OpenBioLLM-Llama3-70B TPS calculator
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
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
- Training data
- tokens
- Epochs
- 4
70B
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
- Hugging Face
- aaditya
Llama 3 license https://huggingface.co/aaditya/Llama3-OpenBioLLM-70B
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.
- 01 B300 288 GB · 8,000 GB/s · Q8_0 48.4 tok/s
- 02 B200 180 GB · 8,000 GB/s · Q8_0 48.4 tok/s
- 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q8_0 38.7 tok/s
- 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q8_0 38.7 tok/s
- 05 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q8_0 30.9 tok/s
- 06 H200 NVL 141 GB · 4,890 GB/s · Q8_0 29.6 tok/s
- 07 H200 SXM 141 GB 141 GB · 4,890 GB/s · Q8_0 29.6 tok/s
- 08 H800 SXM5 80 GB · 3,360 GB/s · Q6_K 29.5 tok/s
- 09 H100 SXM5 80 GB 80 GB · 3,360 GB/s · Q6_K 29.5 tok/s
- 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.
- 01 A800 PCIe 40 GB 40 GB · needs 33.0 GB · Q3_K_M · tight 25.5 tok/s
- 02 A100 PCIe 40 GB 40 GB · needs 33.0 GB · Q3_K_M · tight 25.5 tok/s
- 03 A100 SXM4 40 GB 40 GB · needs 33.0 GB · Q3_K_M · tight 25.5 tok/s
- 04 Radeon PRO W7900D 48 GB · needs 41.2 GB · Q4_K_M · tight 9.4 tok/s
- 05 RTX PRO 5000 Blackwell 48 GB · needs 41.2 GB · Q4_K_M · tight 18.7 tok/s
- 06 RTX 5880 Ada Generation 48 GB · needs 41.2 GB · Q4_K_M · tight 12.1 tok/s
- 07 L20 48 GB · needs 41.2 GB · Q4_K_M · tight 12.1 tok/s
- 08 Radeon PRO W7800 48 GB 48 GB · needs 41.2 GB · Q4_K_M · tight 9.4 tok/s
- 09 Radeon PRO W7900 48 GB · needs 41.2 GB · Q4_K_M · tight 9.4 tok/s
- 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 sits at 70B 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 25.5 tokens per second.
The quickest result comes from a B200 at around 48.4 tokens per second — its 8,000 GB/s of bandwidth is what buys that.
Where it came from
OpenBioLLM-Llama3-70B 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 is derived from Llama 3-70B rather than trained from scratch, which 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. It is published under the aaditya organisation on Hugging Face.
Understanding the speeds
Across every card that can run it, the middle of the range is about 17.1 tokens per second, and 49 of them clear the ten tokens per second that roughly matches reading speed.
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.
-
01
Start from the memory column
The table lists every card that can hold OpenBioLLM-Llama3-70B — around 33.0 GB at Q3_K_M. That figure, not the card's headline performance, is what decides whether it runs.
-
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.
-
03
Set a quality floor
The quantisation column varies by card, because a bigger card holds a more accurate copy of OpenBioLLM-Llama3-70B — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
-
04
Sort by speed
The speed ordering for OpenBioLLM-Llama3-70B is effectively an ordering by memory bandwidth, which is why the B200 tops it at 48.4 tok/s.
-
05
Check the fit verdict before buying
The fit column separates cards that just manage OpenBioLLM-Llama3-70B from those with room to spare. Buy for the second if the context might grow.
-
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-Llama3-70B is settled.
Answers
OpenBioLLM-Llama3-70B — common questions
What GPU do I need to run OpenBioLLM-Llama3-70B?
The smallest card in our catalogue that holds OpenBioLLM-Llama3-70B is the A100 PCIe 40 GB, with 40 GB of memory. It runs the model at Q3_K_M using about 33.0 GB, and produces roughly 25.5 tokens per second. 61 cards in total can run it.
How fast is OpenBioLLM-Llama3-70B on a GPU?
It depends on the card. The quickest we calculate is a 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 49 of the cards that can run OpenBioLLM-Llama3-70B clear that.
How much VRAM does OpenBioLLM-Llama3-70B need?
About 33.0 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 OpenBioLLM-Llama3-70B open source?
Its weights are published, so OpenBioLLM-Llama3-70B 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-Llama3-70B have?
OpenBioLLM-Llama3-70B has 70B parameters. 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.
Who created OpenBioLLM-Llama3-70B?
OpenBioLLM-Llama3-70B was published by Saama, based in United States of America.
When was OpenBioLLM-Llama3-70B released?
OpenBioLLM-Llama3-70B was published in January 2025.
What is OpenBioLLM-Llama3-70B used for?
OpenBioLLM-Llama3-70B 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-Llama3-70B?
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-Llama3-70B if it does not fit in my GPU?
It can be split between the card and system memory, but OpenBioLLM-Llama3-70B generates painfully slowly that way — the nearest miss we calculate is short by 12.4 GB. Nothing on this page assumes offloading.
Would two GPUs run OpenBioLLM-Llama3-70B faster?
Two cards buy memory rather than speed. That matters for OpenBioLLM-Llama3-70B only if one card cannot hold it — 61 can, so a second adds little.
Why does the quantisation differ between cards for OpenBioLLM-Llama3-70B?
Because capacity varies, so does how hard OpenBioLLM-Llama3-70B has to be squeezed — 4 distinct levels appear in the table above. Set a minimum quality to compare at one.
How accurate are these OpenBioLLM-Llama3-70B speed estimates?
These are estimates with real error bars. The fastest result here, 41–58 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.