SEA-LION V3 Llama3.1 8B TPS calculator

Open weights AI Singapore 8B parameters December 2024

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 SEA-LION V3 Llama3.1 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: AI Singapore
Organisation type: Government
Country: Singapore
Published: 19 December 2024
Authors: AI Singapore

What it does

The problem areas the model was built for. A model can carry several of each.

Domain: Language
Task: Question answering, Chat, Language modeling/generation
Approach: Self-supervised learning
Base model: Llama 3.1-8B
Numerical format: BF16

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: 200,000,000,000 tokens

Training compute

The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.

Training compute: 1.2 × 10²⁴ FLOP
How it was established: Hardware,Operation counting
Fine-tuning compute: 9.4 × 10²¹ FLOP

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 H200 SXM
Chips used: 64
Wall-clock time: 136 hours
Power draw: 88.1 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: Open source
Hugging Face: aisingapore

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Likely above 10²³ FLOP: Yes
Record confidence: Confident

Sources

Where this record came from and when it was last checked.

Reference: SEA-LION V3
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run SEA-LION V3 Llama3.1 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 SEA-LION V3 Llama3.1 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

Hardware requirements in practice

Minimum card

Quadro 6000

Memory needed

5.1 GB

Fastest

424 tok/s

SEA-LION V3 Llama3.1 8B reaches a parameter count of 8B. That is small enough that hardware is rarely the obstacle, including on cards several years old. The number of cards we track that can run it: 582.

The entry point is Quadro 6000, with a memory capacity of 6 GB, running it at a compression of Q3_K_M and producing around 17.4 tokens per second.

At the other end sits B200, generating roughly 424 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

What this model is

SEA-LION V3 Llama3.1 8B was published by AI Singapore, in the country recorded as Singapore, during December 2024. The publishing organisation is categorised as government.

It works in the domain of Language, and is recorded as performing the task of question answering, Chat, Language modeling/generation.

Rather than being trained from scratch, it is derived from Llama 3.1-8B. 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 aisingapore.

What decides the speed

The median result is around 23.8 tokens per second. Producing text faster than most people read it: 551 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.

What went into building it

Training it took a computation budget of roughly 1.2 × 10²⁴ FLOP, on hardware recorded as NVIDIA H200 SXM. That figure measures what producing the model cost, and has no bearing on how fast it answers.

It was trained on a corpus of about 200,000,000,000 tokens of text.

Step by step

How to choose a GPU for SEA-LION V3 Llama3.1 8B

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

01

Check what it needs before anything else

Every card here has been checked against SEA-LION V3 Llama3.1 8B, needing around 5.1 GB at a compression of Q3_K_M. That figure, not the headline performance of a card, is what decides whether it runs.
02

Decide how long your conversations run

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for SEA-LION V3 Llama3.1 8B.
03

Set a quality floor

Compression is what makes a model fit smaller cards, at some cost in accuracy, 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.
04

Rank by throughput rather than spec sheet

The speed ordering is effectively an ordering by memory bandwidth, for SEA-LION V3 Llama3.1 8B. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 424 tok/s.
05

Read the fit column last

The fit column separates cards that just manage it from those with room to spare, in the case of SEA-LION V3 Llama3.1 8B. 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.
06

Open the card you have settled on

Each card page repeats this sweep for every model we hold, answering what else the hardware is good for beyond SEA-LION V3 Llama3.1 8B.

Answers

SEA-LION V3 Llama3.1 8B — common questions

SEA-LION V3 Llama3.1 8B— who created it?

It was published by AI Singapore, based in Singapore, an organisation categorised as government.

SEA-LION V3 Llama3.1 8B— when was it released?

It was published in December 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.

SEA-LION V3 Llama3.1 8B— what is it used for?

It works in the domain of Language, and is recorded as handling the task of question answering, Chat, Language modeling/generation. These are the areas it was designed around; they describe intent rather than a hard boundary.

SEA-LION V3 Llama3.1 8B— where can I download it?

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

SEA-LION V3 Llama3.1 8B— how much compute was used to train it?

Training consumed around 1.2 × 10²⁴ FLOP, on hardware recorded as NVIDIA H200 SXM. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.

SEA-LION V3 Llama3.1 8B— can I run it if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part held in system memory drags the whole thing down. The nearest miss we calculate falls short by 1.5 GB. Every figure here assumes the whole model is resident on the card.

SEA-LION V3 Llama3.1 8B— would two GPUs run it faster?

Capacity adds across cards; throughput does not. The number of cards already holding it on their own: 582. So a second card is rarely the answer here.

SEA-LION V3 Llama3.1 8B— why does the quantisation differ between cards?

A larger card holds a more accurate copy. The number of compression levels used across the cards that run it: 4. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

SEA-LION V3 Llama3.1 8B— 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: 360–508 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

SEA-LION V3 Llama3.1 8B— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Quadro 6000, with a memory capacity of 6 GB. It runs the model at a compression of Q3_K_M using about 5.1 GB, and produces roughly 17.4 tokens per second. The number of cards able to run it in total: 582.

SEA-LION V3 Llama3.1 8B— how fast is it on a GPU?

It depends on the card. The quickest we calculate is 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 the number of cards clearing that: 551.

SEA-LION V3 Llama3.1 8B— how much VRAM does it need?

It needs about 5.1 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.

SEA-LION V3 Llama3.1 8B— can I run it on a GPU holding 8 GB?

Yes. The card CMP 170HX 8 GB, holding 8 GB, runs it at a compression of Q5_K_M, using about 7.0 GB and generating roughly 141 tokens per second. The fit is tight.

SEA-LION V3 Llama3.1 8B— can I run it on a GPU holding 12 GB?

Yes. The card GeForce RTX 3080 Ti, holding 12 GB, runs it at a compression of Q8_0, using about 9.8 GB and generating roughly 48.3 tokens per second. The fit is tight.

SEA-LION V3 Llama3.1 8B— can I run it on a GPU holding 16 GB?

Yes. The card Tesla V100 SXM2 16 GB, holding 16 GB, runs it at a compression of Q8_0, using about 9.8 GB and generating roughly 59.8 tokens per second. The fit is comfortable.

SEA-LION V3 Llama3.1 8B— can I run it on a GPU holding 24 GB?

Yes. The card GeForce RTX 5090 D V2, holding 24 GB, runs it at a compression of Q8_0, using about 9.8 GB and generating roughly 70.9 tokens per second. The fit is comfortable.

SEA-LION V3 Llama3.1 8B— 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.

SEA-LION V3 Llama3.1 8B— how many parameters does it have?

It has a parameter count of 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.

Source

Original publication

Record last updated 28 November 2025

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