FoxBrain TPS calculator

Open weights Foxconn 70B parameters March 2025

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

61 cards that can run it

818 cards we hold specifications for

Smallest card that fits

A100 PCIe 40 GB

40 GB · Q3_K_M · 25.5 tok/s

Fastest card

B200

48.4 tok/s · 180 GB

Which GPUs can run FoxBrain?

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

61 cards match

Calculating

					Needs	Quantisation	Fit
48.4 tok/s 29–77 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	75.6 GB	Q8_0	Comfortable
48.4 tok/s 29–77 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	75.6 GB	Q8_0	Comfortable
38.7 tok/s 23–62 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	75.6 GB	Q8_0	Comfortable
38.7 tok/s 23–62 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	75.6 GB	Q8_0	Comfortable
30.9 tok/s 19–49 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	75.6 GB	Q8_0	Comfortable
29.6 tok/s 18–47 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	75.6 GB	Q8_0	Comfortable
29.6 tok/s 18–47 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	75.6 GB	Q8_0	Comfortable
29.5 tok/s 18–47 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	59.3 GB	Q6_K	Comfortable
29.5 tok/s 18–47 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	59.3 GB	Q6_K	Comfortable
28.3 tok/s 17–45 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	75.6 GB	Q8_0	Comfortable
26.1 tok/s 16–42 · low confidence	GRID A100B NVIDIA	48 GB	1,870 GB/s	May 2020	43.0 GB	Q4_K_M	Tight
25.5 tok/s 15–41 · low confidence	A100 PCIe 40 GB NVIDIA	40 GB	1,560 GB/s	Jun 2020	34.9 GB	Q3_K_M	Tight
25.5 tok/s 15–41 · low confidence	A100 SXM4 40 GB NVIDIA	40 GB	1,560 GB/s	May 2020	34.9 GB	Q3_K_M	Tight
25.5 tok/s 15–41 · low confidence	A800 PCIe 40 GB NVIDIA	40 GB	1,560 GB/s	Nov 2022	34.9 GB	Q3_K_M	Tight
25.1 tok/s 15–40 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	75.6 GB	Q8_0	Comfortable
25.1 tok/s 15–40 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	75.6 GB	Q8_0	Comfortable
25.1 tok/s 15–40 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	75.6 GB	Q8_0	Comfortable
23.8 tok/s 14–38 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	75.6 GB	Q8_0	Tight
21.8 tok/s 13–35 · low confidence	H100 SXM5 64 GB NVIDIA	64 GB	2,020 GB/s	Mar 2023	51.2 GB	Q5_K_M	Tight
20.3 tok/s 12–33 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	75.6 GB	Q8_0	Tight
20.3 tok/s 12–33 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	75.6 GB	Q8_0	Tight
20.3 tok/s 12–33 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	75.6 GB	Q8_0	Tight
18.7 tok/s 11–30 · low confidence	RTX PRO 5000 Blackwell NVIDIA	48 GB	1,340 GB/s	Mar 2025	43.0 GB	Q4_K_M	Tight
17.9 tok/s 11–29 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	59.3 GB	Q6_K	Comfortable
17.9 tok/s 11–29 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	59.3 GB	Q6_K	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: Foxconn
Organisation type: Industry
Country: Taiwan
Published: 10 March 2025

What it does

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

Domain: Language
Task: Language modeling/generation, Question answering, Mathematical reasoning
Base model: Llama 3.1-70B

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: 70B
Training data: 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.

How it was established: Operation counting,Hardware
Fine-tuning compute: 4.7 × 10²² FLOP

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 H100 PCIe
Chips used: 120
Chip-hours: 64,512
Power draw: 82.4 kW
Data centre: Taipei-1 Supercomputer facility

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
Hugging Face: FoxconnAI

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: FoxBrain: Foxconn’s Industrial-Grade Reasoning LLM for Domain-Specific Applications
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run FoxBrain

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 FoxBrain

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

A100 PCIe 40 GB

Memory needed

34.9 GB

Fastest

48.4 tok/s

FoxBrain sits at 70B parameters, which puts it above consumer hardware and into the range where a card is bought for this purpose rather than repurposed for it. 61 of the cards we track can hold it.

The entry point is the A100 PCIe 40 GB: 40 GB of memory, Q3_K_M compression, roughly 25.5 tokens per second.

A B200 is the fastest we calculate for it: about 48.4 tokens per second, from 8,000 GB/s of memory bandwidth.

About this model

FoxBrain was published by Foxconn, in Taiwan, in March 2025. The organisation is categorised as industry.

It works in Language, and is recorded as doing language modeling/generation, Question answering, Mathematical reasoning.

It is derived from Llama 3.1-70B rather than trained from scratch, which is the usual way a specialised model is produced.

Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all. It is published under the FoxconnAI organisation on Hugging Face.

How fast it runs, and why

Across every card that can run it, the middle of the range is about 17.1 tokens per second, and 50 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.

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 FoxBrain

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 FoxBrain actually needs — around 34.9 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

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for FoxBrain.
03

Set a quality floor

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

Rank by throughput rather than spec sheet

Sort by speed to see how cards rank for FoxBrain. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 48.4 tok/s.
05

Read the fit column last

The fit column separates cards that just manage FoxBrain from those with room to spare. Buy for the second if the context might grow.
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 FoxBrain is settled.

Answers

FoxBrain — common questions

How accurate are these FoxBrain speed estimates?

These are estimates with real error bars. The fastest result here, 29–77 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 FoxBrain?

The smallest card in our catalogue that holds FoxBrain is the A100 PCIe 40 GB, with 40 GB of memory. It runs the model at Q3_K_M using about 34.9 GB, and produces roughly 25.5 tokens per second. 61 cards in total can run it.

How fast is FoxBrain on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 48.4 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 50 of the cards that can run FoxBrain clear that.

How much VRAM does FoxBrain need?

About 34.9 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.

Is FoxBrain open source?

Its weights are published, so FoxBrain 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 FoxBrain have?

FoxBrain has 70B parameters. 70B. 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 FoxBrain?

FoxBrain was published by Foxconn, based in Taiwan, categorised as industry.

When was FoxBrain released?

FoxBrain was published in March 2025.

What is FoxBrain used for?

FoxBrain works in Language, and is recorded as handling language modeling/generation, Question answering, Mathematical reasoning. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download FoxBrain?

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

Can I run FoxBrain 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 — the nearest miss we calculate is short by 14.2 GB. Our figures for FoxBrain assume it is fully resident.

Would two GPUs run FoxBrain faster?

A second card roughly doubles the memory available but not the generation rate. With 61 cards already able to run FoxBrain alone, the case for pairing is weak.

Why does the quantisation differ between cards for FoxBrain?

Each card is shown running the least-compressed copy it can hold, and FoxBrain appears at 5 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

Source

Original publication

Record last updated 28 November 2025

The other direction

Looking at it from the other side?

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