Stable Beluga 1 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 · 27.3 tok/s
Fastest card
B200
52.0 tok/s · 180 GB
Which GPUs can run Stable Beluga 1?
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 | |||||
|---|---|---|---|---|---|---|---|
|
52.0
tok/s
31–83 · low confidence |
B200 NVIDIA | 180 GB | 8,000 GB/s | Jan 2024 | 70.5 GB | Q8_0 | Comfortable |
|
52.0
tok/s
31–83 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 70.5 GB | Q8_0 | Comfortable |
|
41.5
tok/s
25–66 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 70.5 GB | Q8_0 | Comfortable |
|
41.5
tok/s
25–66 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 70.5 GB | Q8_0 | Comfortable |
|
33.2
tok/s
20–53 · low confidence |
Radeon Instinct MI300 AMD | 128 GB | 6,550 GB/s | Jan 2023 | 70.5 GB | Q8_0 | Comfortable |
|
31.8
tok/s
19–51 · low confidence |
H200 NVL NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 70.5 GB | Q8_0 | Comfortable |
|
31.8
tok/s
19–51 · low confidence |
H200 SXM 141 GB NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 70.5 GB | Q8_0 | Comfortable |
|
30.4
tok/s
18–49 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 70.5 GB | Q8_0 | Comfortable |
|
28.0
tok/s
17–45 · low confidence |
GRID A100B NVIDIA | 48 GB | 1,870 GB/s | May 2020 | 40.1 GB | Q4_K_M | Tight |
|
27.3
tok/s
16–44 · low confidence |
A100 PCIe 40 GB NVIDIA | 40 GB | 1,560 GB/s | Jun 2020 | 32.6 GB | Q3_K_M | Tight |
|
27.3
tok/s
16–44 · low confidence |
A100 SXM4 40 GB NVIDIA | 40 GB | 1,560 GB/s | May 2020 | 32.6 GB | Q3_K_M | Tight |
|
27.3
tok/s
16–44 · low confidence |
A800 PCIe 40 GB NVIDIA | 40 GB | 1,560 GB/s | Nov 2022 | 32.6 GB | Q3_K_M | Tight |
|
27.0
tok/s
16–43 · low confidence |
Radeon Instinct MI300A AMD | 128 GB | 5,325 GB/s | Dec 2023 | 70.5 GB | Q8_0 | Comfortable |
|
27.0
tok/s
16–43 · low confidence |
Radeon Instinct MI300X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 70.5 GB | Q8_0 | Comfortable |
|
27.0
tok/s
16–43 · low confidence |
Radeon Instinct MI308X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 70.5 GB | Q8_0 | Comfortable |
|
25.6
tok/s
15–41 · low confidence |
H100 NVL 94 GB NVIDIA | 94 GB | 3,940 GB/s | Mar 2023 | 70.5 GB | Q8_0 | Tight |
|
21.8
tok/s
13–35 · low confidence |
H100 PCIe 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 70.5 GB | Q8_0 | Comfortable |
|
21.8
tok/s
13–35 · low confidence |
H100 SXM5 80 GB NVIDIA | 80 GB | 3,360 GB/s | Oct 2022 | 70.5 GB | Q8_0 | Tight |
|
21.8
tok/s
13–35 · low confidence |
H100 SXM5 94 GB NVIDIA | 94 GB | 3,360 GB/s | Mar 2023 | 70.5 GB | Q8_0 | Tight |
|
21.8
tok/s
13–35 · low confidence |
H100 SXM5 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 70.5 GB | Q8_0 | Comfortable |
|
21.8
tok/s
13–35 · low confidence |
H800 SXM5 NVIDIA | 80 GB | 3,360 GB/s | Mar 2023 | 70.5 GB | Q8_0 | Tight |
|
20.1
tok/s
12–32 · low confidence |
RTX PRO 5000 Blackwell NVIDIA | 48 GB | 1,340 GB/s | Mar 2025 | 40.1 GB | Q4_K_M | Tight |
|
19.1
tok/s
11–31 · low confidence |
H100 SXM5 64 GB NVIDIA | 64 GB | 2,020 GB/s | Mar 2023 | 55.3 GB | Q6_K | Tight |
|
16.6
tok/s
10–27 · low confidence |
Radeon Instinct MI250 AMD | 128 GB | 3,280 GB/s | Nov 2021 | 70.5 GB | Q8_0 | Comfortable |
|
16.6
tok/s
10–27 · low confidence |
Radeon Instinct MI250X AMD | 128 GB | 3,280 GB/s | Nov 2021 | 70.5 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
- Stability AI
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 21 July 2023
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language generation
- Base model
- LLaMA-65B
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
- 65.2B
- Training data
- tokens
65.2B
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
non-comm license https://huggingface.co/stabilityai/StableBeluga1-Delta
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
- Meet Stable Beluga 1 and Stable Beluga 2, Our Large and Mighty Instruction Fine-Tuned Language Models
- Last updated
- 28 November 2025
The extremes
The ten fastest GPUs that run Stable Beluga 1
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 52.0 tok/s
- 02 B200 180 GB · 8,000 GB/s · Q8_0 52.0 tok/s
- 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q8_0 41.5 tok/s
- 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q8_0 41.5 tok/s
- 05 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q8_0 33.2 tok/s
- 06 H200 NVL 141 GB · 4,890 GB/s · Q8_0 31.8 tok/s
- 07 H200 SXM 141 GB 141 GB · 4,890 GB/s · Q8_0 31.8 tok/s
- 08 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q8_0 30.4 tok/s
- 09 GRID A100B 48 GB · 1,870 GB/s · Q4_K_M 28.0 tok/s
- 10 A800 PCIe 40 GB 40 GB · 1,560 GB/s · Q3_K_M 27.3 tok/s
The smallest GPUs that still run Stable Beluga 1
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 32.6 GB · Q3_K_M · tight 27.3 tok/s
- 02 A100 PCIe 40 GB 40 GB · needs 32.6 GB · Q3_K_M · tight 27.3 tok/s
- 03 A100 SXM4 40 GB 40 GB · needs 32.6 GB · Q3_K_M · tight 27.3 tok/s
- 04 Radeon PRO W7900D 48 GB · needs 40.1 GB · Q4_K_M · tight 10.1 tok/s
- 05 RTX PRO 5000 Blackwell 48 GB · needs 40.1 GB · Q4_K_M · tight 20.1 tok/s
- 06 RTX 5880 Ada Generation 48 GB · needs 40.1 GB · Q4_K_M · tight 13.0 tok/s
- 07 L20 48 GB · needs 40.1 GB · Q4_K_M · tight 13.0 tok/s
- 08 Radeon PRO W7800 48 GB 48 GB · needs 40.1 GB · Q4_K_M · tight 10.1 tok/s
- 09 Radeon PRO W7900 48 GB · needs 40.1 GB · Q4_K_M · tight 10.1 tok/s
- 10 Data Center GPU Max 1100 48 GB · needs 40.1 GB · Q4_K_M · tight 12.0 tok/s
What the numbers mean
The hardware side
Minimum card
A100 PCIe 40 GB
Memory needed
32.6 GB
Fastest
52.0 tok/s
Stable Beluga 1 sits at 65.2B 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.
At the low end, a A100 PCIe 40 GB handles it — 40 GB, at Q3_K_M, for about 27.3 tokens per second.
Top of the range is the B200, at roughly 52.0 tokens per second thanks to 8,000 GB/s of bandwidth.
About this model
Stable Beluga 1 was published by Stability AI, in United Kingdom of Great Britain and Northern Ireland, in July 2023. industry is the category the publisher falls under.
It works in Language, and is recorded as doing language generation.
It is derived from LLaMA-65B rather than trained from scratch, which is the usual way a specialised model is produced.
Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all.
How fast it runs, and why
Across every card that can run it, the middle of the range is about 13.3 tokens per second, and 56 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.
Memory here is estimated from size rather than computed from the architecture, which is not recorded for this model — the numbers are indicative rather than exact.
Step by step
How to choose a GPU for Stable Beluga 1
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 Stable Beluga 1 — around 32.6 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
The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context Stable Beluga 1 can slip off a card that handles short questions easily.
-
03
Decide how much compression you will accept
Each card runs the least-compressed copy it can hold — Q3_K_M on the smallest card that fits. Setting a floor drops the cards that only manage Stable Beluga 1 by squeezing it further than you would want.
-
04
Compare tokens per second, not specifications
Sort by speed to see how cards rank for Stable Beluga 1. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 52.0 tok/s.
-
05
Check the fit verdict before buying
A tight fit runs Stable Beluga 1 but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
-
06
Check the card from the other side
Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Stable Beluga 1.
Answers
Stable Beluga 1 — common questions
How fast is Stable Beluga 1 on a GPU?
It depends on the card. The quickest we calculate is a B200 at about 52.0 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 56 of the cards that can run Stable Beluga 1 clear that.
How much VRAM does Stable Beluga 1 need?
About 32.6 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 Stable Beluga 1 open source?
Its weights are published, so Stable Beluga 1 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 Stable Beluga 1 have?
Stable Beluga 1 has 65.2B parameters. 65.2B. 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 Stable Beluga 1?
Stable Beluga 1 was published by Stability AI, based in United Kingdom of Great Britain and Northern Ireland, categorised as industry.
When was Stable Beluga 1 released?
Stable Beluga 1 was published in July 2023. 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 Stable Beluga 1 used for?
Stable Beluga 1 works in Language, and is recorded as handling language generation. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
Where can I download Stable Beluga 1?
The weights for Stable Beluga 1 are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
Can I run Stable Beluga 1 if it does not fit in my GPU?
It can be split between the card and system memory, but Stable Beluga 1 generates painfully slowly that way — the nearest miss we calculate is short by 11.3 GB. Nothing on this page assumes offloading.
Would two GPUs run Stable Beluga 1 faster?
Two cards buy memory rather than speed. That matters for Stable Beluga 1 only if one card cannot hold it — 61 can, so a second adds little.
Why does the quantisation differ between cards for Stable Beluga 1?
A larger card holds a more accurate copy. Across the cards that run Stable Beluga 1, 4 compression levels are used; the floor control above pins it to one.
How accurate are these Stable Beluga 1 speed estimates?
They are calculated from specifications rather than measured, and each carries a range — 31–83 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 Stable Beluga 1?
The smallest card in our catalogue that holds Stable Beluga 1 is the A100 PCIe 40 GB, with 40 GB of memory. It runs the model at Q3_K_M using about 32.6 GB, and produces roughly 27.3 tokens per second. 61 cards in total can run it.
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.