Seed-OSS-36B-Base TPS calculator

Open weights ByteDance 36B parameters August 2025

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

126 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla M40 24 GB

24 GB · IQ4_XS · 7.1 tok/s

Fastest card

B200

94.1 tok/s · 180 GB

Which GPUs can run Seed-OSS-36B-Base?

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

126 cards match

Calculating

					Needs	Quantisation	Fit
94.1 tok/s 56–151 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	39.2 GB	Q8_0	Comfortable
94.1 tok/s 56–151 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	39.2 GB	Q8_0	Comfortable
75.2 tok/s 45–120 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	39.2 GB	Q8_0	Comfortable
75.2 tok/s 45–120 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	39.2 GB	Q8_0	Comfortable
60.1 tok/s 36–96 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	39.2 GB	Q8_0	Comfortable
57.5 tok/s 35–92 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	39.2 GB	Q8_0	Comfortable
57.5 tok/s 35–92 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	39.2 GB	Q8_0	Comfortable
55.1 tok/s 33–88 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	39.2 GB	Q8_0	Comfortable
48.9 tok/s 29–78 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	39.2 GB	Q8_0	Comfortable
48.9 tok/s 29–78 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	39.2 GB	Q8_0	Comfortable
48.9 tok/s 29–78 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	39.2 GB	Q8_0	Comfortable
46.4 tok/s 28–74 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	39.2 GB	Q8_0	Comfortable
39.5 tok/s 24–63 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	39.2 GB	Q8_0	Comfortable
39.5 tok/s 24–63 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	39.2 GB	Q8_0	Comfortable
39.5 tok/s 24–63 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	39.2 GB	Q8_0	Comfortable
39.5 tok/s 24–63 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	39.2 GB	Q8_0	Comfortable
39.5 tok/s 24–63 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	39.2 GB	Q8_0	Comfortable
39.3 tok/s 24–63 · low confidence	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	26.7 GB	Q5_K_M	Tight
39.3 tok/s 24–63 · low confidence	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	26.7 GB	Q5_K_M	Tight
38.7 tok/s 23–62 · low confidence	GeForce RTX 5090 D V2 NVIDIA	24 GB	1,340 GB/s	Aug 2025	20.4 GB	IQ4_XS	Tight
37.6 tok/s 23–60 · low confidence	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	26.7 GB	Q5_K_M	Tight
37.6 tok/s 23–60 · low confidence	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	26.7 GB	Q5_K_M	Tight
35.2 tok/s 21–56 · low confidence	A30X NVIDIA	24 GB	1,220 GB/s	Apr 2021	20.4 GB	IQ4_XS	Tight
30.1 tok/s 18–48 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	39.2 GB	Q8_0	Comfortable
30.1 tok/s 18–48 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	39.2 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: ByteDance
Organisation type: Industry
Country: China
Published: 21 August 2025

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, System control, Code generation, Mathematical reasoning

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: 36B
Training data: 12,000,000,000,000 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: 2.6 × 10²⁴ 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)
Training code: Unreleased
Hugging Face: ByteDance-Seed

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: Seed-OSS Open-Source Models Release
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Seed-OSS-36B-Base

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 Seed-OSS-36B-Base

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 you need to run it

Minimum card

Tesla M40 24 GB

Memory needed

20.4 GB

Fastest

94.1 tok/s

Seed-OSS-36B-Base reaches a parameter count of 36B. 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: 126.

The least hardware that works is Tesla M40 24 GB, with a memory capacity of 24 GB, running it at a compression of IQ4_XS and producing around 7.1 tokens per second.

At the other end sits B200, generating roughly 94.1 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

Background

Seed-OSS-36B-Base was published by ByteDance, in the country recorded as China, during August 2025. The publishing organisation is categorised as industry.

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

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. On Hugging Face it is published under the organisation ByteDance-Seed.

Reading the throughput figures

The median result is around 20.4 tokens per second. Clearing the ten tokens per second that roughly matches reading speed: 98 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.

Memory here is estimated from size rather than computed from the architecture, which is not recorded for this model — the numbers are indicative rather than exact.

How it was trained

Producing it required arithmetic totalling around 2.6 × 10²⁴ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.

The training set ran to roughly 12,000,000,000,000 tokens of text.

Step by step

How to choose a GPU for Seed-OSS-36B-Base

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

Start from what it actually needs, which is the requirement of Seed-OSS-36B-Base, needing around 20.4 GB at a compression of IQ4_XS. Capacity is the gate — a card either holds it or it does not.
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 Seed-OSS-36B-Base.
03

Set a quality floor

Compression is what makes a model fit smaller cards, at some cost in accuracy, reaching a compression of IQ4_XS 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

Ranking by tokens per second follows memory bandwidth rather than core counts, for Seed-OSS-36B-Base. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 94.1 tok/s.
05

Read the fit column last

Tight means it loads and works with no room to raise the context later, in the case of Seed-OSS-36B-Base. 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

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. A card is usually bought for more than one model, so it is worth a look before buying for Seed-OSS-36B-Base.

Answers

Seed-OSS-36B-Base — common questions

Seed-OSS-36B-Base— 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 4.5 GB. Every figure here assumes the whole model is resident on the card.

Seed-OSS-36B-Base— would two GPUs run it faster?

Capacity adds across cards; throughput does not. The number of cards already holding it on their own: 126. So a second card is rarely the answer here.

Seed-OSS-36B-Base— why does the quantisation differ between cards?

Each card is shown running the least-compressed copy it can hold. The number of distinct compression levels across the cards that fit it: 5. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

Seed-OSS-36B-Base— how accurate are these speed estimates?

They are calculated from specifications rather than measured, and each carries a range. One example: 56–151 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Seed-OSS-36B-Base— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Tesla M40 24 GB, with a memory capacity of 24 GB. It runs the model at a compression of IQ4_XS using about 20.4 GB, and produces roughly 7.1 tokens per second. The number of cards able to run it in total: 126.

Seed-OSS-36B-Base— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 94.1 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: 98.

Seed-OSS-36B-Base— how much VRAM does it need?

It needs about 20.4 GB at a compression of IQ4_XS, 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.

Seed-OSS-36B-Base— 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 IQ4_XS, using about 20.4 GB and generating roughly 38.7 tokens per second. The fit is tight.

Seed-OSS-36B-Base— 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.

Seed-OSS-36B-Base— how many parameters does it have?

It has a parameter count of 36B. 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.

Seed-OSS-36B-Base— who created it?

It was published by ByteDance, based in China, an organisation categorised as industry.

Seed-OSS-36B-Base— when was it released?

It was published in August 2025.

Seed-OSS-36B-Base— 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, System control, Code generation, Mathematical reasoning. These are the areas it was designed around; they describe intent rather than a hard boundary.

Seed-OSS-36B-Base— where can I download it?

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

Seed-OSS-36B-Base— how much compute was used to train it?

Training consumed around 2.6 × 10²⁴ 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.

Source

Original publication

Record last updated 28 November 2025

The other direction

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