North Mini Code TPS calculator

Open weights Cohere 30B parameters June 2026

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

241 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 7120P

16 GB · Q3_K_M · 48.4 tok/s

Fastest card

B200

627 tok/s · 180 GB

Which GPUs can run North Mini Code?

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

241 cards match

Calculating

					Needs	Quantisation	Fit
627 tok/s 376–1,004 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	31.2 GB	Q8_0	Comfortable
627 tok/s 376–1,004 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	31.2 GB	Q8_0	Comfortable
501 tok/s 301–802 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	31.2 GB	Q8_0	Comfortable
501 tok/s 301–802 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	31.2 GB	Q8_0	Comfortable
401 tok/s 240–641 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	31.2 GB	Q8_0	Comfortable
384 tok/s 230–614 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	31.2 GB	Q8_0	Comfortable
384 tok/s 230–614 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	31.2 GB	Q8_0	Comfortable
367 tok/s 220–587 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	31.2 GB	Q8_0	Comfortable
326 tok/s 195–521 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	31.2 GB	Q8_0	Comfortable
326 tok/s 195–521 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	31.2 GB	Q8_0	Comfortable
326 tok/s 195–521 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	31.2 GB	Q8_0	Comfortable
309 tok/s 185–494 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	31.2 GB	Q8_0	Comfortable
264 tok/s 158–422 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	31.2 GB	Q8_0	Comfortable
264 tok/s 158–422 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	31.2 GB	Q8_0	Comfortable
264 tok/s 158–422 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	31.2 GB	Q8_0	Comfortable
264 tok/s 158–422 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	31.2 GB	Q8_0	Comfortable
264 tok/s 158–422 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	31.2 GB	Q8_0	Comfortable
239 tok/s 143–383 · low confidence	Tesla V100 SXM2 16 GB NVIDIA	16 GB	1,130 GB/s	Nov 2019	13.7 GB	Q3_K_M	Tight
213 tok/s 128–341 · low confidence	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	24.2 GB	Q6_K	Tight
213 tok/s 128–341 · low confidence	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	24.2 GB	Q6_K	Tight
204 tok/s 122–326 · low confidence	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	24.2 GB	Q6_K	Tight
204 tok/s 122–326 · low confidence	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	24.2 GB	Q6_K	Tight
203 tok/s 122–325 · low confidence	GeForce RTX 5080 NVIDIA	16 GB	960 GB/s	Jan 2025	13.7 GB	Q3_K_M	Tight
201 tok/s 120–321 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	31.2 GB	Q8_0	Comfortable
201 tok/s 120–321 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	31.2 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: Cohere
Organisation type: Industry
Country: Canada
Published: 9 June 2026
Authors: Jay Alammar, Sophia Althammer, Dennis Aumiller, Leon Engländer, Yannis Flet-Berliac, Eden Gilbert, Sarra Habchi, Kylie He, Dhruti Joshi, Jozef Mokrý, David Mora, Josh Netto-Rosen, Deniz Qian, Lawrence Rodgers, Willem Röpke, Tom Sherborne, Ahmet Üstün, Minjie Xu, Diana Abagyan, Sammie Bae, Björn Bebensee, Walter Beller-Morales, Sepideh Shaterian Bidgoli, Bas Büller, David Cairuz, Kris Cao, Roman Ca…

What it does

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

Domain: Language
Task: Coding, Language modeling/generation, Question answering

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: 30B
Training data: tokens

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: CohereLabs

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: Introducing North Mini Code: Cohere’s first model for developers
Last updated: 10 June 2026

The extremes

The ten fastest GPUs that run North Mini Code

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 North Mini Code

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

Xeon Phi 7120P

Memory needed

13.7 GB

Fastest

627 tok/s

North Mini Code reaches a parameter count of 30B. That lands in the range a serious desktop card can handle once the weights are compressed. The number of cards we track that can run it: 241.

The entry point is Xeon Phi 7120P, with a memory capacity of 16 GB, running it at a compression of Q3_K_M and producing around 48.4 tokens per second.

At the other end sits B200, generating roughly 627 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

Where it came from

North Mini Code was published by Cohere, in the country recorded as Canada, during June 2026. The publishing organisation is categorised as industry.

It works in the domain of Language, and is recorded as performing the task of coding, Language modeling/generation, Question answering.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. On Hugging Face it is published under the organisation CohereLabs.

Understanding the speeds

Half the cards that hold it manage more than 95.1 tokens per second. Clearing the ten tokens per second that roughly matches reading speed: 239 of them.

Because it routes each token through a subset of its weights, it produces text at the pace of a much smaller model. The catch is memory: all of it still has to fit, so the speed is a bonus rather than a discount on hardware.

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.

Step by step

How to choose a GPU for North Mini Code

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

Every card here has been checked against North Mini Code, needing around 13.7 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

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

Rank by throughput rather than spec sheet

The speed ordering is effectively an ordering by memory bandwidth, for North Mini Code. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 627 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage it from those with room to spare, in the case of North Mini Code. 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

Every card name links to its own page, which runs the same calculation across the whole model catalogue. A card is usually bought for more than one model, so it is worth a look before buying for North Mini Code.

Answers

North Mini Code — common questions

North Mini Code— who created it?

It was published by Cohere, based in Canada, an organisation categorised as industry.

North Mini Code— when was it released?

It was published in June 2026.

North Mini Code— what is it used for?

It works in the domain of Language, and is recorded as handling the task of coding, Language modeling/generation, Question answering. 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.

North Mini Code— where can I download it?

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

North Mini Code— 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. The nearest miss we calculate falls short by 6.4 GB. Every figure here assumes the whole model is resident on the card.

North Mini Code— 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: 241. So a second card is rarely the answer here.

North Mini Code— 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: 5. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

North Mini Code— how accurate are these speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked, which is why each is published as a range rather than a single number. One example: 376–1,004 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

North Mini Code— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Xeon Phi 7120P, with a memory capacity of 16 GB. It runs the model at a compression of Q3_K_M using about 13.7 GB, and produces roughly 48.4 tokens per second. The number of cards able to run it in total: 241.

North Mini Code— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 627 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: 239.

North Mini Code— how much VRAM does it need?

It needs about 13.7 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.

North Mini Code— 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 Q3_K_M, using about 13.7 GB and generating roughly 239 tokens per second. The fit is tight.

North Mini Code— 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 Q5_K_M, using about 20.7 GB and generating roughly 188 tokens per second. The fit is tight.

North Mini Code— 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.

North Mini Code— how many parameters does it have?

It has a parameter count of 30B. 30B total, 3B active. 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.

Source

Original publication

Record last updated 10 June 2026

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