Qwen2.5-Math-1.5B TPS calculator

Open weights Alibaba 1.5B parameters September 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

818 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla C1080

4 GB · Q8_0 · 24.6 tok/s

Fastest card

B200

2,259 tok/s · 180 GB

Which GPUs can run Qwen2.5-Math-1.5B?

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
2,259 tok/s 1,355–3,614 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	2.3 GB	Q8_0	Comfortable
2,259 tok/s 1,355–3,614 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	2.3 GB	Q8_0	Comfortable
1,804 tok/s 1,082–2,886 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	2.3 GB	Q8_0	Comfortable
1,804 tok/s 1,082–2,886 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	2.3 GB	Q8_0	Comfortable
1,443 tok/s 866–2,308 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	2.3 GB	Q8_0	Comfortable
1,381 tok/s 828–2,209 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.3 GB	Q8_0	Comfortable
1,381 tok/s 828–2,209 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.3 GB	Q8_0	Comfortable
1,321 tok/s 793–2,114 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	2.3 GB	Q8_0	Comfortable
1,173 tok/s 704–1,876 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	2.3 GB	Q8_0	Comfortable
1,173 tok/s 704–1,876 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	2.3 GB	Q8_0	Comfortable
1,173 tok/s 704–1,876 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	2.3 GB	Q8_0	Comfortable
1,112 tok/s 667–1,780 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
949 tok/s 569–1,518 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
949 tok/s 569–1,518 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	2.3 GB	Q8_0	Comfortable
949 tok/s 569–1,518 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
949 tok/s 569–1,518 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
949 tok/s 569–1,518 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
722 tok/s 433–1,156 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	2.3 GB	Q8_0	Comfortable
722 tok/s 433–1,156 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	2.3 GB	Q8_0	Comfortable
602 tok/s 361–963 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	2.3 GB	Q8_0	Comfortable
589 tok/s 353–943 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	2.3 GB	Q8_0	Comfortable
576 tok/s 346–922 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	2.3 GB	Q8_0	Comfortable
576 tok/s 346–922 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	2.3 GB	Q8_0	Comfortable
576 tok/s 346–922 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	2.3 GB	Q8_0	Comfortable
576 tok/s 346–922 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	2.3 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: Alibaba
Organisation type: Industry
Country: China
Published: 19 September 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, Quantitative reasoning, Mathematical reasoning
Base model: Qwen2.5-1.5B

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: 1.5B
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: 1.8 × 10²³ FLOP
How it was established: Operation counting
Fine-tuning compute: 9 × 10²¹ FLOP

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: Qwen

How it is classified

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

Likely above 10²³ FLOP: Yes
Record confidence: Confident

Sources

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

Reference: Qwen2.5-Math: The world's leading open-sourced mathematical LLMs
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Qwen2.5-Math-1.5B

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 Qwen2.5-Math-1.5B

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

Hardware requirements in practice

Minimum card

Tesla C1080

Memory needed

2.3 GB

Fastest

2,259 tok/s

Qwen2.5-Math-1.5B is small enough at 1.5B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The entry point is the Tesla C1080: 4 GB of memory, Q8_0 compression, roughly 24.6 tokens per second.

Top of the range is the B200, at roughly 2,259 tokens per second thanks to 8,000 GB/s of bandwidth.

Where it came from

Qwen2.5-Math-1.5B was published by Alibaba, in China, in September 2024. It comes out of industry.

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

Its starting point was Qwen2.5-1.5B — most models at this scale are adapted from an existing base rather than built from nothing.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. It is published under the Qwen organisation on Hugging Face.

Understanding the speeds

The median result is around 63.4 tokens per second; 796 cards produce text faster than most people read it.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

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

The training run consumed about 1.8 × 10²³ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

Step by step

How to choose a GPU for Qwen2.5-Math-1.5B

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

Look at what Qwen2.5-Math-1.5B actually needs — around 2.3 GB at Q8_0. No amount of processing power compensates for a card that cannot hold it.
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 Qwen2.5-Math-1.5B.
03

Decide how much compression you will accept

Each card runs the least-compressed copy it can hold — Q8_0 on the smallest card that fits. Setting a floor drops the cards that only manage Qwen2.5-Math-1.5B by squeezing it further than you would want.
04

Compare tokens per second, not specifications

Sort by speed to see how cards rank for Qwen2.5-Math-1.5B. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 2,259 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs Qwen2.5-Math-1.5B but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

Check the card from the other side

Following a card through to its own page shows every other model it can hold, which is the question that follows once Qwen2.5-Math-1.5B is settled.

Answers

Qwen2.5-Math-1.5B — common questions

Can I run Qwen2.5-Math-1.5B on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 2.3 GB and generating roughly 421 tokens per second — a comfortable fit.

Can I run Qwen2.5-Math-1.5B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 2.3 GB and generating roughly 258 tokens per second — a comfortable fit.

Can I run Qwen2.5-Math-1.5B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 2.3 GB and generating roughly 319 tokens per second — a comfortable fit.

Can I run Qwen2.5-Math-1.5B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 2.3 GB and generating roughly 378 tokens per second — a comfortable fit.

Is Qwen2.5-Math-1.5B open source?

Its weights are published, so Qwen2.5-Math-1.5B 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 Qwen2.5-Math-1.5B have?

Qwen2.5-Math-1.5B has 1.5B parameters. 1.5B. 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 Qwen2.5-Math-1.5B?

Qwen2.5-Math-1.5B was published by Alibaba, based in China, categorised as industry.

When was Qwen2.5-Math-1.5B released?

Qwen2.5-Math-1.5B was published in September 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 Qwen2.5-Math-1.5B used for?

Qwen2.5-Math-1.5B works in Language, and is recorded as handling language modeling/generation, Quantitative reasoning, Mathematical reasoning. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Qwen2.5-Math-1.5B?

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

How much compute was used to train Qwen2.5-Math-1.5B?

Around 1.8 × 10²³ 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 Qwen2.5-Math-1.5B if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part in system memory drags the whole thing down. Our figures for Qwen2.5-Math-1.5B assume it is fully resident.

Would two GPUs run Qwen2.5-Math-1.5B faster?

Two cards buy memory rather than speed. That matters for Qwen2.5-Math-1.5B only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for Qwen2.5-Math-1.5B?

Each card is shown running the least-compressed copy it can hold, and Qwen2.5-Math-1.5B appears at 1 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these Qwen2.5-Math-1.5B 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 1,355–3,614 tok/s on the B200 rather than a single number.

What GPU do I need to run Qwen2.5-Math-1.5B?

The smallest card in our catalogue that holds Qwen2.5-Math-1.5B is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 2.3 GB, and produces roughly 24.6 tokens per second. 818 cards in total can run it.

How fast is Qwen2.5-Math-1.5B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 2,259 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 796 of the cards that can run Qwen2.5-Math-1.5B clear that.

How much VRAM does Qwen2.5-Math-1.5B need?

About 2.3 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.

Source

Original publication

Record last updated 28 November 2025

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