LLaMA-65B TPS calculator

Open weights Meta AI 65.2B parameters February 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

61 cards that can run it

818 cards we hold specifications for

Smallest card that fits

A100 PCIe 40 GB

40 GB · Q3_K_M · 27.3 tok/s

Fastest card

B200

52.0 tok/s · 180 GB

Which GPUs can run LLaMA-65B?

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

61 cards match

Calculating

					Needs	Quantisation	Fit
52.0 tok/s 44–62	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	71.4 GB	Q8_0	Comfortable
52.0 tok/s 44–62	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	71.4 GB	Q8_0	Comfortable
41.5 tok/s 25–66 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	71.4 GB	Q8_0	Comfortable
41.5 tok/s 25–66 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	71.4 GB	Q8_0	Comfortable
33.2 tok/s 20–53 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	71.4 GB	Q8_0	Comfortable
31.8 tok/s 27–38	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	71.4 GB	Q8_0	Comfortable
31.8 tok/s 27–38	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	71.4 GB	Q8_0	Comfortable
30.4 tok/s 18–49 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	71.4 GB	Q8_0	Comfortable
28.0 tok/s 24–34	GRID A100B NVIDIA	48 GB	1,870 GB/s	May 2020	41.1 GB	Q4_K_M	Tight
27.3 tok/s 23–33	A100 PCIe 40 GB NVIDIA	40 GB	1,560 GB/s	Jun 2020	33.5 GB	Q3_K_M	Tight
27.3 tok/s 23–33	A100 SXM4 40 GB NVIDIA	40 GB	1,560 GB/s	May 2020	33.5 GB	Q3_K_M	Tight
27.3 tok/s 23–33	A800 PCIe 40 GB NVIDIA	40 GB	1,560 GB/s	Nov 2022	33.5 GB	Q3_K_M	Tight
27.0 tok/s 16–43 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	71.4 GB	Q8_0	Comfortable
27.0 tok/s 16–43 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	71.4 GB	Q8_0	Comfortable
27.0 tok/s 16–43 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	71.4 GB	Q8_0	Comfortable
25.6 tok/s 22–31	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	71.4 GB	Q8_0	Tight
21.8 tok/s 19–26	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	71.4 GB	Q8_0	Comfortable
21.8 tok/s 19–26	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	71.4 GB	Q8_0	Tight
21.8 tok/s 19–26	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	71.4 GB	Q8_0	Tight
21.8 tok/s 19–26	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	71.4 GB	Q8_0	Comfortable
21.8 tok/s 19–26	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	71.4 GB	Q8_0	Tight
20.1 tok/s 17–24	RTX PRO 5000 Blackwell NVIDIA	48 GB	1,340 GB/s	Mar 2025	41.1 GB	Q4_K_M	Tight
19.1 tok/s 16–23	H100 SXM5 64 GB NVIDIA	64 GB	2,020 GB/s	Mar 2023	56.3 GB	Q6_K	Tight
16.6 tok/s 10–27 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	71.4 GB	Q8_0	Comfortable
16.6 tok/s 10–27 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	71.4 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: Meta AI
Organisation type: Industry
Country: United States of America
Published: 24 February 2023
Authors: Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample

What it does

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

Domain: Language
Task: Language modeling, Code generation
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: 65.2B
Training data: 1,400,000,000,000 tokens
Epochs: 1.09
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: 5.5 × 10²³ 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: NVIDIA A100
Chips used: 2,048
Chip-hours: 1,024,000
Wall-clock time: 500 hours (20.8 days)
Hardware utilisation: MFU 47.5%
Power draw: 1.6 MW
Compute cost: $578,026

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 (non-commercial)
Training code: Unreleased

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Foundation model: Yes
Likely above 10²³ FLOP: Yes
Why it is tracked: Historical significance,Highly cited
Record confidence: Confident
Citations: 19,926
Benchmark data: LLaMA-65B

Sources

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

Reference: LLaMA: Open and Efficient Foundation Language Models
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run LLaMA-65B

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-65B

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

A100 PCIe 40 GB

Memory needed

33.5 GB

Fastest

52.0 tok/s

LLaMA-65B sits at 65.2B parameters, which puts it above consumer hardware and into the range where a card is bought for this purpose rather than repurposed for it. 61 of the cards we track can hold it.

The entry point is the A100 PCIe 40 GB: 40 GB of memory, Q3_K_M compression, roughly 27.3 tokens per second.

The quickest result comes from a B200 at around 52.0 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

What this model is

LLaMA-65B was published by Meta AI, in United States of America, in February 2023. It comes out of industry.

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

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

What decides the speed

The median result is around 13.3 tokens per second; 56 cards produce text faster than most people read it.

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.

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

What went into building it

Training it took roughly 5.5 × 10²³ FLOP of computation, on NVIDIA A100 — a measure of what producing the model cost, not of how fast it answers.

Around 1,400,000,000,000 tokens went into training it.

Its inclusion criterion is historical significance,Highly cited.

Step by step

How to choose a GPU for LLaMA-65B

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 LLaMA-65B — around 33.5 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

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context LLaMA-65B can slip off a card that handles short questions easily.
03

Choose how far you will compress it

The quantisation column varies by card, because a bigger card holds a more accurate copy of LLaMA-65B — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Sort by speed

Ranking by tokens per second for LLaMA-65B follows memory bandwidth, not core counts, which is why the B200 tops it at 52.0 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs LLaMA-65B 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

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for LLaMA-65B alone — a card is usually bought for more than one model.

Answers

LLaMA-65B — common questions

Would two GPUs run LLaMA-65B faster?

Capacity adds across cards; throughput does not. Since 61 of the cards we track already hold LLaMA-65B on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for LLaMA-65B?

Because capacity varies, so does how hard LLaMA-65B has to be squeezed — 4 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these LLaMA-65B speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked. That is why each is published as a range such as 44–62 tok/s on the B200 rather than a single number.

What GPU do I need to run LLaMA-65B?

The smallest card in our catalogue that holds LLaMA-65B is the A100 PCIe 40 GB, with 40 GB of memory. It runs the model at Q3_K_M using about 33.5 GB, and produces roughly 27.3 tokens per second. 61 cards in total can run it.

How fast is LLaMA-65B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 52.0 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 56 of the cards that can run LLaMA-65B clear that.

How much VRAM does LLaMA-65B need?

About 33.5 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.

Is LLaMA-65B open source?

Its weights are published, so LLaMA-65B 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-65B have?

LLaMA-65B has 65.2B parameters. Model card, table 1: https://github.com/facebookresearch/llama/blob/53011c3d7946dadb8274a4c5c7586ab54edf792d/MODEL_CARD.md. 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-65B?

LLaMA-65B was published by Meta AI, based in United States of America, categorised as industry.

When was LLaMA-65B released?

LLaMA-65B was published in February 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-65B used for?

LLaMA-65B works in Language, and is recorded as handling language modeling, 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.

Where can I download LLaMA-65B?

The weights for LLaMA-65B 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-65B?

Around 5.5 × 10²³ FLOP, on NVIDIA A100. 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-65B if it does not fit in my GPU?

It can be split between the card and system memory, but LLaMA-65B generates painfully slowly that way — the nearest miss we calculate is short by 12.3 GB. Nothing on this page assumes offloading.

Source

Original publication

Record last updated 25 May 2026

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