Hybrid H3-1.3B TPS calculator

Open weights Stanford University,University at Buffalo 1.3B 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 · 28.4 tok/s

Fastest card

B200

2,606 tok/s · 180 GB

Which GPUs can run Hybrid H3-1.3B?

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

818 cards match

Calculating

					Needs	Quantisation	Fit
2,606 tok/s 1,564–4,170 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	2.1 GB	Q8_0	Comfortable
2,606 tok/s 1,564–4,170 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	2.1 GB	Q8_0	Comfortable
2,081 tok/s 1,249–3,330 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	2.1 GB	Q8_0	Comfortable
2,081 tok/s 1,249–3,330 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	2.1 GB	Q8_0	Comfortable
1,664 tok/s 999–2,663 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	2.1 GB	Q8_0	Comfortable
1,593 tok/s 956–2,549 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.1 GB	Q8_0	Comfortable
1,593 tok/s 956–2,549 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.1 GB	Q8_0	Comfortable
1,525 tok/s 915–2,440 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	2.1 GB	Q8_0	Comfortable
1,353 tok/s 812–2,165 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	2.1 GB	Q8_0	Comfortable
1,353 tok/s 812–2,165 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	2.1 GB	Q8_0	Comfortable
1,353 tok/s 812–2,165 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	2.1 GB	Q8_0	Comfortable
1,284 tok/s 770–2,054 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	2.1 GB	Q8_0	Comfortable
1,095 tok/s 657–1,751 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.1 GB	Q8_0	Comfortable
1,095 tok/s 657–1,751 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	2.1 GB	Q8_0	Comfortable
1,095 tok/s 657–1,751 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	2.1 GB	Q8_0	Comfortable
1,095 tok/s 657–1,751 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.1 GB	Q8_0	Comfortable
1,095 tok/s 657–1,751 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	2.1 GB	Q8_0	Comfortable
834 tok/s 500–1,334 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	2.1 GB	Q8_0	Comfortable
834 tok/s 500–1,334 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	2.1 GB	Q8_0	Comfortable
695 tok/s 417–1,111 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	2.1 GB	Q8_0	Comfortable
680 tok/s 408–1,088 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	2.1 GB	Q8_0	Comfortable
665 tok/s 399–1,063 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	2.1 GB	Q8_0	Comfortable
665 tok/s 399–1,063 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	2.1 GB	Q8_0	Comfortable
665 tok/s 399–1,063 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	2.1 GB	Q8_0	Comfortable
665 tok/s 399–1,063 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	2.1 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

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: 1.3B
Training data: 400,000,000,000 tokens
Epochs: 1
Batch size: 1,048,576

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 H3-1.3B

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 H3-1.3B

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 H3-1.3B

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

2.1 GB

Fastest

2,606 tok/s

Hybrid H3-1.3B reaches a parameter count of 1.3B. That is small enough that hardware is rarely the obstacle, including on cards several years old. The number of cards we track that can run it: 818.

At the low end it is handled by Tesla C1080, with a memory capacity of 4 GB, running it at a compression of Q8_0 and producing around 28.4 tokens per second.

The fastest we calculate for it is B200, generating roughly 2,606 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

Where it came from

Hybrid H3-1.3B was published by Stanford University,University at Buffalo, in the country recorded as United States of America, during December 2022. It comes out of an organisation categorised as academia,Academia.

It works in the domain of Language, and is recorded as performing the task of language modeling/generation.

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.

Understanding the speeds

The median result is around 73.2 tokens per second. Exceeding reading speed outright: 797 of them.

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.

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

Training consumed a corpus of around 400,000,000,000 tokens of text.

Step by step

How to choose a GPU for Hybrid H3-1.3B

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 able to hold Hybrid H3-1.3B, needing around 2.1 GB at a compression of Q8_0. No amount of processing power compensates for a card that cannot hold it.
02

Set the context length you will work at

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect, because at long context a card that handles short questions easily can be dropped by Hybrid H3-1.3B.
03

Set a quality floor

The quantisation column varies by card, because a bigger card holds a more accurate copy, reaching a compression of Q8_0 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

Sort by speed

Sort by speed to see how cards rank for Hybrid H3-1.3B. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 2,606 tok/s.
05

Check the fit verdict before buying

A tight fit runs, but leaves nothing spare for a longer conversation, in the case of Hybrid H3-1.3B. 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 Hybrid H3-1.3B.

Answers

Hybrid H3-1.3B — common questions

Hybrid H3-1.3B— where can I download it?

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

Hybrid H3-1.3B— can I run it if it does not fit in my GPU?

It can be split between the card and system memory, but it generates painfully slowly that way. Every figure here assumes the whole model is resident on the card.

Hybrid H3-1.3B— would two GPUs run it faster?

Two cards buy memory rather than speed, which matters only if one card cannot hold it. The number that can: 818. So a second card is rarely the answer here.

Hybrid H3-1.3B— why does the quantisation differ between cards?

Because capacity varies, so does how hard it has to be squeezed. The number of distinct levels in the table above: 1. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

Hybrid H3-1.3B— how accurate are these speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked, which is why each is published as a range rather than a single number. One example: 1,564–4,170 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Hybrid H3-1.3B— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Tesla C1080, with a memory capacity of 4 GB. It runs the model at a compression of Q8_0 using about 2.1 GB, and produces roughly 28.4 tokens per second. The number of cards able to run it in total: 818.

Hybrid H3-1.3B— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 2,606 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: 797.

Hybrid H3-1.3B— how much VRAM does it need?

It needs about 2.1 GB at a compression of Q8_0, 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.

Hybrid H3-1.3B— can I run it on a GPU holding 8 GB?

Yes. The card CMP 170HX 8 GB, holding 8 GB, runs it at a compression of Q8_0, using about 2.1 GB and generating roughly 485 tokens per second. The fit is comfortable.

Hybrid H3-1.3B— can I run it on a GPU holding 12 GB?

Yes. The card GeForce RTX 3080 Ti, holding 12 GB, runs it at a compression of Q8_0, using about 2.1 GB and generating roughly 297 tokens per second. The fit is comfortable.

Hybrid H3-1.3B— can I run it on a GPU holding 16 GB?

Yes. The card Tesla V100 SXM2 16 GB, holding 16 GB, runs it at a compression of Q8_0, using about 2.1 GB and generating roughly 368 tokens per second. The fit is comfortable.

Hybrid H3-1.3B— 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 Q8_0, using about 2.1 GB and generating roughly 437 tokens per second. The fit is comfortable.

Hybrid H3-1.3B— 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.

Hybrid H3-1.3B— how many parameters does it have?

It has a parameter count of 1.3B. 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.

Hybrid H3-1.3B— who created it?

It was published by Stanford University,University at Buffalo, based in United States of America, an organisation categorised as academia,Academia.

Hybrid H3-1.3B— when was it released?

It 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.

Hybrid H3-1.3B— what is it used for?

It works in the domain of Language, and is recorded as handling the task of 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.

Source

Original publication

Record last updated 25 May 2026

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