LUXIA-21.4B-Alignment TPS calculator

Open weights SaltLux 21.4B parameters May 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

241 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 7120P

16 GB · Q4_K_M · 10.5 tok/s

Fastest card

B200

158 tok/s · 180 GB

Which GPUs can run LUXIA-21.4B-Alignment?

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.

Search cards

Card memory

Also show cards that cannot run it

241 cards match

Calculating

					Needs	Quantisation	Fit
158 tok/s 95–253 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	23.6 GB	Q8_0	Comfortable
158 tok/s 95–253 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	23.6 GB	Q8_0	Comfortable
126 tok/s 76–202 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	23.6 GB	Q8_0	Comfortable
126 tok/s 76–202 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	23.6 GB	Q8_0	Comfortable
101 tok/s 61–162 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	23.6 GB	Q8_0	Comfortable
96.8 tok/s 58–155 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	23.6 GB	Q8_0	Comfortable
96.8 tok/s 58–155 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	23.6 GB	Q8_0	Comfortable
92.6 tok/s 56–148 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	23.6 GB	Q8_0	Comfortable
82.2 tok/s 49–132 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	23.6 GB	Q8_0	Comfortable
82.2 tok/s 49–132 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	23.6 GB	Q8_0	Comfortable
82.2 tok/s 49–132 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	23.6 GB	Q8_0	Comfortable
78.0 tok/s 47–125 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	23.6 GB	Q8_0	Comfortable
66.5 tok/s 40–106 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	23.6 GB	Q8_0	Comfortable
66.5 tok/s 40–106 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	23.6 GB	Q8_0	Comfortable
66.5 tok/s 40–106 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	23.6 GB	Q8_0	Comfortable
66.5 tok/s 40–106 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	23.6 GB	Q8_0	Comfortable
66.5 tok/s 40–106 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	23.6 GB	Q8_0	Comfortable
51.6 tok/s 31–83 · low confidence	Tesla V100 SXM2 16 GB NVIDIA	16 GB	1,130 GB/s	Nov 2019	13.6 GB	Q4_K_M	Tight
50.6 tok/s 30–81 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	23.6 GB	Q8_0	Comfortable
50.6 tok/s 30–81 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	23.6 GB	Q8_0	Comfortable
43.9 tok/s 26–70 · low confidence	GeForce RTX 5080 NVIDIA	16 GB	960 GB/s	Jan 2025	13.6 GB	Q4_K_M	Tight
42.2 tok/s 25–68 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	23.6 GB	Q8_0	Comfortable
41.3 tok/s 25–66 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	23.6 GB	Q8_0	Comfortable
41.0 tok/s 25–66 · low confidence	Tesla V100 DGXS 16 GB NVIDIA	16 GB	897 GB/s	Mar 2018	13.6 GB	Q4_K_M	Tight
41.0 tok/s 25–66 · low confidence	Tesla V100 PCIe 16 GB NVIDIA	16 GB	897 GB/s	Jun 2017	13.6 GB	Q4_K_M	Tight

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: SaltLux
Organisation type: Industry
Country: Korea (Republic of)
Published: 27 May 2024

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: InternLM2-20B

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: 21.4B
Training data: tokens

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 (unrestricted)
Training code: Unreleased
Hugging Face: saltlux

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: LUXIA-21.4B-Alignment
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run LUXIA-21.4B-Alignment

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 LUXIA-21.4B-Alignment

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

The hardware side

Minimum card

Xeon Phi 7120P

Memory needed

13.6 GB

Fastest

158 tok/s

LUXIA-21.4B-Alignment reaches a parameter count of 21.4B. That lands in the range a serious desktop card can handle once the weights are compressed. The number of cards we track that can run it: 241.

At the low end it is handled by Xeon Phi 7120P, with a memory capacity of 16 GB, running it at a compression of Q4_K_M and producing around 10.5 tokens per second.

The fastest we calculate for it is B200, generating roughly 158 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

What this model is

LUXIA-21.4B-Alignment was published by SaltLux, in the country recorded as Korea (Republic of), during May 2024. The category the publisher falls under is industry.

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

Rather than being trained from scratch, it is derived from InternLM2-20B. 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 saltlux.

What decides the speed

The median result is around 20.5 tokens per second. Exceeding reading speed outright: 195 of them.

It is a dense model, so every parameter is read for every token produced. That makes speed track memory bandwidth almost exactly — a card with twice the bandwidth generates roughly twice as fast.

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 LUXIA-21.4B-Alignment

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

Start from what it actually needs, which is the requirement of LUXIA-21.4B-Alignment, needing around 13.6 GB at a compression of Q4_K_M. No amount of processing power compensates for a card that cannot hold it.
02

Match the context to your actual use

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect, because at long context a card that handles short questions easily can be dropped by LUXIA-21.4B-Alignment.
03

Choose how far you will compress it

Each card runs the least-compressed copy it can hold, reaching a compression of Q4_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

Compare tokens per second, not specifications

Ranking by tokens per second follows memory bandwidth rather than core counts, for LUXIA-21.4B-Alignment. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 158 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 LUXIA-21.4B-Alignment. 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

See what else that card runs

Each card page repeats this sweep for every model we hold, answering what else the hardware is good for beyond LUXIA-21.4B-Alignment.

Answers

LUXIA-21.4B-Alignment — common questions

LUXIA-21.4B-Alignment— what is it used for?

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

LUXIA-21.4B-Alignment— where can I download it?

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

LUXIA-21.4B-Alignment— 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 2.8 GB. Every figure here assumes the whole model is resident on the card.

LUXIA-21.4B-Alignment— 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: 241. So a second card is rarely the answer here.

LUXIA-21.4B-Alignment— 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.

LUXIA-21.4B-Alignment— how accurate are these speed estimates?

They are calculated from specifications rather than measured, and each carries a range. One example: 95–253 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

LUXIA-21.4B-Alignment— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Xeon Phi 7120P, with a memory capacity of 16 GB. It runs the model at a compression of Q4_K_M using about 13.6 GB, and produces roughly 10.5 tokens per second. The number of cards able to run it in total: 241.

LUXIA-21.4B-Alignment— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 158 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: 195.

LUXIA-21.4B-Alignment— how much VRAM does it need?

It needs about 13.6 GB at a compression of Q4_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.

LUXIA-21.4B-Alignment— 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 Q4_K_M, using about 13.6 GB and generating roughly 51.6 tokens per second. The fit is tight.

LUXIA-21.4B-Alignment— 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 Q6_K, using about 18.6 GB and generating roughly 38.5 tokens per second. The fit is tight.

LUXIA-21.4B-Alignment— 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.

LUXIA-21.4B-Alignment— how many parameters does it have?

It has a parameter count of 21.4B. 21.4B. 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.

LUXIA-21.4B-Alignment— who created it?

It was published by SaltLux, based in Korea (Republic of), an organisation categorised as industry.

LUXIA-21.4B-Alignment— when was it released?

It was published in May 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.

Source

Original publication

Record last updated 28 November 2025

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