Athene-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 Athene-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
29–77 · low confidence |
B200 NVIDIA | 180 GB | 8,000 GB/s | Jan 2024 | 75.6 GB | Q8_0 | Comfortable |
|
48.4
tok/s
29–77 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 75.6 GB | Q8_0 | Comfortable |
|
38.7
tok/s
23–62 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 75.6 GB | Q8_0 | Comfortable |
|
38.7
tok/s
23–62 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 75.6 GB | Q8_0 | Comfortable |
|
30.9
tok/s
19–49 · low confidence |
Radeon Instinct MI300 AMD | 128 GB | 6,550 GB/s | Jan 2023 | 75.6 GB | Q8_0 | Comfortable |
|
29.6
tok/s
18–47 · low confidence |
H200 NVL NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 75.6 GB | Q8_0 | Comfortable |
|
29.6
tok/s
18–47 · low confidence |
H200 SXM 141 GB NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 75.6 GB | Q8_0 | Comfortable |
|
29.5
tok/s
18–47 · low confidence |
H100 SXM5 80 GB NVIDIA | 80 GB | 3,360 GB/s | Oct 2022 | 59.3 GB | Q6_K | Comfortable |
|
29.5
tok/s
18–47 · low confidence |
H800 SXM5 NVIDIA | 80 GB | 3,360 GB/s | Mar 2023 | 59.3 GB | Q6_K | Comfortable |
|
28.3
tok/s
17–45 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 75.6 GB | Q8_0 | Comfortable |
|
26.1
tok/s
16–42 · low confidence |
GRID A100B NVIDIA | 48 GB | 1,870 GB/s | May 2020 | 43.0 GB | Q4_K_M | Tight |
|
25.5
tok/s
15–41 · low confidence |
A100 PCIe 40 GB NVIDIA | 40 GB | 1,560 GB/s | Jun 2020 | 34.9 GB | Q3_K_M | Tight |
|
25.5
tok/s
15–41 · low confidence |
A100 SXM4 40 GB NVIDIA | 40 GB | 1,560 GB/s | May 2020 | 34.9 GB | Q3_K_M | Tight |
|
25.5
tok/s
15–41 · low confidence |
A800 PCIe 40 GB NVIDIA | 40 GB | 1,560 GB/s | Nov 2022 | 34.9 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 | 75.6 GB | Q8_0 | Comfortable |
|
25.1
tok/s
15–40 · low confidence |
Radeon Instinct MI300X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 75.6 GB | Q8_0 | Comfortable |
|
25.1
tok/s
15–40 · low confidence |
Radeon Instinct MI308X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 75.6 GB | Q8_0 | Comfortable |
|
23.8
tok/s
14–38 · low confidence |
H100 NVL 94 GB NVIDIA | 94 GB | 3,940 GB/s | Mar 2023 | 75.6 GB | Q8_0 | Tight |
|
21.8
tok/s
13–35 · low confidence |
H100 SXM5 64 GB NVIDIA | 64 GB | 2,020 GB/s | Mar 2023 | 51.2 GB | Q5_K_M | Tight |
|
20.3
tok/s
12–33 · low confidence |
H100 PCIe 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 75.6 GB | Q8_0 | Tight |
|
20.3
tok/s
12–33 · low confidence |
H100 SXM5 94 GB NVIDIA | 94 GB | 3,360 GB/s | Mar 2023 | 75.6 GB | Q8_0 | Tight |
|
20.3
tok/s
12–33 · low confidence |
H100 SXM5 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 75.6 GB | Q8_0 | Tight |
|
18.7
tok/s
11–30 · low confidence |
RTX PRO 5000 Blackwell NVIDIA | 48 GB | 1,340 GB/s | Mar 2025 | 43.0 GB | Q4_K_M | Tight |
|
17.9
tok/s
11–29 · low confidence |
A100 SXM4 80 GB NVIDIA | 80 GB | 2,040 GB/s | Nov 2020 | 59.3 GB | Q6_K | Comfortable |
|
17.9
tok/s
11–29 · low confidence |
A100X NVIDIA | 80 GB | 2,040 GB/s | Jun 2021 | 59.3 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
- Nexusflow
- Organisation type
- Industry
- Country
- United States of America
- Published
- 19 July 2024
- Authors
- Evan Frick, Peter Jin, Tianle Li, Karthik Ganesan, Jian Zhang, Jiantao Jiao, Banghua Zhu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Question answering
- 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
70B
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
- Hugging Face
- Nexusflow
Nexus Research License "for personal use only" + llama license applies
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
- Athene-70B: Redefining the Boundaries of Post-Training for Open Models
- Last updated
- 28 November 2025
The extremes
The ten fastest GPUs that run Athene-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 Athene-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 34.9 GB · Q3_K_M · tight 25.5 tok/s
- 02 A100 PCIe 40 GB 40 GB · needs 34.9 GB · Q3_K_M · tight 25.5 tok/s
- 03 A100 SXM4 40 GB 40 GB · needs 34.9 GB · Q3_K_M · tight 25.5 tok/s
- 04 Radeon PRO W7900D 48 GB · needs 43.0 GB · Q4_K_M · tight 9.4 tok/s
- 05 RTX PRO 5000 Blackwell 48 GB · needs 43.0 GB · Q4_K_M · tight 18.7 tok/s
- 06 RTX 5880 Ada Generation 48 GB · needs 43.0 GB · Q4_K_M · tight 12.1 tok/s
- 07 L20 48 GB · needs 43.0 GB · Q4_K_M · tight 12.1 tok/s
- 08 Radeon PRO W7800 48 GB 48 GB · needs 43.0 GB · Q4_K_M · tight 9.4 tok/s
- 09 Radeon PRO W7900 48 GB · needs 43.0 GB · Q4_K_M · tight 9.4 tok/s
- 10 Data Center GPU Max 1100 48 GB · needs 43.0 GB · Q4_K_M · tight 11.2 tok/s
What the numbers mean
What it takes to run this model
Minimum card
A100 PCIe 40 GB
Memory needed
34.9 GB
Fastest
48.4 tok/s
Athene-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 smallest card that holds it is the A100 PCIe 40 GB with 40 GB, running it at Q3_K_M and producing around 25.5 tokens per second.
Top of the range is the B200, at roughly 48.4 tokens per second thanks to 8,000 GB/s of bandwidth.
Background
Athene-70B was published by Nexusflow, in United States of America, in July 2024. It comes out of industry.
It works in Language, and is recorded as doing language modeling/generation, Question answering.
It is derived from Llama 3-70B rather than trained from scratch, which 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. It is published under the Nexusflow organisation on Hugging Face.
Reading the throughput figures
The median result is around 17.1 tokens per second; 50 cards produce text faster than most people read it.
Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.
Its internal architecture is not on file, so memory is approximated from the parameter count and marked accordingly. Expect the real figure to differ, more so at long context.
Step by step
How to choose a GPU for Athene-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 Athene-70B — around 34.9 GB at Q3_K_M. That figure, not the card's headline performance, is what decides whether it runs.
-
02
Decide how long your conversations run
The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context Athene-70B can slip off a card that handles short questions easily.
-
03
Decide how much compression you will accept
The quantisation column varies by card, because a bigger card holds a more accurate copy of Athene-70B — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
-
04
Compare tokens per second, not specifications
The speed ordering for Athene-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 Athene-70B from those with room to spare. Buy for the second if the context might grow.
-
06
Open the card you have settled on
Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Athene-70B.
Answers
Athene-70B — common questions
How much VRAM does Athene-70B need?
About 34.9 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 Athene-70B open source?
Its weights are published, so Athene-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 Athene-70B have?
Athene-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 Athene-70B?
Athene-70B was published by Nexusflow, based in United States of America, categorised as industry.
When was Athene-70B released?
Athene-70B was published in July 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.
What is Athene-70B used for?
Athene-70B works in Language, and is recorded as handling language modeling/generation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download Athene-70B?
Its weights are published under the Nexusflow organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
Can I run Athene-70B 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 Athene-70B is rarely worth using — the nearest miss we calculate is short by 14.2 GB. Every figure here assumes the whole model is on the card.
Would two GPUs run Athene-70B faster?
A second card roughly doubles the memory available but not the generation rate. With 61 cards already able to run Athene-70B alone, the case for pairing is weak.
Why does the quantisation differ between cards for Athene-70B?
Because capacity varies, so does how hard Athene-70B has to be squeezed — 5 distinct levels appear in the table above. Set a minimum quality to compare at one.
How accurate are these Athene-70B speed estimates?
They are calculated from specifications rather than measured, and each carries a range — 29–77 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 Athene-70B?
The smallest card in our catalogue that holds Athene-70B is the A100 PCIe 40 GB, with 40 GB of memory. It runs the model at Q3_K_M using about 34.9 GB, and produces roughly 25.5 tokens per second. 61 cards in total can run it.
How fast is Athene-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 50 of the cards that can run Athene-70B clear that.
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.