OpenBioLLM-Llama3-8B TPS calculator

Open weights Saama 8B parameters January 2025

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 OpenBioLLM-Llama3-8B?

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: Saama
Country: United States of America
Published: 14 January 2025
Authors: Ankit Pal, Malaikannan Sankarasubbu

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
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

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 H100 SXM5 80GB
Chips used: 1
Power draw: 756 W

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: aaditya

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: Introducing OpenBioLLM-Llama3-70B & 8B: Saama’s AI Research Lab Released the Most Openly Available Medical-Domain LLMs to Date!
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run OpenBioLLM-Llama3-8B

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 OpenBioLLM-Llama3-8B

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

What it takes to run this model

Minimum card

Quadro 6000

Memory needed

5.1 GB

Fastest

424 tok/s

OpenBioLLM-Llama3-8B 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 smallest card that holds it is the Quadro 6000 with 6 GB, running it at Q3_K_M and producing around 17.4 tokens per second.

The quickest result comes from a B200 at around 424 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Where it came from

OpenBioLLM-Llama3-8B was published by Saama, in United States of America, in January 2025.

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

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 aaditya organisation on Hugging Face.

Understanding the speeds

The median result is around 23.8 tokens per second; 551 cards produce text faster than most people read it.

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.

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 OpenBioLLM-Llama3-8B

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

The table lists every card that can hold OpenBioLLM-Llama3-8B — around 5.1 GB at Q3_K_M. That figure, not the card's headline performance, is what decides whether it runs.
02

Match the context to your actual use

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context OpenBioLLM-Llama3-8B can slip off a card that handles short questions easily.
03

Set a quality floor

Compression is what makes OpenBioLLM-Llama3-8B 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

Sort by speed

Ranking by tokens per second for OpenBioLLM-Llama3-8B follows memory bandwidth, not core counts, which is why the B200 tops it at 424 tok/s.
05

Read the fit column last

A tight fit runs OpenBioLLM-Llama3-8B but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

See what else that card runs

Following a card through to its own page shows every other model it can hold, which is the question that follows once OpenBioLLM-Llama3-8B is settled.

Answers

OpenBioLLM-Llama3-8B — common questions

Can I run OpenBioLLM-Llama3-8B 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 OpenBioLLM-Llama3-8B 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 OpenBioLLM-Llama3-8B 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 OpenBioLLM-Llama3-8B 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 OpenBioLLM-Llama3-8B open source?

Its weights are published, so OpenBioLLM-Llama3-8B 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 OpenBioLLM-Llama3-8B have?

OpenBioLLM-Llama3-8B has 8B parameters. 8B. 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 OpenBioLLM-Llama3-8B?

OpenBioLLM-Llama3-8B was published by Saama, based in United States of America.

When was OpenBioLLM-Llama3-8B released?

OpenBioLLM-Llama3-8B was published in January 2025.

What is OpenBioLLM-Llama3-8B used for?

OpenBioLLM-Llama3-8B works in Language, Medicine, and is recorded as handling language modeling/generation, Question answering, Medical diagnosis. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download OpenBioLLM-Llama3-8B?

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

Can I run OpenBioLLM-Llama3-8B if it does not fit in my GPU?

Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded OpenBioLLM-Llama3-8B is rarely worth using — the nearest miss we calculate is short by 1.5 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run OpenBioLLM-Llama3-8B faster?

A second card roughly doubles the memory available but not the generation rate. With 582 cards already able to run OpenBioLLM-Llama3-8B alone, the case for pairing is weak.

Why does the quantisation differ between cards for OpenBioLLM-Llama3-8B?

A larger card holds a more accurate copy. Across the cards that run OpenBioLLM-Llama3-8B, 4 compression levels are used; the floor control above pins it to one.

How accurate are these OpenBioLLM-Llama3-8B 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 OpenBioLLM-Llama3-8B?

The smallest card in our catalogue that holds OpenBioLLM-Llama3-8B 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 OpenBioLLM-Llama3-8B 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 OpenBioLLM-Llama3-8B clear that.

How much VRAM does OpenBioLLM-Llama3-8B 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.

Source

Original publication

Record last updated 28 November 2025

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