StarCoder TPS calculator

Open weights Hugging Face,ServiceNow,Northeastern University,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),Carnegie Mellon University (CMU),Johns Hopkins University,Leipzig University,ScaDS.AI,Queen Mary University of London,Roblox,Sea AI Lab,Technion - Israel Institute of Technology,Monash University,CSIRO,Data61,McGill University,Saama,University of British Columbia (UBC),Massachusetts Institute of Technology (MIT),Technical University of Munich,IBM,University of Vermont,UnfoldML,SAP,University of Notre Dame,Columbia University,New York University (NYU),University of Allahabad,Discover Dollar,Toloka,Telefonica,Stanford University,Weizmann Institute of Science,Alan Turing Institute,Wellesley College,EleutherAI,Forschungszentrum Julich 15.5B parameters May 2023

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 · Q3_K_M · 20.1 tok/s

Fastest card

B200

219 tok/s · 180 GB

Which GPUs can run StarCoder?

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
219 tok/s 131–350 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	17.3 GB	Q8_0	Comfortable
219 tok/s 131–350 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	17.3 GB	Q8_0	Comfortable
175 tok/s 105–279 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	17.3 GB	Q8_0	Comfortable
175 tok/s 105–279 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	17.3 GB	Q8_0	Comfortable
140 tok/s 84–223 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	17.3 GB	Q8_0	Comfortable
134 tok/s 80–214 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	17.3 GB	Q8_0	Comfortable
134 tok/s 80–214 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	17.3 GB	Q8_0	Comfortable
128 tok/s 77–205 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	17.3 GB	Q8_0	Comfortable
115 tok/s 69–184 · low confidence	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.3 GB	Q3_K_M	Tight
113 tok/s 68–182 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	17.3 GB	Q8_0	Comfortable
113 tok/s 68–182 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	17.3 GB	Q8_0	Comfortable
113 tok/s 68–182 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	17.3 GB	Q8_0	Comfortable
108 tok/s 65–172 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	17.3 GB	Q8_0	Comfortable
91.8 tok/s 55–147 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	17.3 GB	Q8_0	Comfortable
91.8 tok/s 55–147 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	17.3 GB	Q8_0	Comfortable
91.8 tok/s 55–147 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	17.3 GB	Q8_0	Comfortable
91.8 tok/s 55–147 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	17.3 GB	Q8_0	Comfortable
91.8 tok/s 55–147 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	17.3 GB	Q8_0	Comfortable
69.9 tok/s 42–112 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	17.3 GB	Q8_0	Comfortable
69.9 tok/s 42–112 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	17.3 GB	Q8_0	Comfortable
58.3 tok/s 35–93 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	17.3 GB	Q8_0	Comfortable
57.6 tok/s 35–92 · low confidence	GeForce RTX 3080 12 GB NVIDIA	12 GB	912 GB/s	Jan 2022	10.1 GB	Q4_K_M	Tight
57.6 tok/s 35–92 · low confidence	GeForce RTX 3080 Ti NVIDIA	12 GB	912 GB/s	May 2021	10.1 GB	Q4_K_M	Tight
57.0 tok/s 34–91 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	17.3 GB	Q8_0	Comfortable
56.1 tok/s 34–90 · low confidence	CMP 90HX NVIDIA	10 GB	760 GB/s	Jul 2021	8.3 GB	Q3_K_M	Tight

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: Hugging Face,ServiceNow,Northeastern University,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),Carnegie Mellon University (CMU),Johns Hopkins University,Leipzig University,ScaDS.AI,Queen Mary University of London,Roblox,Sea AI Lab,Technion - Israel Institute of Technology,Monash University,CSIRO,Data61,McGill University,Saama,University of British Columbia (UBC),Massachusetts Institute of Technology (MIT),Technical University of Munich,IBM,University of Vermont,UnfoldML,SAP,University of Notre Dame,Columbia University,New York University (NYU),University of Allahabad,Discover Dollar,Toloka,Telefonica,Stanford University,Weizmann Institute of Science,Alan Turing Institute,Wellesley College,EleutherAI,Forschungszentrum Julich
Organisation type: Industry,Industry,Academia,Academia,Academia,Academia,Academia,Academia,Industry,Academia,Academia,Government,Government,Academia,Academia,Academia,Academia,Industry,Academia,Industry,Academia,Academia,Academia,Academia,Industry,Industry,Industry,Academia,Academia,Government,Academia,Research collective,Government
Country: United States of America, Canada, Germany, United Kingdom of Great Britain and Northern Ireland, Singapore, Israel, Australia, Sweden, India, Netherlands, Spain
Published: 9 May 2023
Authors: Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Benjamin Lipki…

What it does

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

Domain: Language
Task: Code generation
Numerical format: BF16

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: 15.5B
Training data: 203,750,000,000 tokens
Epochs: 1
Batch size: 4,000,000

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: 8.5 × 10²² FLOP
How it was established: Reported,Hardware

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 SXM4 80 GB
Chips used: 512
Chip-hours: 320,256
Wall-clock time: 626 hours (26.1 days)
Hardware utilisation: MFU 22.7%
Power draw: 408.0 kW
Compute cost: $212,218

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 (restricted use)
Training code: Unreleased

How it is classified

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

Foundation model: Yes
Why it is tracked: SOTA improvement
Record confidence: Confident
Citations: 1,181

Sources

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

Reference: StarCoder: may the source be with you!
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run StarCoder

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 StarCoder

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

P102-101

Memory needed

8.3 GB

Fastest

219 tok/s

StarCoder is small enough at 15.5B parameters that hardware is rarely the obstacle — 306 of the cards we track can run it, including cards several years old.

At the low end, a P102-101 handles it — 10 GB, at Q3_K_M, for about 20.1 tokens per second.

The quickest result comes from a B200 at around 219 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Background

StarCoder was published by Hugging Face,ServiceNow,Northeastern University,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),Carnegie Mellon University (CMU),Johns Hopkins University,Leipzig University,ScaDS.AI,Queen Mary University of London,Roblox,Sea AI Lab,Technion - Israel Institute of Technology,Monash University,CSIRO,Data61,McGill University,Saama,University of British Columbia (UBC),Massachusetts Institute of Technology (MIT),Technical University of Munich,IBM,University of Vermont,UnfoldML,SAP,University of Notre Dame,Columbia University,New York University (NYU),University of Allahabad,Discover Dollar,Toloka,Telefonica,Stanford University,Weizmann Institute of Science,Alan Turing Institute,Wellesley College,EleutherAI,Forschungszentrum Julich, in United States of America, in May 2023. industry,Industry,Academia,Academia,Academia,Academia,Academia,Academia,Industry,Academia,Academia,Government,Government,Academia,Academia,Academia,Academia,Industry,Academia,Industry,Academia,Academia,Academia,Academia,Industry,Industry,Industry,Academia,Academia,Government,Academia,Research collective,Government is the category the publisher falls under.

It works in Language, and is recorded as doing code generation.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.

Reading the throughput figures

Half the cards that hold it manage more than 20.4 tokens per second, and 261 exceed reading speed outright.

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.

How it was trained

Producing it required around 8.5 × 10²² FLOP of arithmetic, on NVIDIA A100 SXM4 80 GB, which is a statement about the training budget rather than about inference.

Around 203,750,000,000 tokens went into training it.

Its inclusion criterion is sOTA improvement.

Step by step

How to choose a GPU for StarCoder

The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.

01

Read the memory figure first

Look at what StarCoder actually needs — around 8.3 GB at Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
02

Set the context length you will work at

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason StarCoder stops fitting a card that seemed fine.
03

Decide how much compression you will accept

The quantisation column varies by card, because a bigger card holds a more accurate copy of StarCoder — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Sort by speed

Ranking by tokens per second for StarCoder follows memory bandwidth, not core counts, which is why the B200 tops it at 219 tok/s.
05

Check the fit verdict before buying

Tight means StarCoder 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

Check the card from the other side

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond StarCoder.

Answers

StarCoder — common questions

What is StarCoder used for?

StarCoder works in Language, and is recorded as handling code generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download StarCoder?

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

How much compute was used to train StarCoder?

Around 8.5 × 10²² FLOP, on NVIDIA A100 SXM4 80 GB. 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 StarCoder 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 StarCoder is rarely worth using — the nearest miss we calculate is short by 2.9 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run StarCoder faster?

Capacity adds across cards; throughput does not. Since 306 of the cards we track already hold StarCoder on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for StarCoder?

A larger card holds a more accurate copy. Across the cards that run StarCoder, 5 compression levels are used; the floor control above pins it to one.

How accurate are these StarCoder 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 131–350 tok/s on the B200 rather than a single number.

What GPU do I need to run StarCoder?

The smallest card in our catalogue that holds StarCoder is the P102-101, with 10 GB of memory. It runs the model at Q3_K_M using about 8.3 GB, and produces roughly 20.1 tokens per second. 306 cards in total can run it.

How fast is StarCoder on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 219 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 261 of the cards that can run StarCoder clear that.

How much VRAM does StarCoder need?

About 8.3 GB at Q3_K_M 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 StarCoder on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q4_K_M, using about 10.1 GB and generating roughly 57.6 tokens per second — a tight fit.

Can I run StarCoder on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q6_K, using about 13.7 GB and generating roughly 44.9 tokens per second — a tight fit.

Can I run StarCoder on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 17.3 GB and generating roughly 36.6 tokens per second — a comfortable fit.

Is StarCoder open source?

Its weights are published, so StarCoder 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 StarCoder have?

StarCoder has 15.5B parameters. "We trained a 15.5B parameter model". 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 StarCoder?

StarCoder was published by Hugging Face,ServiceNow,Northeastern University,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms),Carnegie Mellon University (CMU),Johns Hopkins University,Leipzig University,ScaDS.AI,Queen Mary University of London,Roblox,Sea AI Lab,Technion - Israel Institute of Technology,Monash University,CSIRO,Data61,McGill University,Saama,University of British Columbia (UBC),Massachusetts Institute of Technology (MIT),Technical University of Munich,IBM,University of Vermont,UnfoldML,SAP,University of Notre Dame,Columbia University,New York University (NYU),University of Allahabad,Discover Dollar,Toloka,Telefonica,Stanford University,Weizmann Institute of Science,Alan Turing Institute,Wellesley College,EleutherAI,Forschungszentrum Julich, based in United States of America, categorised as industry,Industry,Academia,Academia,Academia,Academia,Academia,Academia,Industry,Academia,Academia,Government,Government,Academia,Academia,Academia,Academia,Industry,Academia,Industry,Academia,Academia,Academia,Academia,Industry,Industry,Industry,Academia,Academia,Government,Academia,Research collective,Government.

When was StarCoder released?

StarCoder was published in May 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

Source

Original publication

Record last updated 25 May 2026

The other direction

Looking at it from the other side?

This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.

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