GigaChat Lite (GigaChat-20B-A3B) TPS calculator

Open weights Sber 20B 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

295 of 818 cards that can run it

Smallest card that fits

GeForce GTX 1080 Ti

11 GB · Q3_K_M · 131 tok/s

Fastest card

B200

941 tok/s · 180 GB

Which GPUs can run GigaChat Lite (GigaChat-20B-A3B)?

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

295 cards match

Calculating

					Needs	Quantisation	Fit
941 tok/s 565–1,506 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	21.3 GB	Q8_0	Comfortable
941 tok/s 565–1,506 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	21.3 GB	Q8_0	Comfortable
752 tok/s 451–1,202 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	21.3 GB	Q8_0	Comfortable
752 tok/s 451–1,202 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	21.3 GB	Q8_0	Comfortable
601 tok/s 361–962 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	21.3 GB	Q8_0	Comfortable
575 tok/s 345–920 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	21.3 GB	Q8_0	Comfortable
575 tok/s 345–920 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	21.3 GB	Q8_0	Comfortable
551 tok/s 330–881 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	21.3 GB	Q8_0	Comfortable
489 tok/s 293–782 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	21.3 GB	Q8_0	Comfortable
489 tok/s 293–782 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	21.3 GB	Q8_0	Comfortable
489 tok/s 293–782 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	21.3 GB	Q8_0	Comfortable
464 tok/s 278–742 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	21.3 GB	Q8_0	Comfortable
395 tok/s 237–632 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	21.3 GB	Q8_0	Comfortable
395 tok/s 237–632 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	21.3 GB	Q8_0	Comfortable
395 tok/s 237–632 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	21.3 GB	Q8_0	Comfortable
395 tok/s 237–632 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	21.3 GB	Q8_0	Comfortable
395 tok/s 237–632 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	21.3 GB	Q8_0	Comfortable
301 tok/s 181–482 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	21.3 GB	Q8_0	Comfortable
301 tok/s 181–482 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	21.3 GB	Q8_0	Comfortable
290 tok/s 174–463 · low confidence	GeForce RTX 3080 12 GB NVIDIA	12 GB	912 GB/s	Jan 2022	9.7 GB	Q3_K_M	Tight
290 tok/s 174–463 · low confidence	GeForce RTX 3080 Ti NVIDIA	12 GB	912 GB/s	May 2021	9.7 GB	Q3_K_M	Tight
251 tok/s 150–401 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	21.3 GB	Q8_0	Comfortable
245 tok/s 147–393 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	21.3 GB	Q8_0	Comfortable
244 tok/s 146–390 · low confidence	RTX A5000-12Q NVIDIA	12 GB	768 GB/s	Apr 2021	9.7 GB	Q3_K_M	Tight
240 tok/s 144–384 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	21.3 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: Sber
Organisation type: Industry,Government
Country: Russia
Published: 13 December 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, Chat

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: 20B
Training data: 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: 9.9 × 10²² FLOP

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: ai-sage

How it is classified

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

Record confidence: Speculative

Sources

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

Reference: the first open MoE model in Russia
Last updated: 11 February 2026

The extremes

The ten fastest GPUs for GigaChat Lite (GigaChat-20B-A3B)

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 GigaChat Lite (GigaChat-20B-A3B)

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

What it takes to run this model

Minimum card

GeForce GTX 1080 Ti

Memory needed

9.7 GB

Fastest

941 tok/s

With 20B parameters, GigaChat Lite (GigaChat-20B-A3B) lands in the range a serious desktop card can handle once the weights are compressed. 295 of the cards we track can run it.

The smallest card that holds it is the GeForce GTX 1080 Ti with 11 GB, running it at Q3_K_M and producing around 131 tokens per second.

A B200 is the fastest we calculate for it: about 941 tokens per second, from 8,000 GB/s of memory bandwidth.

What this model is

GigaChat Lite (GigaChat-20B-A3B) was published by Sber, in Russia, in December 2024. The organisation is categorised as industry,Government.

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

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 ai-sage organisation on Hugging Face.

What decides the speed

Across every card that can run it, the middle of the range is about 101.7 tokens per second, and 293 of them clear the ten tokens per second that roughly matches reading speed.

This is a mixture-of-experts model, which routes each token through only part of itself. It therefore generates far faster than its total size suggests — while still needing every parameter resident in memory, so it is quick without being cheap to hold.

Without the attention layout on record, the memory column is an approximation. It is close enough to choose hardware by, and least reliable at long context.

What went into building it

Producing it required around 9.9 × 10²² FLOP of arithmetic, which is a statement about the training budget rather than about inference.

Step by step

How to choose a GPU for GigaChat Lite (GigaChat-20B-A3B)

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

Every card here has been checked against GigaChat Lite (GigaChat-20B-A3B) — around 9.7 GB at Q3_K_M. Capacity is the gate — a card either holds it or it does not.
02

Set the context length you will work at

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason GigaChat Lite (GigaChat-20B-A3B) stops fitting a card that seemed fine.
03

Decide how much compression you will accept

Compression is what makes GigaChat Lite (GigaChat-20B-A3B) fit smaller cards, at some cost in accuracy — Q3_K_M on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Sort by speed

Ranking by tokens per second for GigaChat Lite (GigaChat-20B-A3B) follows memory bandwidth, not core counts, which is why the B200 tops it at 941 tok/s.
05

Read the fit column last

A tight fit runs GigaChat Lite (GigaChat-20B-A3B) 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

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for GigaChat Lite (GigaChat-20B-A3B) alone — a card is usually bought for more than one model.

Answers

GigaChat Lite (GigaChat-20B-A3B) — common questions

How much VRAM does GigaChat Lite (GigaChat-20B-A3B) need?

About 9.7 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.

Can I run GigaChat Lite (GigaChat-20B-A3B) on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q3_K_M, using about 9.7 GB and generating roughly 290 tokens per second — a tight fit.

Can I run GigaChat Lite (GigaChat-20B-A3B) on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q5_K_M, using about 14.3 GB and generating roughly 237 tokens per second — a tight fit.

Can I run GigaChat Lite (GigaChat-20B-A3B) on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 21.3 GB and generating roughly 158 tokens per second — a tight fit.

Is GigaChat Lite (GigaChat-20B-A3B) open source?

Its weights are published, so GigaChat Lite (GigaChat-20B-A3B) 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 GigaChat Lite (GigaChat-20B-A3B) have?

GigaChat Lite (GigaChat-20B-A3B) has 20B parameters. 20B total parameters 3.3B active 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 GigaChat Lite (GigaChat-20B-A3B)?

GigaChat Lite (GigaChat-20B-A3B) was published by Sber, based in Russia, categorised as industry,Government.

When was GigaChat Lite (GigaChat-20B-A3B) released?

GigaChat Lite (GigaChat-20B-A3B) 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.

What is GigaChat Lite (GigaChat-20B-A3B) used for?

GigaChat Lite (GigaChat-20B-A3B) works in Language, and is recorded as handling language modeling/generation, Question answering, Chat. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download GigaChat Lite (GigaChat-20B-A3B)?

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

How much compute was used to train GigaChat Lite (GigaChat-20B-A3B)?

Around 9.9 × 10²² FLOP. 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.

Can I run GigaChat Lite (GigaChat-20B-A3B) if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part in system memory drags the whole thing down — the nearest miss we calculate is short by 3.0 GB. Our figures for GigaChat Lite (GigaChat-20B-A3B) assume it is fully resident.

Would two GPUs run GigaChat Lite (GigaChat-20B-A3B) faster?

A second card roughly doubles the memory available but not the generation rate. With 295 cards already able to run GigaChat Lite (GigaChat-20B-A3B) alone, the case for pairing is weak.

Why does the quantisation differ between cards for GigaChat Lite (GigaChat-20B-A3B)?

A larger card holds a more accurate copy. Across the cards that run GigaChat Lite (GigaChat-20B-A3B), 4 compression levels are used; the floor control above pins it to one.

How accurate are these GigaChat Lite (GigaChat-20B-A3B) speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 565–1,506 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 GigaChat Lite (GigaChat-20B-A3B)?

The smallest card in our catalogue that holds GigaChat Lite (GigaChat-20B-A3B) is the GeForce GTX 1080 Ti, with 11 GB of memory. It runs the model at Q3_K_M using about 9.7 GB, and produces roughly 131 tokens per second. 295 cards in total can run it.

How fast is GigaChat Lite (GigaChat-20B-A3B) on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 941 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 293 of the cards that can run GigaChat Lite (GigaChat-20B-A3B) clear that.

Source

Original publication

Record last updated 11 February 2026

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