Hybrid H3-355M TPS calculator

Open weights Stanford University,University at Buffalo 355M parameters December 2022

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

818 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla C1080

4 GB · Q8_0 · 104 tok/s

Fastest card

B200

9,544 tok/s · 180 GB

Which GPUs can run Hybrid H3-355M?

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

818 cards match

Calculating

					Needs	Quantisation	Fit
9,544 tok/s 5,727–15,271 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.1 GB	Q8_0	Comfortable
9,544 tok/s 5,727–15,271 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.1 GB	Q8_0	Comfortable
7,621 tok/s 4,573–12,194 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.1 GB	Q8_0	Comfortable
7,621 tok/s 4,573–12,194 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.1 GB	Q8_0	Comfortable
6,095 tok/s 3,657–9,752 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.1 GB	Q8_0	Comfortable
5,834 tok/s 3,500–9,334 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.1 GB	Q8_0	Comfortable
5,834 tok/s 3,500–9,334 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.1 GB	Q8_0	Comfortable
5,583 tok/s 3,350–8,933 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.1 GB	Q8_0	Comfortable
4,955 tok/s 2,973–7,928 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.1 GB	Q8_0	Comfortable
4,955 tok/s 2,973–7,928 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.1 GB	Q8_0	Comfortable
4,955 tok/s 2,973–7,928 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.1 GB	Q8_0	Comfortable
4,701 tok/s 2,820–7,521 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
4,009 tok/s 2,405–6,414 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
4,009 tok/s 2,405–6,414 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.1 GB	Q8_0	Comfortable
4,009 tok/s 2,405–6,414 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
4,009 tok/s 2,405–6,414 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
4,009 tok/s 2,405–6,414 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
3,052 tok/s 1,831–4,884 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.1 GB	Q8_0	Comfortable
3,052 tok/s 1,831–4,884 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.1 GB	Q8_0	Comfortable
2,544 tok/s 1,526–4,070 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.1 GB	Q8_0	Comfortable
2,489 tok/s 1,494–3,983 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.1 GB	Q8_0	Comfortable
2,434 tok/s 1,460–3,894 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.1 GB	Q8_0	Comfortable
2,434 tok/s 1,460–3,894 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.1 GB	Q8_0	Comfortable
2,434 tok/s 1,460–3,894 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.1 GB	Q8_0	Comfortable
2,434 tok/s 1,460–3,894 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	1.1 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: Stanford University,University at Buffalo
Organisation type: Academia,Academia
Country: United States of America
Published: 28 December 2022
Authors: Daniel Y. Fu, Tri Dao, Khaled K. Saab, Armin W. Thomas, Atri Rudra, Christopher Ré

What it does

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

Domain: Language
Task: Language modeling/generation
Numerical format: FP16

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: 355M
Training data: tokens
Epochs: 1

The training run

What it physically took to train: which chips, how many, for how long, and what that drew from the wall.

Training hardware: NVIDIA A100
Chips used: 8
Power draw: 6.4 kW

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: Open source

How it is classified

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

Record confidence: Confident
Citations: 636
Benchmark data: Hybrid H3-355M

Sources

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

Reference: Hungry Hungry Hippos: Towards Language Modeling with State Space Models
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run Hybrid H3-355M

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 Hybrid H3-355M

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

Tesla C1080

Memory needed

1.1 GB

Fastest

9,544 tok/s

Hybrid H3-355M is small enough at 355M parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

At the low end, a Tesla C1080 handles it — 4 GB, at Q8_0, for about 104 tokens per second.

At the other end, a B200 generates roughly 9,544 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

Background

Hybrid H3-355M was published by Stanford University,University at Buffalo, in United States of America, in December 2022. It comes out of academia,Academia.

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

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

The median result is around 268.0 tokens per second; 817 cards produce text faster than most people read it.

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.

Step by step

How to choose a GPU for Hybrid H3-355M

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

Look at what Hybrid H3-355M actually needs — around 1.1 GB at Q8_0. 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: at long context Hybrid H3-355M can slip off a card that handles short questions easily.
03

Choose how far you will compress it

Compression is what makes Hybrid H3-355M fit smaller cards, at some cost in accuracy — Q8_0 on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Rank by throughput rather than spec sheet

The speed ordering for Hybrid H3-355M is effectively an ordering by memory bandwidth, which is why the B200 tops it at 9,544 tok/s.
05

Read the fit column last

Tight means Hybrid H3-355M loads and works, with no room to raise the context later. Comfortable means you can. The difference matters more than a few tokens per second.
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 Hybrid H3-355M is settled.

Answers

Hybrid H3-355M — common questions

Can I run Hybrid H3-355M on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 1.1 GB and generating roughly 1,348 tokens per second — a comfortable fit.

Can I run Hybrid H3-355M on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 1.1 GB and generating roughly 1,599 tokens per second — a comfortable fit.

Is Hybrid H3-355M open source?

Its weights are published, so Hybrid H3-355M 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 Hybrid H3-355M have?

Hybrid H3-355M has 355M 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 Hybrid H3-355M?

Hybrid H3-355M was published by Stanford University,University at Buffalo, based in United States of America, categorised as academia,Academia.

When was Hybrid H3-355M released?

Hybrid H3-355M was published in December 2022. 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 Hybrid H3-355M used for?

Hybrid H3-355M works in Language, and is recorded as handling language modeling/generation. 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 Hybrid H3-355M?

The weights for Hybrid H3-355M are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

Can I run Hybrid H3-355M if it does not fit in my GPU?

It can be split between the card and system memory, but Hybrid H3-355M generates painfully slowly that way. Nothing on this page assumes offloading.

Would two GPUs run Hybrid H3-355M faster?

A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run Hybrid H3-355M alone, the case for pairing is weak.

Why does the quantisation differ between cards for Hybrid H3-355M?

Because capacity varies, so does how hard Hybrid H3-355M has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these Hybrid H3-355M speed estimates?

These are estimates with real error bars. The fastest result here, 5,727–15,271 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

What GPU do I need to run Hybrid H3-355M?

The smallest card in our catalogue that holds Hybrid H3-355M is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.1 GB, and produces roughly 104 tokens per second. 818 cards in total can run it.

How fast is Hybrid H3-355M on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 9,544 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 817 of the cards that can run Hybrid H3-355M clear that.

How much VRAM does Hybrid H3-355M need?

About 1.1 GB at Q8_0 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 Hybrid H3-355M on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 1.1 GB and generating roughly 1,778 tokens per second — a comfortable fit.

Can I run Hybrid H3-355M on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 1.1 GB and generating roughly 1,089 tokens per second — a comfortable fit.

Source

Original publication

Record last updated 25 May 2026

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