Qwen2.5-Coder (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 · 23.9 tok/s

Fastest card

B200

2,200 tok/s · 180 GB

Which GPUs can run Qwen2.5-Coder (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.

Search cards

Card memory

Also show cards that cannot run it

818 cards match

Calculating

					Needs	Quantisation	Fit
2,200 tok/s 1,320–3,520 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	2.3 GB	Q8_0	Comfortable
2,200 tok/s 1,320–3,520 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	2.3 GB	Q8_0	Comfortable
1,757 tok/s 1,054–2,811 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	2.3 GB	Q8_0	Comfortable
1,757 tok/s 1,054–2,811 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	2.3 GB	Q8_0	Comfortable
1,405 tok/s 843–2,248 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	2.3 GB	Q8_0	Comfortable
1,345 tok/s 807–2,152 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.3 GB	Q8_0	Comfortable
1,345 tok/s 807–2,152 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.3 GB	Q8_0	Comfortable
1,287 tok/s 772–2,059 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	2.3 GB	Q8_0	Comfortable
1,142 tok/s 685–1,828 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	2.3 GB	Q8_0	Comfortable
1,142 tok/s 685–1,828 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	2.3 GB	Q8_0	Comfortable
1,142 tok/s 685–1,828 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	2.3 GB	Q8_0	Comfortable
1,084 tok/s 650–1,734 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
924 tok/s 554–1,479 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
924 tok/s 554–1,479 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	2.3 GB	Q8_0	Comfortable
924 tok/s 554–1,479 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
924 tok/s 554–1,479 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
924 tok/s 554–1,479 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
704 tok/s 422–1,126 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	2.3 GB	Q8_0	Comfortable
704 tok/s 422–1,126 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	2.3 GB	Q8_0	Comfortable
586 tok/s 352–938 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	2.3 GB	Q8_0	Comfortable
574 tok/s 344–918 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	2.3 GB	Q8_0	Comfortable
561 tok/s 337–898 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	2.3 GB	Q8_0	Comfortable
561 tok/s 337–898 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	2.3 GB	Q8_0	Comfortable
561 tok/s 337–898 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	2.3 GB	Q8_0	Comfortable
561 tok/s 337–898 · 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: 18 September 2024
Authors: Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Kai Dang, An Yang, Rui Men, Fei Huang, Xingzhang Ren, Xuancheng Ren, Jingren Zhou, Junyang Lin

What it does

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

Domain: Language
Task: Code generation, Code autocompletion, Quantitative reasoning, Question answering, 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: 1.5B
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: 5.1 × 10²² 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: Qwen

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: Qwen2.5-Coder Technical Report
Last updated: 11 February 2026

The extremes

The ten fastest GPUs that run Qwen2.5-Coder (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-Coder (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

What it takes to run this model

Minimum card

Tesla C1080

Memory needed

2.3 GB

Fastest

2,200 tok/s

Qwen2.5-Coder (1.5B) reaches a parameter count of 1.5B. 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.

The smallest card that holds it is Tesla C1080, with a memory capacity of 4 GB, running it at a compression of Q8_0 and producing around 23.9 tokens per second.

At the other end sits B200, generating roughly 2,200 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

About this model

Qwen2.5-Coder (1.5B) was published by Alibaba, in the country recorded as China, during September 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 code generation, Code autocompletion, Quantitative reasoning, Question answering, Language modeling/generation.

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached. On Hugging Face it is published under the organisation Qwen.

How fast it runs, and why

Across every card that can run it, the middle of the range sits at 61.8 tokens per second. Clearing the ten tokens per second that roughly matches reading speed: 796 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.

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.

Training and provenance

The training run consumed about 5.1 × 10²² FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.

Step by step

How to choose a GPU for Qwen2.5-Coder (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

Every card here has been checked against Qwen2.5-Coder (1.5B), needing around 2.3 GB at a compression of Q8_0. Capacity is the gate — a card either holds it or it does not.
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, because at long context a card that handles short questions easily can be dropped by Qwen2.5-Coder (1.5B).
03

Decide how much compression you will accept

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 Qwen2.5-Coder (1.5B). It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 2,200 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs, but leaves nothing spare for a longer conversation, in the case of Qwen2.5-Coder (1.5B). 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 Qwen2.5-Coder (1.5B).

Answers

Qwen2.5-Coder (1.5B) — common questions

Qwen2.5-Coder (1.5B)— 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 2.3 GB and generating roughly 369 tokens per second. The fit is comfortable.

Qwen2.5-Coder (1.5B)— 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.

Qwen2.5-Coder (1.5B)— how many parameters does it have?

It has a parameter count of 1.5B. Number of Parameters: 1.54B. 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.

Qwen2.5-Coder (1.5B)— who created it?

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

Qwen2.5-Coder (1.5B)— when was it released?

It 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.

Qwen2.5-Coder (1.5B)— what is it used for?

It works in the domain of Language, and is recorded as handling the task of code generation, Code autocompletion, Quantitative reasoning, Question answering, 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.

Qwen2.5-Coder (1.5B)— where can I download it?

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

Qwen2.5-Coder (1.5B)— how much compute was used to train it?

Training consumed around 5.1 × 10²² 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.

Qwen2.5-Coder (1.5B)— can I run it if it does not fit in my GPU?

Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded model is rarely worth using. Every figure here assumes the whole model is resident on the card.

Qwen2.5-Coder (1.5B)— 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.

Qwen2.5-Coder (1.5B)— 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: 1. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

Qwen2.5-Coder (1.5B)— how accurate are these speed estimates?

These are estimates with real error bars, and any of them could reasonably land anywhere in its published range depending on which runtime you use. The fastest result here: 1,320–3,520 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Qwen2.5-Coder (1.5B)— 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 2.3 GB, and produces roughly 23.9 tokens per second. The number of cards able to run it in total: 818.

Qwen2.5-Coder (1.5B)— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 2,200 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: 796.

Qwen2.5-Coder (1.5B)— how much VRAM does it need?

It needs about 2.3 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.

Qwen2.5-Coder (1.5B)— 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 2.3 GB and generating roughly 410 tokens per second. The fit is comfortable.

Qwen2.5-Coder (1.5B)— 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 2.3 GB and generating roughly 251 tokens per second. The fit is comfortable.

Qwen2.5-Coder (1.5B)— 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 2.3 GB and generating roughly 311 tokens per second. The fit is comfortable.

Source

Original publication

Record last updated 11 February 2026

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