MetaMath 7B (Mistral finetune) TPS calculator

Open weights University of Cambridge,Southern University of Science and Technology (SUSTech),Hong Kong University of Science and Technology (HKUST),Huawei Noah's Ark Lab,Alan Turing Institute,Max Planck Institute for Intelligent Systems 7B parameters May 2024

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

582 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Quadro 6000

6 GB · IQ4_XS · 18.1 tok/s

Fastest card

B200

484 tok/s · 180 GB

Which GPUs can run MetaMath 7B (Mistral finetune)?

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

582 cards match

Calculating

					Needs	Quantisation	Fit
484 tok/s 411–581	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	8.8 GB	Q8_0	Comfortable
484 tok/s 411–581	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	8.8 GB	Q8_0	Comfortable
387 tok/s 232–618 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	8.8 GB	Q8_0	Comfortable
387 tok/s 232–618 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	8.8 GB	Q8_0	Comfortable
309 tok/s 185–495 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	8.8 GB	Q8_0	Comfortable
296 tok/s 251–355	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	8.8 GB	Q8_0	Comfortable
296 tok/s 251–355	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	8.8 GB	Q8_0	Comfortable
283 tok/s 170–453 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	8.8 GB	Q8_0	Comfortable
251 tok/s 151–402 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	8.8 GB	Q8_0	Comfortable
251 tok/s 151–402 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	8.8 GB	Q8_0	Comfortable
251 tok/s 151–402 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	8.8 GB	Q8_0	Comfortable
238 tok/s 203–286	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	8.8 GB	Q8_0	Comfortable
203 tok/s 173–244	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	8.8 GB	Q8_0	Comfortable
203 tok/s 173–244	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	8.8 GB	Q8_0	Comfortable
203 tok/s 173–244	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	8.8 GB	Q8_0	Comfortable
203 tok/s 173–244	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	8.8 GB	Q8_0	Comfortable
203 tok/s 173–244	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	8.8 GB	Q8_0	Comfortable
155 tok/s 93–248 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	8.8 GB	Q8_0	Comfortable
155 tok/s 93–248 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	8.8 GB	Q8_0	Comfortable
131 tok/s 111–157	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	7.1 GB	Q6_K	Tight
129 tok/s 77–206 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	8.8 GB	Q8_0	Comfortable
126 tok/s 76–202 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	8.8 GB	Q8_0	Comfortable
123 tok/s 105–148	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	8.8 GB	Q8_0	Comfortable
123 tok/s 105–148	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	8.8 GB	Q8_0	Comfortable
123 tok/s 105–148	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	8.8 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: University of Cambridge,Southern University of Science and Technology (SUSTech),Hong Kong University of Science and Technology (HKUST),Huawei Noah's Ark Lab,Alan Turing Institute,Max Planck Institute for Intelligent Systems
Organisation type: Academia,Academia,Academia,Industry,Government,Academia
Country: United Kingdom of Great Britain and Northern Ireland, China, Hong Kong, Germany
Published: 3 May 2024
Authors: Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, Weiyang Liu

What it does

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

Domain: Language
Task: Quantitative reasoning
Base model: Mistral 7B

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: 7B
Training data: tokens
Epochs: 3

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
Fine-tuning compute: 1.3 × 10¹⁹ 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 A100
Chips used: 8
Power draw: 6.3 kW

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)
Hugging Face: meta-math

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: MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Last updated: 11 February 2026

The extremes

The ten fastest GPUs that run MetaMath 7B (Mistral finetune)

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 MetaMath 7B (Mistral finetune)

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

Quadro 6000

Memory needed

5.1 GB

Fastest

484 tok/s

MetaMath 7B (Mistral finetune) reaches a parameter count of 7B. 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: 582.

At the low end it is handled by Quadro 6000, with a memory capacity of 6 GB, running it at a compression of IQ4_XS and producing around 18.1 tokens per second.

The fastest we calculate for it is B200, generating roughly 484 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

About this model

MetaMath 7B (Mistral finetune) was published by University of Cambridge,Southern University of Science and Technology (SUSTech),Hong Kong University of Science and Technology (HKUST),Huawei Noah's Ark Lab,Alan Turing Institute,Max Planck Institute for Intelligent Systems, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during May 2024. The publishing organisation is categorised as academia,Academia,Academia,Industry,Government,Academia.

It works in the domain of Language, and is recorded as performing the task of quantitative reasoning.

Rather than being trained from scratch, it is derived from Mistral 7B. That is why it shares the base model's general shape and size.

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

How fast it runs, and why

The median result is around 26.1 tokens per second. Exceeding reading speed outright: 552 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.

Because the architecture is recorded, the memory column is derived rather than estimated.

Step by step

How to choose a GPU for MetaMath 7B (Mistral finetune)

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

Start from what it actually needs, which is the requirement of MetaMath 7B (Mistral finetune), needing around 5.1 GB at a compression of IQ4_XS. That figure, not the headline performance of a card, is what decides whether it runs.
02

Set the context length you will work at

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect, because at long context a card that handles short questions easily can be dropped by MetaMath 7B (Mistral finetune).
03

Set a quality floor

The quantisation column varies by card, because a bigger card holds a more accurate copy, reaching a compression of IQ4_XS 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 MetaMath 7B (Mistral finetune). It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 484 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 MetaMath 7B (Mistral finetune). 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

See what else that card runs

Following a card through to its own page shows every other model it can hold, which is the question that follows once you have settled on MetaMath 7B (Mistral finetune).

Answers

MetaMath 7B (Mistral finetune) — common questions

MetaMath 7B (Mistral finetune)— when was it released?

It was published in May 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

MetaMath 7B (Mistral finetune)— what is it used for?

It works in the domain of Language, and is recorded as handling the task of quantitative reasoning. 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.

MetaMath 7B (Mistral finetune)— where can I download it?

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

MetaMath 7B (Mistral finetune)— can I run it if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part held in system memory drags the whole thing down. The nearest miss we calculate falls short by 1.0 GB. Every figure here assumes the whole model is resident on the card.

MetaMath 7B (Mistral finetune)— 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: 582. So a second card is rarely the answer here.

MetaMath 7B (Mistral finetune)— 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: 3. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

MetaMath 7B (Mistral finetune)— 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: 411–581 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

MetaMath 7B (Mistral finetune)— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Quadro 6000, with a memory capacity of 6 GB. It runs the model at a compression of IQ4_XS using about 5.1 GB, and produces roughly 18.1 tokens per second. The number of cards able to run it in total: 582.

MetaMath 7B (Mistral finetune)— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 484 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: 552.

MetaMath 7B (Mistral finetune)— how much VRAM does it need?

It needs about 5.1 GB at a compression of IQ4_XS, 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.

MetaMath 7B (Mistral finetune)— can I run it on a GPU holding 8 GB?

Yes. The card CMP 170HX 8 GB, holding 8 GB, runs it at a compression of Q6_K, using about 7.1 GB and generating roughly 131 tokens per second. The fit is tight.

MetaMath 7B (Mistral finetune)— 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 Q8_0, using about 8.8 GB and generating roughly 55.2 tokens per second. The fit is comfortable.

MetaMath 7B (Mistral finetune)— 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 Q8_0, using about 8.8 GB and generating roughly 68.4 tokens per second. The fit is comfortable.

MetaMath 7B (Mistral finetune)— 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 8.8 GB and generating roughly 81.1 tokens per second. The fit is comfortable.

MetaMath 7B (Mistral finetune)— 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.

MetaMath 7B (Mistral finetune)— how many parameters does it have?

It has a parameter count of 7B. 7B. 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.

MetaMath 7B (Mistral finetune)— who created it?

It was published by University of Cambridge,Southern University of Science and Technology (SUSTech),Hong Kong University of Science and Technology (HKUST),Huawei Noah's Ark Lab,Alan Turing Institute,Max Planck Institute for Intelligent Systems, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as academia,Academia,Academia,Industry,Government,Academia.

Source

Original publication

Record last updated 11 February 2026

The other direction

Looking at it from the other side?

This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.

All AI models