OpenBioLLM-Llama3-70B TPS calculator

Open weights Saama 70B 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

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 · 25.5 tok/s

Fastest card

B200

48.4 tok/s · 180 GB

Which GPUs can run OpenBioLLM-Llama3-70B?

Set the inputs, read the answer

A longer conversation needs more memory, which can push this model off smaller cards.

Hides cards that would only fit the model by compressing it below this point.

61 cards match

Calculating
Needs Quantisation Fit
48.4 tok/s

41–58

B200 NVIDIA 180 GB 8,000 GB/s Jan 2024 73.8 GB Q8_0 Comfortable
48.4 tok/s

41–58

B300 NVIDIA 288 GB 8,000 GB/s Sep 2025 73.8 GB Q8_0 Comfortable
38.7 tok/s

23–62 · low confidence

Radeon Instinct MI350X AMD 288 GB 8,190 GB/s Jan 2025 73.8 GB Q8_0 Comfortable
38.7 tok/s

23–62 · low confidence

Radeon Instinct MI355X AMD 288 GB 8,190 GB/s Jan 2025 73.8 GB Q8_0 Comfortable
30.9 tok/s

19–49 · low confidence

Radeon Instinct MI300 AMD 128 GB 6,550 GB/s Jan 2023 73.8 GB Q8_0 Comfortable
29.6 tok/s

25–36

H200 NVL NVIDIA 141 GB 4,890 GB/s Nov 2024 73.8 GB Q8_0 Comfortable
29.6 tok/s

25–36

H200 SXM 141 GB NVIDIA 141 GB 4,890 GB/s Nov 2024 73.8 GB Q8_0 Comfortable
29.5 tok/s

25–35

H100 SXM5 80 GB NVIDIA 80 GB 3,360 GB/s Oct 2022 57.5 GB Q6_K Comfortable
29.5 tok/s

25–35

H800 SXM5 NVIDIA 80 GB 3,360 GB/s Mar 2023 57.5 GB Q6_K Comfortable
28.3 tok/s

17–45 · low confidence

Radeon Instinct MI325X AMD 256 GB 6,000 GB/s Oct 2024 73.8 GB Q8_0 Comfortable
26.1 tok/s

22–31

GRID A100B NVIDIA 48 GB 1,870 GB/s May 2020 41.2 GB Q4_K_M Tight
25.5 tok/s

22–31

A100 PCIe 40 GB NVIDIA 40 GB 1,560 GB/s Jun 2020 33.0 GB Q3_K_M Tight
25.5 tok/s

22–31

A100 SXM4 40 GB NVIDIA 40 GB 1,560 GB/s May 2020 33.0 GB Q3_K_M Tight
25.5 tok/s

22–31

A800 PCIe 40 GB NVIDIA 40 GB 1,560 GB/s Nov 2022 33.0 GB Q3_K_M Tight
25.1 tok/s

15–40 · low confidence

Radeon Instinct MI300A AMD 128 GB 5,325 GB/s Dec 2023 73.8 GB Q8_0 Comfortable
25.1 tok/s

15–40 · low confidence

Radeon Instinct MI300X AMD 192 GB 5,325 GB/s Dec 2023 73.8 GB Q8_0 Comfortable
25.1 tok/s

15–40 · low confidence

Radeon Instinct MI308X AMD 192 GB 5,325 GB/s Dec 2023 73.8 GB Q8_0 Comfortable
23.8 tok/s

20–29

H100 NVL 94 GB NVIDIA 94 GB 3,940 GB/s Mar 2023 73.8 GB Q8_0 Tight
20.3 tok/s

17–24

H100 PCIe 96 GB NVIDIA 96 GB 3,360 GB/s Mar 2023 73.8 GB Q8_0 Tight
20.3 tok/s

17–24

H100 SXM5 94 GB NVIDIA 94 GB 3,360 GB/s Mar 2023 73.8 GB Q8_0 Tight
20.3 tok/s

17–24

H100 SXM5 96 GB NVIDIA 96 GB 3,360 GB/s Mar 2023 73.8 GB Q8_0 Tight
18.7 tok/s

16–22

RTX PRO 5000 Blackwell NVIDIA 48 GB 1,340 GB/s Mar 2025 41.2 GB Q4_K_M Tight
17.9 tok/s

15–22

A100 SXM4 80 GB NVIDIA 80 GB 2,040 GB/s Nov 2020 57.5 GB Q6_K Comfortable
17.9 tok/s

15–22

A100X NVIDIA 80 GB 2,040 GB/s Jun 2021 57.5 GB Q6_K Comfortable
17.9 tok/s

15–22

A800 SXM4 80 GB NVIDIA 80 GB 2,040 GB/s Aug 2022 57.5 GB Q6_K 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 3-70B

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
70B

70B

Training data
tokens
Epochs
4

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
8
Power draw
11.0 kW

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

Llama 3 license https://huggingface.co/aaditya/Llama3-OpenBioLLM-70B

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-Llama3-70B

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.

  1. 01 B300 288 GB · 8,000 GB/s · Q8_0 48.4 tok/s
  2. 02 B200 180 GB · 8,000 GB/s · Q8_0 48.4 tok/s
  3. 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q8_0 38.7 tok/s
  4. 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q8_0 38.7 tok/s
  5. 05 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q8_0 30.9 tok/s
  6. 06 H200 NVL 141 GB · 4,890 GB/s · Q8_0 29.6 tok/s
  7. 07 H200 SXM 141 GB 141 GB · 4,890 GB/s · Q8_0 29.6 tok/s
  8. 08 H800 SXM5 80 GB · 3,360 GB/s · Q6_K 29.5 tok/s
  9. 09 H100 SXM5 80 GB 80 GB · 3,360 GB/s · Q6_K 29.5 tok/s
  10. 10 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q8_0 28.3 tok/s

The smallest GPUs that still run OpenBioLLM-Llama3-70B

The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.

  1. 01 A800 PCIe 40 GB 40 GB · needs 33.0 GB · Q3_K_M · tight 25.5 tok/s
  2. 02 A100 PCIe 40 GB 40 GB · needs 33.0 GB · Q3_K_M · tight 25.5 tok/s
  3. 03 A100 SXM4 40 GB 40 GB · needs 33.0 GB · Q3_K_M · tight 25.5 tok/s
  4. 04 Radeon PRO W7900D 48 GB · needs 41.2 GB · Q4_K_M · tight 9.4 tok/s
  5. 05 RTX PRO 5000 Blackwell 48 GB · needs 41.2 GB · Q4_K_M · tight 18.7 tok/s
  6. 06 RTX 5880 Ada Generation 48 GB · needs 41.2 GB · Q4_K_M · tight 12.1 tok/s
  7. 07 L20 48 GB · needs 41.2 GB · Q4_K_M · tight 12.1 tok/s
  8. 08 Radeon PRO W7800 48 GB 48 GB · needs 41.2 GB · Q4_K_M · tight 9.4 tok/s
  9. 09 Radeon PRO W7900 48 GB · needs 41.2 GB · Q4_K_M · tight 9.4 tok/s
  10. 10 Data Center GPU Max 1100 48 GB · needs 41.2 GB · Q4_K_M · tight 11.2 tok/s

What the numbers mean

Hardware requirements in practice

Minimum card

A100 PCIe 40 GB

Memory needed

33.0 GB

Fastest

48.4 tok/s

OpenBioLLM-Llama3-70B reaches a parameter count of 70B. That puts it above consumer hardware, into the range where a card is bought for this purpose rather than repurposed for it. The number of cards we track that can hold it: 61.

The entry point is A100 PCIe 40 GB, with a memory capacity of 40 GB, running it at a compression of Q3_K_M and producing around 25.5 tokens per second.

The quickest result comes from B200, generating roughly 48.4 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

Where it came from

OpenBioLLM-Llama3-70B was published by Saama, in the country recorded as United States of America, during January 2025.

It works in the domain of Language, Medicine, and is recorded as performing the task of language modeling/generation, Question answering, Medical diagnosis.

Rather than being trained from scratch, it is derived from Llama 3-70B. That is the usual way a specialised model is produced.

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached. On Hugging Face it is published under the organisation aaditya.

Understanding the speeds

Across every card that can run it, the middle of the range sits at 17.1 tokens per second. Clearing the ten tokens per second that roughly matches reading speed: 49 of them.

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 OpenBioLLM-Llama3-70B

The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.

  1. 01

    Start from the memory column

    The table lists every card able to hold OpenBioLLM-Llama3-70B, needing around 33.0 GB at a compression of Q3_K_M. That figure, not the headline performance of a card, is what decides whether it runs.

  2. 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 OpenBioLLM-Llama3-70B.

  3. 03

    Set a quality floor

    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.

  4. 04

    Sort by speed

    The speed ordering is effectively an ordering by memory bandwidth, for OpenBioLLM-Llama3-70B. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 48.4 tok/s.

  5. 05

    Check the fit verdict before buying

    The fit column separates cards that just manage it from those with room to spare, in the case of OpenBioLLM-Llama3-70B. 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.

  6. 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 you have settled on OpenBioLLM-Llama3-70B.

Answers

OpenBioLLM-Llama3-70B — common questions

01

OpenBioLLM-Llama3-70B— what GPU do I need to run it?

The smallest card in our catalogue that holds it is A100 PCIe 40 GB, with a memory capacity of 40 GB. It runs the model at a compression of Q3_K_M using about 33.0 GB, and produces roughly 25.5 tokens per second. The number of cards able to run it in total: 61.

02

OpenBioLLM-Llama3-70B— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 48.4 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: 49.

03

OpenBioLLM-Llama3-70B— how much VRAM does it need?

It needs about 33.0 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.

04

OpenBioLLM-Llama3-70B— 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.

05

OpenBioLLM-Llama3-70B— how many parameters does it have?

It has a parameter count of 70B. 70B. 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.

06

OpenBioLLM-Llama3-70B— who created it?

It was published by Saama, based in United States of America.

07

OpenBioLLM-Llama3-70B— when was it released?

It was published in January 2025.

08

OpenBioLLM-Llama3-70B— 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. These are the areas it was designed around; they describe intent rather than a hard boundary.

09

OpenBioLLM-Llama3-70B— where can I download it?

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

10

OpenBioLLM-Llama3-70B— can I run it if it does not fit in my GPU?

It can be split between the card and system memory, but it generates painfully slowly that way. The nearest miss we calculate falls short by 12.4 GB. Every figure here assumes the whole model is resident on the card.

11

OpenBioLLM-Llama3-70B— would two GPUs run it faster?

Two cards buy memory rather than speed, which matters only if one card cannot hold it. The number that can: 61. So a second card is rarely the answer here.

12

OpenBioLLM-Llama3-70B— 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.

13

OpenBioLLM-Llama3-70B— 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: 41–58 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Source

Original publication

Record last updated 28 November 2025

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