WizardCoder-15.5B TPS calculator

Open weights Microsoft 15.5B parameters June 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 WizardCoder-15.5B?

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

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: Microsoft
Organisation type: Industry
Country: United States of America
Published: 14 June 2023
Authors: Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, Daxin Jiang

What it does

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

Domain: Language
Task: Code generation
Base model: StarCoder

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: 209,715,200 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 × 10²³ FLOP
How it was established: Operation counting
Fine-tuning compute: 2 × 10¹⁹ 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 (restricted use)
Training code: Open source

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: Likely
Citations: 952

Sources

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

Reference: WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run WizardCoder-15.5B

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 WizardCoder-15.5B

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

The hardware side

Minimum card

P102-101

Memory needed

8.3 GB

Fastest

219 tok/s

WizardCoder-15.5B reaches a parameter count of 15.5B. 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: 306.

At the low end it is handled by P102-101, with a memory capacity of 10 GB, running it at a compression of Q3_K_M and producing around 20.1 tokens per second.

Top of the range is B200, generating roughly 219 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

About this model

WizardCoder-15.5B was published by Microsoft, in the country recorded as United States of America, during June 2023. 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.

Rather than being trained from scratch, it is derived from StarCoder. Most models at this scale are adapted from an existing base rather than built from nothing.

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.

How fast it runs, and why

The median result is around 20.4 tokens per second. Clearing the ten tokens per second that roughly matches reading speed: 261 of them.

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

Training it took a computation budget of roughly 1.1 × 10²³ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.

The training set ran to roughly 209,715,200 tokens of text.

Step by step

How to choose a GPU for WizardCoder-15.5B

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

Start from what it actually needs, which is the requirement of WizardCoder-15.5B, needing around 8.3 GB at a compression of Q3_K_M. Capacity is the gate — a card either holds it or it does not.
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 WizardCoder-15.5B.
03

Decide how much compression you will accept

Each card runs the least-compressed copy it can hold, reaching a compression of Q3_K_M 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 WizardCoder-15.5B. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 219 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 WizardCoder-15.5B. 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

Open the card you have settled on

Each card page repeats this sweep for every model we hold, answering what else the hardware is good for beyond WizardCoder-15.5B.

Answers

WizardCoder-15.5B — common questions

WizardCoder-15.5B— how much compute was used to train it?

Training consumed around 1.1 × 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.

WizardCoder-15.5B— can I run it if it does not fit in my GPU?

It can be split between the card and system memory, but it generates painfully slowly that way. The nearest miss we calculate falls short by 2.9 GB. Every figure here assumes the whole model is resident on the card.

WizardCoder-15.5B— would two GPUs run it faster?

A second card roughly doubles the memory available but not the generation rate. The number already able to run it alone: 306. So a second card is rarely the answer here.

WizardCoder-15.5B— why does the quantisation differ between cards?

A larger card holds a more accurate copy. The number of compression levels used across the cards that run it: 5. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

WizardCoder-15.5B— 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: 131–350 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

WizardCoder-15.5B— what GPU do I need to run it?

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

WizardCoder-15.5B— how fast is it on a GPU?

It depends on the card. The quickest we calculate is 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 the number of cards clearing that: 261.

WizardCoder-15.5B— how much VRAM does it need?

It needs about 8.3 GB at a compression of Q3_K_M, 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.

WizardCoder-15.5B— 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 Q4_K_M, using about 10.1 GB and generating roughly 57.6 tokens per second. The fit is tight.

WizardCoder-15.5B— 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 Q6_K, using about 13.7 GB and generating roughly 44.9 tokens per second. The fit is tight.

WizardCoder-15.5B— 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 17.3 GB and generating roughly 36.6 tokens per second. The fit is comfortable.

WizardCoder-15.5B— 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.

WizardCoder-15.5B— how many parameters does it have?

It has a parameter count of 15.5B. 15.5B. 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.

WizardCoder-15.5B— who created it?

It was published by Microsoft, based in United States of America, an organisation categorised as industry.

WizardCoder-15.5B— when was it released?

It was published in June 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.

WizardCoder-15.5B— what is it used for?

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

WizardCoder-15.5B— where can I download it?

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

Source

Original publication

Record last updated 25 May 2026

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