CodeT5+ TPS calculator

Open weights Salesforce 16B 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 of 818 cards that can run it

Smallest card that fits

P102-101

10 GB · Q3_K_M · 19.5 tok/s

Fastest card

B200

212 tok/s · 180 GB

Which GPUs can run CodeT5+?

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
212 tok/s 127–339 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	17.8 GB	Q8_0	Comfortable
212 tok/s 127–339 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	17.8 GB	Q8_0	Comfortable
169 tok/s 101–271 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	17.8 GB	Q8_0	Comfortable
169 tok/s 101–271 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	17.8 GB	Q8_0	Comfortable
135 tok/s 81–216 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	17.8 GB	Q8_0	Comfortable
129 tok/s 78–207 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	17.8 GB	Q8_0	Comfortable
129 tok/s 78–207 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	17.8 GB	Q8_0	Comfortable
124 tok/s 74–198 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	17.8 GB	Q8_0	Comfortable
111 tok/s 67–178 · low confidence	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.5 GB	Q3_K_M	Tight
110 tok/s 66–176 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	17.8 GB	Q8_0	Comfortable
110 tok/s 66–176 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	17.8 GB	Q8_0	Comfortable
110 tok/s 66–176 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	17.8 GB	Q8_0	Comfortable
104 tok/s 63–167 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	17.8 GB	Q8_0	Comfortable
88.9 tok/s 53–142 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	17.8 GB	Q8_0	Comfortable
88.9 tok/s 53–142 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	17.8 GB	Q8_0	Comfortable
88.9 tok/s 53–142 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	17.8 GB	Q8_0	Comfortable
88.9 tok/s 53–142 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	17.8 GB	Q8_0	Comfortable
88.9 tok/s 53–142 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	17.8 GB	Q8_0	Comfortable
67.7 tok/s 41–108 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	17.8 GB	Q8_0	Comfortable
67.7 tok/s 41–108 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	17.8 GB	Q8_0	Comfortable
56.4 tok/s 34–90 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	17.8 GB	Q8_0	Comfortable
55.8 tok/s 33–89 · low confidence	GeForce RTX 3080 12 GB NVIDIA	12 GB	912 GB/s	Jan 2022	10.4 GB	Q4_K_M	Tight
55.8 tok/s 33–89 · low confidence	GeForce RTX 3080 Ti NVIDIA	12 GB	912 GB/s	May 2021	10.4 GB	Q4_K_M	Tight
55.2 tok/s 33–88 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	17.8 GB	Q8_0	Comfortable
54.3 tok/s 33–87 · low confidence	CMP 90HX NVIDIA	10 GB	760 GB/s	Jul 2021	8.5 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: Salesforce
Organisation type: Industry
Country: United States of America
Published: 20 May 2023
Authors: Yue Wang, Hung Le, Akhilesh Deepak Gotmare, Nghi D.Q. Bui, Junnan Li, Steven C.H. Hoi

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
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: 16B
Training data: 51,500,000,000 tokens

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

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: Open source
Hugging Face: Salesforce

How it is classified

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

Why it is tracked: SOTA improvement
Record confidence: Confident
Citations: 699

Sources

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

Reference: CodeT5+: Open Code Large Language Models for Code Understanding and Generation
Last updated: 25 May 2026

The extremes

The ten fastest GPUs for CodeT5+

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 CodeT5+

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

P102-101

Memory needed

8.5 GB

Fastest

212 tok/s

CodeT5+ is small enough at 16B parameters that hardware is rarely the obstacle — 306 of the cards we track can run it, including cards several years old.

The entry point is the P102-101: 10 GB of memory, Q3_K_M compression, roughly 19.5 tokens per second.

The quickest result comes from a B200 at around 212 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Background

CodeT5+ was published by Salesforce, in United States of America, in May 2023. It comes out of industry.

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

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

Reading the throughput figures

The median result is around 19.8 tokens per second; 259 cards produce text faster than most people read it.

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.

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.

What went into building it

The training set ran to roughly 51,500,000,000 tokens.

Its inclusion criterion is sOTA improvement.

Step by step

How to choose a GPU for CodeT5+

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 CodeT5+ — around 8.5 GB at Q3_K_M. 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 CodeT5+ 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 — Q3_K_M on the smallest card that fits. Setting a floor drops the cards that only manage CodeT5+ by squeezing it further than you would want.
04

Rank by throughput rather than spec sheet

Sort by speed to see how cards rank for CodeT5+. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 212 tok/s.
05

Read the fit column last

A tight fit runs CodeT5+ 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 CodeT5+ is settled.

Answers

CodeT5+ — common questions

Can I run CodeT5+ on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 17.8 GB and generating roughly 35.5 tokens per second — a comfortable fit.

Is CodeT5+ open source?

Its weights are published, so CodeT5+ 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 CodeT5+ have?

CodeT5+ has 16B parameters. "We implemented a family of CodeT5+ models, with model sizes ranging from 220M to 16B". 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 CodeT5+?

CodeT5+ was published by Salesforce, based in United States of America, categorised as industry.

When was CodeT5+ released?

CodeT5+ 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.

What is CodeT5+ used for?

CodeT5+ works in Language, and is recorded as handling code generation, Code autocompletion. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download CodeT5+?

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

Can I run CodeT5+ 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 CodeT5+ is rarely worth using — the nearest miss we calculate is short by 3.2 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run CodeT5+ faster?

Two cards buy memory rather than speed. That matters for CodeT5+ only if one card cannot hold it — 306 can, so a second adds little.

Why does the quantisation differ between cards for CodeT5+?

Because capacity varies, so does how hard CodeT5+ has to be squeezed — 5 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these CodeT5+ speed estimates?

These are estimates with real error bars. The fastest result here, 127–339 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 CodeT5+?

The smallest card in our catalogue that holds CodeT5+ is the P102-101, with 10 GB of memory. It runs the model at Q3_K_M using about 8.5 GB, and produces roughly 19.5 tokens per second. 306 cards in total can run it.

How fast is CodeT5+ on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 212 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 259 of the cards that can run CodeT5+ clear that.

How much VRAM does CodeT5+ need?

About 8.5 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 CodeT5+ on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q4_K_M, using about 10.4 GB and generating roughly 55.8 tokens per second — a tight fit.

Can I run CodeT5+ on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q6_K, using about 14.1 GB and generating roughly 43.5 tokens per second — a tight fit.

Source

Original publication

Record last updated 25 May 2026

The other direction

Looking at it from the other side?

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