Me Llama 13B TPS calculator

Open weights Yale School of Medicine,University of Florida,University of Texas Health Science Center 13B parameters February 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

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 Me Llama 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.

Search cards

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: Yale School of Medicine,University of Florida,University of Texas Health Science Center
Organisation type: Academia,Academia,Academia
Country: United States of America
Published: 20 February 2024
Authors: Qianqian Xie, Qingyu Chen, Aokun Chen, Cheng Peng, Yan Hu, Fongci Lin, Xueqing Peng, Jimin Huang, Jeffrey Zhang, Vipina Keloth, Xinyu Zhou, Lingfei Qian, Huan He, Dennis Shung, Lucila Ohno-Machado, Yonghui Wu, Hua Xu, Jiang Bian

What it does

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

Domain: Language, Medicine
Task: Language modeling/generation, Question answering, Medical diagnosis, Named entity recognition (NER), Text classification, Text summarization
Base model: Llama 2-13B
Numerical format: BF16

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: 129,000,000,000 tokens
Epochs: 1

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.

How it was established: Operation counting
Fine-tuning compute: 1 × 10²² FLOP

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

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.

Record confidence: Confident

Sources

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

Reference: Me LLaMA: Foundation Large Language Models for Medical Applications
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Me Llama 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 Me Llama 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

Hardware requirements in practice

Minimum card

GeForce GTX 1080 Ti

Memory needed

9.4 GB

Fastest

261 tok/s

Me Llama 13B reaches a parameter count of 13B. That is small enough that hardware is rarely the obstacle, including on cards several years old. The number of cards we track that can run it: 295.

The entry point is GeForce GTX 1080 Ti, with a memory capacity of 11 GB, running it at a compression of Q3_K_M and producing around 36.2 tokens per second.

Top of the range is B200, generating roughly 261 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

Where it came from

Me Llama 13B was published by Yale School of Medicine,University of Florida,University of Texas Health Science Center, in the country recorded as United States of America, during February 2024. The category the publisher falls under is academia,Academia,Academia.

It works in the domain of Language, Medicine, and is recorded as performing the task of language modeling/generation, Question answering, Medical diagnosis, Named entity recognition (NER), Text classification, Text summarization.

It builds on Llama 2-13B. That is the usual way a specialised model is produced.

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 sits at 24.2 tokens per second. Clearing the ten tokens per second that roughly matches reading speed: 258 of them.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

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

How it was trained

It was trained on a corpus of about 129,000,000,000 tokens of text.

Step by step

How to choose a GPU for Me Llama 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

The table lists every card able to hold Me Llama 13B, needing around 9.4 GB at a compression of Q3_K_M. Capacity is the gate — a card either holds it or it does not.
02

Match the context to your actual use

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason a card that seemed fine stops fitting Me Llama 13B.
03

Decide how much compression you will accept

The quantisation column varies by card, because a bigger card holds a more accurate copy, reaching a compression of Q3_K_M on the smallest card that fits. Setting a minimum quality drops the cards that only manage it by squeezing further than you would want, and holds the comparison at one level.
04

Compare tokens per second, not specifications

Ranking by tokens per second follows memory bandwidth rather than core counts, for Me Llama 13B. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 261 tok/s.
05

Read the fit column last

Tight means it loads and works with no room to raise the context later, in the case of Me Llama 13B. Comfortable means you can grow the context later. That difference matters more than a few tokens per second, so buy for comfortable if you expect to.
06

Check the card from the other side

Each card page repeats this sweep for every model we hold, answering what else the hardware is good for beyond Me Llama 13B.

Answers

Me Llama 13B — common questions

Me Llama 13B— where can I download it?

The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

Me Llama 13B— can I run it if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part held in system memory drags the whole thing down. The nearest miss we calculate falls short by 1.9 GB. Every figure here assumes the whole model is resident on the card.

Me Llama 13B— would two GPUs run it faster?

Capacity adds across cards; throughput does not. The number of cards already holding it on their own: 295. So a second card is rarely the answer here.

Me Llama 13B— why does the quantisation differ between cards?

Because capacity varies, so does how hard it has to be squeezed. The number of distinct levels in the table above: 4. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

Me Llama 13B— how accurate are these speed estimates?

These are estimates with real error bars, and any of them could reasonably land anywhere in its published range depending on which runtime you use. The fastest result here: 222–313 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Me Llama 13B— what GPU do I need to run it?

The smallest card in our catalogue that holds it is GeForce GTX 1080 Ti, with a memory capacity of 11 GB. It runs the model at a compression of Q3_K_M using about 9.4 GB, and produces roughly 36.2 tokens per second. The number of cards able to run it in total: 295.

Me Llama 13B— how fast is it on a GPU?

It depends on the card. The quickest we calculate is 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 the number of cards clearing that: 258.

Me Llama 13B— how much VRAM does it need?

It needs about 9.4 GB at a compression of Q3_K_M, 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.

Me Llama 13B— can I run it on a GPU holding 12 GB?

Yes. The card GeForce RTX 3080 Ti, holding 12 GB, runs it at a compression of IQ4_XS, using about 10.1 GB and generating roughly 73.0 tokens per second. The fit is tight.

Me Llama 13B— can I run it on a GPU holding 16 GB?

Yes. The card Tesla V100 SXM2 16 GB, holding 16 GB, runs it at a compression of Q6_K, using about 13.9 GB and generating roughly 53.5 tokens per second. The fit is tight.

Me Llama 13B— can I run it on a GPU holding 24 GB?

Yes. The card GeForce RTX 5090 D V2, holding 24 GB, runs it at a compression of Q8_0, using about 16.9 GB and generating roughly 43.7 tokens per second. The fit is comfortable.

Me Llama 13B— is it open source?

Its weights are published, so it 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.

Me Llama 13B— how many parameters does it have?

It has a parameter count of 13B. 13B. 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.

Me Llama 13B— who created it?

It was published by Yale School of Medicine,University of Florida,University of Texas Health Science Center, based in United States of America, an organisation categorised as academia,Academia,Academia.

Me Llama 13B— when was it released?

It was published in February 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.

Me Llama 13B— what is it used for?

It works in the domain of Language, Medicine, and is recorded as handling the task of language modeling/generation, Question answering, Medical diagnosis, Named entity recognition (NER), Text classification, Text summarization. These are the areas it was designed around; they describe intent rather than a hard boundary.

Source

Original publication

Record last updated 28 November 2025

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