB	Q8_0	Comfortable
178 tok/s 151–213	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	9.8 GB	Q8_0	Comfortable
178 tok/s 151–213	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	9.8 GB	Q8_0	Comfortable
178 tok/s 151–213	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	9.8 GB	Q8_0	Comfortable
141 tok/s 120–169	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	7.0 GB	Q5_K_M	Tight
135 tok/s 81–217 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	9.8 GB	Q8_0	Comfortable
135 tok/s 81–217 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	9.8 GB	Q8_0	Comfortable
120 tok/s 102–144	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	7.9 GB	Q6_K	Tight
113 tok/s 68–181 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	9.8 GB	Q8_0	Comfortable
110 tok/s 66–177 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	9.8 GB	Q8_0	Comfortable
108 tok/s 92–130	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	9.8 GB	Q8_0	Comfortable
108 tok/s 92–130	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	9.8 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: Meta AI
Organisation type: Industry
Country: United States of America
Published: 18 April 2024

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, Code generation
Base model: Llama 3-8B

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: 8B
Training data: tokens

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 (restricted use)
Training code: Unreleased
Hugging Face: meta-llama

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.

Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run LLaMA-3-Instruct-8B

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 LLaMA-3-Instruct-8B

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

Quadro 6000

Memory needed

5.1 GB

Fastest

424 tok/s

LLaMA-3-Instruct-8B is small enough at 8B parameters that hardware is rarely the obstacle — 582 of the cards we track can run it, including cards several years old.

The entry point is the Quadro 6000: 6 GB of memory, Q3_K_M compression, roughly 17.4 tokens per second.

Top of the range is the B200, at roughly 424 tokens per second thanks to 8,000 GB/s of bandwidth.

What this model is

LLaMA-3-Instruct-8B was published by Meta AI, in United States of America, in April 2024. The organisation is categorised as industry.

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

It builds on Llama 3-8B, which is why it shares that model's general shape and size.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. It is published under the meta-llama organisation on Hugging Face.

What decides the speed

Half the cards that hold it manage more than 23.8 tokens per second, and 551 exceed reading speed outright.

Being dense, it reads all of itself per token, which is why the ordering by speed below follows the ordering by memory bandwidth so closely.

Its attention layout is on file, so the memory figures are computed exactly rather than approximated.

Step by step

How to choose a GPU for LLaMA-3-Instruct-8B

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

Every card here has been checked against LLaMA-3-Instruct-8B — around 5.1 GB at Q3_K_M. Capacity is the gate — a card either holds it or it does not.
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 LLaMA-3-Instruct-8B.
03

Set a quality floor

Compression is what makes LLaMA-3-Instruct-8B fit smaller cards, at some cost in accuracy — Q3_K_M on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Rank by throughput rather than spec sheet

Sort by speed to see how cards rank for LLaMA-3-Instruct-8B. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 424 tok/s.
05

Read the fit column last

The fit column separates cards that just manage LLaMA-3-Instruct-8B 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 LLaMA-3-Instruct-8B is settled.

Answers

LLaMA-3-Instruct-8B — common questions

Where can I download LLaMA-3-Instruct-8B?

Its weights are published under the meta-llama organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.

Can I run LLaMA-3-Instruct-8B if it does not fit in my GPU?

It can be split between the card and system memory, but LLaMA-3-Instruct-8B generates painfully slowly that way — the nearest miss we calculate is short by 1.5 GB. Nothing on this page assumes offloading.

Would two GPUs run LLaMA-3-Instruct-8B faster?

A second card roughly doubles the memory available but not the generation rate. With 582 cards already able to run LLaMA-3-Instruct-8B alone, the case for pairing is weak.

Why does the quantisation differ between cards for LLaMA-3-Instruct-8B?

Each card is shown running the least-compressed copy it can hold, and LLaMA-3-Instruct-8B appears at 4 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these LLaMA-3-Instruct-8B speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 360–508 tok/s on the B200, for instance. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

What GPU do I need to run LLaMA-3-Instruct-8B?

The smallest card in our catalogue that holds LLaMA-3-Instruct-8B 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 17.4 tokens per second. 582 cards in total can run it.

How fast is LLaMA-3-Instruct-8B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 424 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 551 of the cards that can run LLaMA-3-Instruct-8B clear that.

How much VRAM does LLaMA-3-Instruct-8B 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.

Can I run LLaMA-3-Instruct-8B on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q5_K_M, using about 7.0 GB and generating roughly 141 tokens per second — a tight fit.

Can I run LLaMA-3-Instruct-8B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 9.8 GB and generating roughly 48.3 tokens per second — a tight fit.

Can I run LLaMA-3-Instruct-8B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 9.8 GB and generating roughly 59.8 tokens per second — a comfortable fit.

Can I run LLaMA-3-Instruct-8B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 9.8 GB and generating roughly 70.9 tokens per second — a comfortable fit.

Is LLaMA-3-Instruct-8B open source?

Its weights are published, so LLaMA-3-Instruct-8B 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 LLaMA-3-Instruct-8B have?

LLaMA-3-Instruct-8B has 8B 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 LLaMA-3-Instruct-8B?

LLaMA-3-Instruct-8B was published by Meta AI, based in United States of America, categorised as industry.

When was LLaMA-3-Instruct-8B released?

LLaMA-3-Instruct-8B was published in April 2024. 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 LLaMA-3-Instruct-8B used for?

LLaMA-3-Instruct-8B works in Language, and is recorded as handling language modeling/generation, Question answering, 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.

Source

Original publication

Record last updated 28 November 2025

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