DeciCoder-6B TPS calculator

Open weights Deci AI 6B parameters January 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

589 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla K20c

5 GB · Q4_K_M · 28.8 tok/s

Fastest card

B200

565 tok/s · 180 GB

Which GPUs can run DeciCoder-6B?

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

589 cards match

Calculating

					Needs	Quantisation	Fit
565 tok/s 339–904 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	7.1 GB	Q8_0	Comfortable
565 tok/s 339–904 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	7.1 GB	Q8_0	Comfortable
451 tok/s 271–721 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	7.1 GB	Q8_0	Comfortable
451 tok/s 271–721 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	7.1 GB	Q8_0	Comfortable
361 tok/s 216–577 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	7.1 GB	Q8_0	Comfortable
345 tok/s 207–552 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	7.1 GB	Q8_0	Comfortable
345 tok/s 207–552 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	7.1 GB	Q8_0	Comfortable
330 tok/s 198–529 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	7.1 GB	Q8_0	Comfortable
293 tok/s 176–469 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	7.1 GB	Q8_0	Comfortable
293 tok/s 176–469 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	7.1 GB	Q8_0	Comfortable
293 tok/s 176–469 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	7.1 GB	Q8_0	Comfortable
278 tok/s 167–445 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	7.1 GB	Q8_0	Comfortable
237 tok/s 142–379 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	7.1 GB	Q8_0	Comfortable
237 tok/s 142–379 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	7.1 GB	Q8_0	Comfortable
237 tok/s 142–379 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	7.1 GB	Q8_0	Comfortable
237 tok/s 142–379 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	7.1 GB	Q8_0	Comfortable
237 tok/s 142–379 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	7.1 GB	Q8_0	Comfortable
181 tok/s 108–289 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	7.1 GB	Q8_0	Comfortable
181 tok/s 108–289 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	7.1 GB	Q8_0	Comfortable
150 tok/s 90–241 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	7.1 GB	Q8_0	Comfortable
147 tok/s 88–236 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	7.1 GB	Q8_0	Comfortable
144 tok/s 86–230 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	7.1 GB	Q8_0	Comfortable
144 tok/s 86–230 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	7.1 GB	Q8_0	Comfortable
144 tok/s 86–230 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	7.1 GB	Q8_0	Comfortable
144 tok/s 86–230 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	7.1 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: Deci AI
Organisation type: Industry
Country: Israel
Published: 15 January 2024
Authors: DeciAI Research Team

What it does

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

Domain: Language
Task: Chat, Code autocompletion

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: 6B
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: 7.6 × 10²¹ FLOP
How it was established: Operation counting

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)

How it is classified

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

Record confidence: Likely

Sources

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

Reference: Model Card for DeciCoder-6B
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run DeciCoder-6B

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 DeciCoder-6B

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

Tesla K20c

Memory needed

4.3 GB

Fastest

565 tok/s

DeciCoder-6B is small enough at 6B parameters that hardware is rarely the obstacle — 589 of the cards we track can run it, including cards several years old.

The smallest card that holds it is the Tesla K20c with 5 GB, running it at Q4_K_M and producing around 28.8 tokens per second.

Top of the range is the B200, at roughly 565 tokens per second thanks to 8,000 GB/s of bandwidth.

Background

DeciCoder-6B was published by Deci AI, in Israel, in January 2024. The organisation is categorised as industry.

It works in Language, and is recorded as doing chat, Code autocompletion.

The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.

Reading the throughput figures

Half the cards that hold it manage more than 25.4 tokens per second, and 539 exceed reading speed outright.

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.

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.

How it was trained

Training it took roughly 7.6 × 10²¹ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.

Step by step

How to choose a GPU for DeciCoder-6B

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 DeciCoder-6B — around 4.3 GB at Q4_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

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason DeciCoder-6B stops fitting a card that seemed fine.
03

Decide how much compression you will accept

The quantisation column varies by card, because a bigger card holds a more accurate copy of DeciCoder-6B — Q4_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Compare tokens per second, not specifications

The speed ordering for DeciCoder-6B is effectively an ordering by memory bandwidth, which is why the B200 tops it at 565 tok/s.
05

Check the fit verdict before buying

The fit column separates cards that just manage DeciCoder-6B from those with room to spare. Buy for the second if the context might grow.
06

Open the card you have settled on

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for DeciCoder-6B alone — a card is usually bought for more than one model.

Answers

DeciCoder-6B — common questions

When was DeciCoder-6B released?

DeciCoder-6B was published in January 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 DeciCoder-6B used for?

DeciCoder-6B works in Language, and is recorded as handling chat, Code autocompletion. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download DeciCoder-6B?

The weights for DeciCoder-6B are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

How much compute was used to train DeciCoder-6B?

Around 7.6 × 10²¹ 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 DeciCoder-6B if it does not fit in my GPU?

Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded DeciCoder-6B is rarely worth using — the nearest miss we calculate is short by 0.7 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run DeciCoder-6B faster?

Two cards buy memory rather than speed. That matters for DeciCoder-6B only if one card cannot hold it — 589 can, so a second adds little.

Why does the quantisation differ between cards for DeciCoder-6B?

Because capacity varies, so does how hard DeciCoder-6B has to be squeezed — 3 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these DeciCoder-6B speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked. That is why each is published as a range such as 339–904 tok/s on the B200 rather than a single number.

What GPU do I need to run DeciCoder-6B?

The smallest card in our catalogue that holds DeciCoder-6B is the Tesla K20c, with 5 GB of memory. It runs the model at Q4_K_M using about 4.3 GB, and produces roughly 28.8 tokens per second. 589 cards in total can run it.

How fast is DeciCoder-6B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 565 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 539 of the cards that can run DeciCoder-6B clear that.

How much VRAM does DeciCoder-6B need?

About 4.3 GB at Q4_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 DeciCoder-6B on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 7.1 GB and generating roughly 105 tokens per second — a tight fit.

Can I run DeciCoder-6B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 7.1 GB and generating roughly 64.4 tokens per second — a comfortable fit.

Can I run DeciCoder-6B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 7.1 GB and generating roughly 79.8 tokens per second — a comfortable fit.

Can I run DeciCoder-6B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 7.1 GB and generating roughly 94.6 tokens per second — a comfortable fit.

Is DeciCoder-6B open source?

Its weights are published, so DeciCoder-6B 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 DeciCoder-6B have?

DeciCoder-6B has 6B parameters. 6B. 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 DeciCoder-6B?

DeciCoder-6B was published by Deci AI, based in Israel, categorised as industry.

Source

Original publication

Record last updated 28 November 2025

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