Orion Star (猎户星空大模型) TPS calculator

Open weights Beijing OrionStar Technology Co., Ltd. 14B 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

306 cards that can run it

818 cards we hold specifications for

Smallest card that fits

P102-101

10 GB · IQ4_XS · 20.2 tok/s

Fastest card

B200

242 tok/s · 180 GB

Which GPUs can run Orion Star (猎户星空大模型)?

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

306 cards match

Calculating

					Needs	Quantisation	Fit
242 tok/s 145–387 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	15.7 GB	Q8_0	Comfortable
242 tok/s 145–387 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	15.7 GB	Q8_0	Comfortable
193 tok/s 116–309 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	15.7 GB	Q8_0	Comfortable
193 tok/s 116–309 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	15.7 GB	Q8_0	Comfortable
155 tok/s 93–247 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	15.7 GB	Q8_0	Comfortable
148 tok/s 89–237 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	15.7 GB	Q8_0	Comfortable
148 tok/s 89–237 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	15.7 GB	Q8_0	Comfortable
142 tok/s 85–227 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	15.7 GB	Q8_0	Comfortable
126 tok/s 75–201 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	15.7 GB	Q8_0	Comfortable
126 tok/s 75–201 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	15.7 GB	Q8_0	Comfortable
126 tok/s 75–201 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	15.7 GB	Q8_0	Comfortable
119 tok/s 72–191 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	15.7 GB	Q8_0	Comfortable
116 tok/s 70–185 · low confidence	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.4 GB	IQ4_XS	Tight
102 tok/s 61–163 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	15.7 GB	Q8_0	Comfortable
102 tok/s 61–163 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	15.7 GB	Q8_0	Comfortable
102 tok/s 61–163 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	15.7 GB	Q8_0	Comfortable
102 tok/s 61–163 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	15.7 GB	Q8_0	Comfortable
102 tok/s 61–163 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	15.7 GB	Q8_0	Comfortable
77.4 tok/s 46–124 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	15.7 GB	Q8_0	Comfortable
77.4 tok/s 46–124 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	15.7 GB	Q8_0	Comfortable
64.5 tok/s 39–103 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	15.7 GB	Q8_0	Comfortable
63.1 tok/s 38–101 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	15.7 GB	Q8_0	Comfortable
61.7 tok/s 37–99 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	15.7 GB	Q8_0	Comfortable
61.7 tok/s 37–99 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	15.7 GB	Q8_0	Comfortable
61.7 tok/s 37–99 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	15.7 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: Beijing OrionStar Technology Co., Ltd.
Organisation type: Industry
Country: China
Published: 21 January 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, Chat, Text summarization, Code 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: 14B
Training data: tokens

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)
Hugging Face: OrionStarAI

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.

Last updated: 11 February 2026

The extremes

The ten fastest GPUs that run Orion Star (猎户星空大模型)

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 Orion Star (猎户星空大模型)

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

P102-101

Memory needed

8.4 GB

Fastest

242 tok/s

Orion Star (猎户星空大模型) reaches a parameter count of 14B. 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: 306.

The least hardware that works is P102-101, with a memory capacity of 10 GB, running it at a compression of IQ4_XS and producing around 20.2 tokens per second.

The fastest we calculate for it is B200, generating roughly 242 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

About this model

Orion Star (猎户星空大模型) was published by Beijing OrionStar Technology Co., Ltd., in the country recorded as China, during January 2024. It comes out of an organisation categorised as industry.

It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Chat, Text summarization, Code 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. On Hugging Face it is published under the organisation OrionStarAI.

How fast it runs, and why

Half the cards that hold it manage more than 20.4 tokens per second. Producing text faster than most people read it: 268 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.

Its internal architecture is not on file, so memory is approximated from the parameter count and marked accordingly. Expect the real figure to differ, more so at long context.

Step by step

How to choose a GPU for Orion Star (猎户星空大模型)

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 Orion Star (猎户星空大模型), needing around 8.4 GB at a compression of IQ4_XS. Capacity is the gate — a card either holds it or it does not.
02

Set the context length you will work at

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for Orion Star (猎户星空大模型).
03

Decide how much compression you will accept

Each card runs the least-compressed copy it can hold, 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

Sort by speed to see how cards rank for Orion Star (猎户星空大模型). It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 242 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 Orion Star (猎户星空大模型). 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

Each card page repeats this sweep for every model we hold, answering what else the hardware is good for beyond Orion Star (猎户星空大模型).

Answers

Orion Star (猎户星空大模型) — common questions

Orion Star (猎户星空大模型)— 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 15.7 GB and generating roughly 40.5 tokens per second. The fit is comfortable.

Orion Star (猎户星空大模型)— 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.

Orion Star (猎户星空大模型)— how many parameters does it have?

It has a parameter count of 14B. 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.

Orion Star (猎户星空大模型)— who created it?

It was published by Beijing OrionStar Technology Co., Ltd., based in China, an organisation categorised as industry.

Orion Star (猎户星空大模型)— when was it released?

It 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.

Orion Star (猎户星空大模型)— what is it used for?

It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Chat, Text summarization, Code 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.

Orion Star (猎户星空大模型)— where can I download it?

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

Orion Star (猎户星空大模型)— 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. The nearest miss we calculate falls short by 2.0 GB. Every figure here assumes the whole model is resident on the card.

Orion Star (猎户星空大模型)— would two GPUs run it faster?

Capacity adds across cards; throughput does not. The number of cards already holding it on their own: 306. So a second card is rarely the answer here.

Orion Star (猎户星空大模型)— 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.

Orion Star (猎户星空大模型)— how accurate are these speed estimates?

They are calculated from specifications rather than measured, and each carries a range. One example: 145–387 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Orion Star (猎户星空大模型)— what GPU do I need to run it?

The smallest card in our catalogue that holds it is P102-101, with a memory capacity of 10 GB. It runs the model at a compression of IQ4_XS using about 8.4 GB, and produces roughly 20.2 tokens per second. The number of cards able to run it in total: 306.

Orion Star (猎户星空大模型)— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 242 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: 268.

Orion Star (猎户星空大模型)— how much VRAM does it need?

It needs about 8.4 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.

Orion Star (猎户星空大模型)— 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 Q5_K_M, using about 10.8 GB and generating roughly 49.3 tokens per second. The fit is tight.

Orion Star (猎户星空大模型)— 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 Q6_K, using about 12.4 GB and generating roughly 49.7 tokens per second. The fit is tight.

Source

Original publication

Record last updated 11 February 2026

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