Llama 2-13B TPS calculator

Open weights Meta AI 13B parameters July 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

295 cards that can run it

818 cards we hold specifications for

Smallest card that fits

GeForce GTX 1080 Ti

11 GB · Q3_K_M · 36.2 tok/s

Fastest card

B200

261 tok/s · 180 GB

Which GPUs can run Llama 2-13B?

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

295 cards match

Calculating

					Needs	Quantisation	Fit
261 tok/s 222–313	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	16.9 GB	Q8_0	Comfortable
261 tok/s 222–313	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	16.9 GB	Q8_0	Comfortable
208 tok/s 125–333 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	16.9 GB	Q8_0	Comfortable
208 tok/s 125–333 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	16.9 GB	Q8_0	Comfortable
166 tok/s 100–266 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	16.9 GB	Q8_0	Comfortable
159 tok/s 135–191	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	16.9 GB	Q8_0	Comfortable
159 tok/s 135–191	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	16.9 GB	Q8_0	Comfortable
152 tok/s 91–244 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	16.9 GB	Q8_0	Comfortable
135 tok/s 81–217 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	16.9 GB	Q8_0	Comfortable
135 tok/s 81–217 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	16.9 GB	Q8_0	Comfortable
135 tok/s 81–217 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	16.9 GB	Q8_0	Comfortable
128 tok/s 109–154	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	16.9 GB	Q8_0	Comfortable
109 tok/s 93–131	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	16.9 GB	Q8_0	Comfortable
109 tok/s 93–131	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	16.9 GB	Q8_0	Comfortable
109 tok/s 93–131	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	16.9 GB	Q8_0	Comfortable
109 tok/s 93–131	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	16.9 GB	Q8_0	Comfortable
109 tok/s 93–131	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	16.9 GB	Q8_0	Comfortable
83.4 tok/s 50–133 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	16.9 GB	Q8_0	Comfortable
83.4 tok/s 50–133 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	16.9 GB	Q8_0	Comfortable
73.0 tok/s 62–88	GeForce RTX 3080 12 GB NVIDIA	12 GB	912 GB/s	Jan 2022	10.1 GB	IQ4_XS	Tight
73.0 tok/s 62–88	GeForce RTX 3080 Ti NVIDIA	12 GB	912 GB/s	May 2021	10.1 GB	IQ4_XS	Tight
69.5 tok/s 42–111 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	16.9 GB	Q8_0	Comfortable
68.0 tok/s 41–109 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	16.9 GB	Q8_0	Comfortable
66.5 tok/s 56–80	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	16.9 GB	Q8_0	Comfortable
66.5 tok/s 56–80	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	16.9 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: Meta AI
Organisation type: Industry
Country: United States of America
Published: 18 July 2023
Authors: Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan,…

What it does

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

Domain: Language
Task: Language modeling
Approach: Supervised

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: 13B
Training data: 2,000,000,000,000 tokens
Epochs: 1
Batch size: 4,000,000

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: 1.6 × 10²³ 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 80 GB
Compute cost: $235,478
Data centre: Meta’s Research Super Cluster

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

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
Why it is tracked: Historical significance,Significant use,Highly cited
Record confidence: Confident
Citations: 16,911

Sources

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

Reference: Llama 2: Open Foundation and Fine-Tuned Chat Models
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run Llama 2-13B

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 2-13B

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

GeForce GTX 1080 Ti

Memory needed

9.4 GB

Fastest

261 tok/s

Llama 2-13B is small enough at 13B parameters that hardware is rarely the obstacle — 295 of the cards we track can run it, including cards several years old.

The least hardware that works is a GeForce GTX 1080 Ti. Its 11 GB is enough at Q3_K_M compression, giving roughly 36.2 tokens per second.

A B200 is the fastest we calculate for it: about 261 tokens per second, from 8,000 GB/s of memory bandwidth.

Background

Llama 2-13B was published by Meta AI, in United States of America, in July 2023. It comes out of industry.

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

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.

Reading the throughput figures

Half the cards that hold it manage more than 24.2 tokens per second, and 258 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.

Because the architecture is recorded, the memory column is derived rather than estimated.

How it was trained

The training run consumed about 1.6 × 10²³ FLOP, on NVIDIA A100 SXM4 80 GB. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

It was trained on about 2,000,000,000,000 tokens of text.

It is tracked in the underlying dataset for one reason in particular: historical significance,Significant use,Highly cited.

Step by step

How to choose a GPU for Llama 2-13B

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 2-13B — around 9.4 GB at Q3_K_M. Capacity is the gate — a card either holds it or it does not.
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 Llama 2-13B stops fitting a card that seemed fine.
03

Decide how much compression you will accept

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 Llama 2-13B by squeezing it further than you would want.
04

Rank by throughput rather than spec sheet

Ranking by tokens per second for Llama 2-13B follows memory bandwidth, not core counts, which is why the B200 tops it at 261 tok/s.
05

Read the fit column last

The fit column separates cards that just manage Llama 2-13B from those with room to spare. Buy for the second if the context might grow.
06

Check the card from the other side

Following a card through to its own page shows every other model it can hold, which is the question that follows once Llama 2-13B is settled.

Answers

Llama 2-13B — common questions

Who created Llama 2-13B?

Llama 2-13B was published by Meta AI, based in United States of America, categorised as industry.

When was Llama 2-13B released?

Llama 2-13B was published in July 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 Llama 2-13B used for?

Llama 2-13B works in Language, and is recorded as handling language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Llama 2-13B?

The weights for Llama 2-13B 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 Llama 2-13B?

Around 1.6 × 10²³ FLOP, on NVIDIA A100 SXM4 80 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 Llama 2-13B if it does not fit in my GPU?

It can be split between the card and system memory, but Llama 2-13B generates painfully slowly that way — the nearest miss we calculate is short by 1.9 GB. Nothing on this page assumes offloading.

Would two GPUs run Llama 2-13B faster?

Capacity adds across cards; throughput does not. Since 295 of the cards we track already hold Llama 2-13B on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for Llama 2-13B?

Each card is shown running the least-compressed copy it can hold, and Llama 2-13B appears at 4 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these Llama 2-13B speed estimates?

These are estimates with real error bars. The fastest result here, 222–313 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 Llama 2-13B?

The smallest card in our catalogue that holds Llama 2-13B is the GeForce GTX 1080 Ti, with 11 GB of memory. It runs the model at Q3_K_M using about 9.4 GB, and produces roughly 36.2 tokens per second. 295 cards in total can run it.

How fast is Llama 2-13B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 261 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 258 of the cards that can run Llama 2-13B clear that.

How much VRAM does Llama 2-13B need?

About 9.4 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 2-13B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at IQ4_XS, using about 10.1 GB and generating roughly 73.0 tokens per second — a tight fit.

Can I run Llama 2-13B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q6_K, using about 13.9 GB and generating roughly 53.5 tokens per second — a tight fit.

Can I run Llama 2-13B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 16.9 GB and generating roughly 43.7 tokens per second — a comfortable fit.

Is Llama 2-13B open source?

Its weights are published, so Llama 2-13B 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 2-13B have?

Llama 2-13B has 13B parameters. Llama has been released in 7B, 13B, and 70B variants. 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.

Source

Original publication

Record last updated 25 May 2026

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