30B-Lazarus TPS calculator

Open weights Caldera AI 30B parameters May 2023

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

132 cards that can run it

818 cards we hold specifications for

Smallest card that fits

RTX A4500

20 GB · IQ4_XS · 22.2 tok/s

Fastest card

B200

113 tok/s · 180 GB

Which GPUs can run 30B-Lazarus?

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

132 cards match

Calculating

					Needs	Quantisation	Fit
113 tok/s 68–181 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	32.8 GB	Q8_0	Comfortable
113 tok/s 68–181 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	32.8 GB	Q8_0	Comfortable
90.2 tok/s 54–144 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	32.8 GB	Q8_0	Comfortable
90.2 tok/s 54–144 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	32.8 GB	Q8_0	Comfortable
72.1 tok/s 43–115 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	32.8 GB	Q8_0	Comfortable
69.0 tok/s 41–110 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	32.8 GB	Q8_0	Comfortable
69.0 tok/s 41–110 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	32.8 GB	Q8_0	Comfortable
66.1 tok/s 40–106 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	32.8 GB	Q8_0	Comfortable
58.6 tok/s 35–94 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	32.8 GB	Q8_0	Comfortable
58.6 tok/s 35–94 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	32.8 GB	Q8_0	Comfortable
58.6 tok/s 35–94 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	32.8 GB	Q8_0	Comfortable
55.6 tok/s 33–89 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
47.4 tok/s 28–76 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
47.4 tok/s 28–76 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	32.8 GB	Q8_0	Comfortable
47.4 tok/s 28–76 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
47.4 tok/s 28–76 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
47.4 tok/s 28–76 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
43.7 tok/s 26–70 · low confidence	GeForce RTX 5090 D V2 NVIDIA	24 GB	1,340 GB/s	Aug 2025	18.8 GB	Q4_K_M	Tight
39.8 tok/s 24–64 · low confidence	A30X NVIDIA	24 GB	1,220 GB/s	Apr 2021	18.8 GB	Q4_K_M	Tight
38.4 tok/s 23–61 · low confidence	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	25.8 GB	Q6_K	Tight
38.4 tok/s 23–61 · low confidence	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	25.8 GB	Q6_K	Tight
36.7 tok/s 22–59 · low confidence	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	25.8 GB	Q6_K	Tight
36.7 tok/s 22–59 · low confidence	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	25.8 GB	Q6_K	Tight
36.1 tok/s 22–58 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	32.8 GB	Q8_0	Comfortable
36.1 tok/s 22–58 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	32.8 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: Caldera AI
Organisation type: Research collective
Published: 27 May 2023

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

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: 30B
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 (non-commercial)
Training code: Unreleased
Hugging Face: CalderaAI

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: 30B-Lazarus
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run 30B-Lazarus

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 30B-Lazarus

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

RTX A4500

Memory needed

17.1 GB

Fastest

113 tok/s

With 30B parameters, 30B-Lazarus lands in the range a serious desktop card can handle once the weights are compressed. 132 of the cards we track can run it.

At the low end, a RTX A4500 handles it — 20 GB, at IQ4_XS, for about 22.2 tokens per second.

The quickest result comes from a B200 at around 113 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Background

30B-Lazarus was published by Caldera AI, in May 2023. It comes out of research collective.

It works in Language, and is recorded as doing language modeling/generation, Question answering.

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 CalderaAI organisation on Hugging Face.

Reading the throughput figures

Across every card that can run it, the middle of the range is about 22.0 tokens per second, and 104 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 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 30B-Lazarus

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

01

Read the memory figure first

The table lists every card that can hold 30B-Lazarus — around 17.1 GB at IQ4_XS. 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 30B-Lazarus 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 — IQ4_XS on the smallest card that fits. Setting a floor drops the cards that only manage 30B-Lazarus by squeezing it further than you would want.
04

Rank by throughput rather than spec sheet

The speed ordering for 30B-Lazarus is effectively an ordering by memory bandwidth, which is why the B200 tops it at 113 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs 30B-Lazarus but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

Open the card you have settled on

Following a card through to its own page shows every other model it can hold, which is the question that follows once 30B-Lazarus is settled.

Answers

30B-Lazarus — common questions

How fast is 30B-Lazarus on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 113 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 104 of the cards that can run 30B-Lazarus clear that.

How much VRAM does 30B-Lazarus need?

About 17.1 GB at IQ4_XS 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.

Can I run 30B-Lazarus on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q4_K_M, using about 18.8 GB and generating roughly 43.7 tokens per second — a tight fit.

Is 30B-Lazarus open source?

Its weights are published, so 30B-Lazarus 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 30B-Lazarus have?

30B-Lazarus has 30B parameters. 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 30B-Lazarus?

30B-Lazarus was published by Caldera AI, categorised as research collective.

When was 30B-Lazarus released?

30B-Lazarus was published in May 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 30B-Lazarus used for?

30B-Lazarus works in Language, and is recorded as handling language modeling/generation, Question answering. 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 30B-Lazarus?

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

Can I run 30B-Lazarus if it does not fit in my GPU?

It can be split between the card and system memory, but 30B-Lazarus generates painfully slowly that way — the nearest miss we calculate is short by 4.4 GB. Nothing on this page assumes offloading.

Would two GPUs run 30B-Lazarus faster?

A second card roughly doubles the memory available but not the generation rate. With 132 cards already able to run 30B-Lazarus alone, the case for pairing is weak.

Why does the quantisation differ between cards for 30B-Lazarus?

A larger card holds a more accurate copy. Across the cards that run 30B-Lazarus, 5 compression levels are used; the floor control above pins it to one.

How accurate are these 30B-Lazarus speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 68–181 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 30B-Lazarus?

The smallest card in our catalogue that holds 30B-Lazarus is the RTX A4500, with 20 GB of memory. It runs the model at IQ4_XS using about 17.1 GB, and produces roughly 22.2 tokens per second. 132 cards in total can run it.

Source

Original publication

Record last updated 28 November 2025

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.

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