PolyCoder TPS calculator

Open weights Carnegie Mellon University (CMU) 2.7B parameters February 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 · 13.7 tok/s

Fastest card

B200

1,255 tok/s · 180 GB

Which GPUs can run PolyCoder?

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
1,255 tok/s 753–2,008 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	3.6 GB	Q8_0	Comfortable
1,255 tok/s 753–2,008 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	3.6 GB	Q8_0	Comfortable
1,002 tok/s 601–1,603 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	3.6 GB	Q8_0	Comfortable
1,002 tok/s 601–1,603 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	3.6 GB	Q8_0	Comfortable
801 tok/s 481–1,282 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	3.6 GB	Q8_0	Comfortable
767 tok/s 460–1,227 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.6 GB	Q8_0	Comfortable
767 tok/s 460–1,227 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.6 GB	Q8_0	Comfortable
734 tok/s 440–1,175 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	3.6 GB	Q8_0	Comfortable
652 tok/s 391–1,042 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	3.6 GB	Q8_0	Comfortable
652 tok/s 391–1,042 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	3.6 GB	Q8_0	Comfortable
652 tok/s 391–1,042 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	3.6 GB	Q8_0	Comfortable
618 tok/s 371–989 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	3.6 GB	Q8_0	Comfortable
527 tok/s 316–843 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.6 GB	Q8_0	Comfortable
527 tok/s 316–843 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	3.6 GB	Q8_0	Comfortable
527 tok/s 316–843 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	3.6 GB	Q8_0	Comfortable
527 tok/s 316–843 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.6 GB	Q8_0	Comfortable
527 tok/s 316–843 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	3.6 GB	Q8_0	Comfortable
401 tok/s 241–642 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	3.6 GB	Q8_0	Comfortable
401 tok/s 241–642 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	3.6 GB	Q8_0	Comfortable
334 tok/s 201–535 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	3.6 GB	Q8_0	Comfortable
327 tok/s 196–524 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	3.6 GB	Q8_0	Comfortable
320 tok/s 192–512 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	3.6 GB	Q8_0	Comfortable
320 tok/s 192–512 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	3.6 GB	Q8_0	Comfortable
320 tok/s 192–512 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	3.6 GB	Q8_0	Comfortable
320 tok/s 192–512 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	3.6 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: Carnegie Mellon University (CMU)
Organisation type: Academia
Country: United States of America
Published: 26 February 2022
Authors: Frank F. Xu, Uri Alon, Graham Neubig, Vincent J. Hellendoorn

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: FP16

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: 2.7B
Training data: 39,300,000,000 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: 1.1 × 10²¹ FLOP
How it was established: 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 Quadro RTX 8000
Wall-clock time: 1,000 hours (41.7 days)

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: NinedayWang

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: Likely
Citations: 860

Sources

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

Reference: A Systematic Evaluation of Large Language Models of Code
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run PolyCoder

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 PolyCoder

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

3.6 GB

Fastest

1,255 tok/s

PolyCoder is small enough at 2.7B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

At the low end, a Tesla C1080 handles it — 4 GB, at Q8_0, for about 13.7 tokens per second.

A B200 is the fastest we calculate for it: about 1,255 tokens per second, from 8,000 GB/s of memory bandwidth.

About this model

PolyCoder was published by Carnegie Mellon University (CMU), in United States of America, in February 2022. It comes out of academia.

It works in Language, and is recorded as doing 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. It is published under the NinedayWang organisation on Hugging Face.

How fast it runs, and why

Across every card that can run it, the middle of the range is about 35.2 tokens per second, and 775 of them clear the ten tokens per second that roughly matches reading speed.

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 1.1 × 10²¹ FLOP, on NVIDIA Quadro RTX 8000. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

It was trained on about 39,300,000,000 tokens of text.

Its inclusion criterion is sOTA improvement.

Step by step

How to choose a GPU for PolyCoder

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

01

Check what it needs before anything else

Every card here has been checked against PolyCoder — around 3.6 GB at Q8_0. Capacity is the gate — a card either holds it or it does not.
02

Decide how long your conversations run

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context PolyCoder can slip off a card that handles short questions easily.
03

Set a quality floor

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 PolyCoder by squeezing it further than you would want.
04

Rank by throughput rather than spec sheet

Ranking by tokens per second for PolyCoder follows memory bandwidth, not core counts, which is why the B200 tops it at 1,255 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs PolyCoder but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

See what else that card runs

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for PolyCoder alone — a card is usually bought for more than one model.

Answers

PolyCoder — common questions

How fast is PolyCoder on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 1,255 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 775 of the cards that can run PolyCoder clear that.

How much VRAM does PolyCoder need?

About 3.6 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.

Can I run PolyCoder on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 3.6 GB and generating roughly 234 tokens per second — a comfortable fit.

Can I run PolyCoder on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 3.6 GB and generating roughly 143 tokens per second — a comfortable fit.

Can I run PolyCoder on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 3.6 GB and generating roughly 177 tokens per second — a comfortable fit.

Can I run PolyCoder on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 3.6 GB and generating roughly 210 tokens per second — a comfortable fit.

Is PolyCoder open source?

Its weights are published, so PolyCoder 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 PolyCoder have?

PolyCoder has 2.7B parameters. 2.7B for largest 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 PolyCoder?

PolyCoder was published by Carnegie Mellon University (CMU), based in United States of America, categorised as academia.

When was PolyCoder released?

PolyCoder was published in February 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.

What is PolyCoder used for?

PolyCoder works in Language, and is recorded as handling code generation. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download PolyCoder?

Its weights are published under the NinedayWang 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 PolyCoder?

Around 1.1 × 10²¹ FLOP, on NVIDIA Quadro RTX 8000. 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 PolyCoder 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 PolyCoder assume it is fully resident.

Would two GPUs run PolyCoder faster?

A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run PolyCoder alone, the case for pairing is weak.

Why does the quantisation differ between cards for PolyCoder?

Because capacity varies, so does how hard PolyCoder has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these PolyCoder speed estimates?

These are estimates with real error bars. The fastest result here, 753–2,008 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

What GPU do I need to run PolyCoder?

The smallest card in our catalogue that holds PolyCoder is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 3.6 GB, and produces roughly 13.7 tokens per second. 818 cards in total can run it.

Source

Original publication

Record last updated 25 May 2026

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