MetaMath 7B (LLaMa 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

509 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 5110P

8 GB · Q4_K_M · 29.1 tok/s

Fastest card

B200

484 tok/s · 180 GB

Which GPUs can run MetaMath 7B (LLaMa 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

509 cards match

Calculating

					Needs	Quantisation	Fit
484 tok/s 411–581	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	9.8 GB	Q8_0	Comfortable
484 tok/s 411–581	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	9.8 GB	Q8_0	Comfortable
387 tok/s 232–618 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	9.8 GB	Q8_0	Comfortable
387 tok/s 232–618 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	9.8 GB	Q8_0	Comfortable
309 tok/s 185–495 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	9.8 GB	Q8_0	Comfortable
296 tok/s 251–355	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	9.8 GB	Q8_0	Comfortable
296 tok/s 251–355	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	9.8 GB	Q8_0	Comfortable
283 tok/s 170–453 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	9.8 GB	Q8_0	Comfortable
251 tok/s 151–402 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	9.8 GB	Q8_0	Comfortable
251 tok/s 151–402 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	9.8 GB	Q8_0	Comfortable
251 tok/s 151–402 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	9.8 GB	Q8_0	Comfortable
238 tok/s 203–286	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	9.8 GB	Q8_0	Comfortable
208 tok/s 177–250	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	6.5 GB	Q4_K_M	Tight
203 tok/s 173–244	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	9.8 GB	Q8_0	Comfortable
203 tok/s 173–244	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	9.8 GB	Q8_0	Comfortable
203 tok/s 173–244	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	9.8 GB	Q8_0	Comfortable
203 tok/s 173–244	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	9.8 GB	Q8_0	Comfortable
203 tok/s 173–244	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	9.8 GB	Q8_0	Comfortable
155 tok/s 93–248 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	9.8 GB	Q8_0	Comfortable
155 tok/s 93–248 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	9.8 GB	Q8_0	Comfortable
137 tok/s 117–165	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.1 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	9.8 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	9.8 GB	Q8_0	Comfortable
123 tok/s 105–148	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	9.8 GB	Q8_0	Comfortable
123 tok/s 105–148	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	9.8 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: Llama 2-7B

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 (restricted use)

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 (LLaMa 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 (LLaMa 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

Xeon Phi 5110P

Memory needed

6.5 GB

Fastest

484 tok/s

MetaMath 7B (LLaMa finetune) is small enough at 7B parameters that hardware is rarely the obstacle — 509 of the cards we track can run it, including cards several years old.

At the low end, a Xeon Phi 5110P handles it — 8 GB, at Q4_K_M, for about 29.1 tokens per second.

Top of the range is the B200, at roughly 484 tokens per second thanks to 8,000 GB/s of bandwidth.

Background

MetaMath 7B (LLaMa 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 United Kingdom of Great Britain and Northern Ireland, in May 2024. academia,Academia,Academia,Industry,Government,Academia is the category the publisher falls under.

It works in Language, and is recorded as doing quantitative reasoning.

Its starting point was Llama 2-7B — most models at this scale are adapted from an existing base rather than built from nothing.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.

Reading the throughput figures

The median result is around 30.4 tokens per second; 482 cards produce text faster than most people read it.

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 attention layout is on file, so the memory figures are computed exactly rather than approximated.

Step by step

How to choose a GPU for MetaMath 7B (LLaMa 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

Look at what MetaMath 7B (LLaMa finetune) actually needs — around 6.5 GB at Q4_K_M. No amount of processing power compensates for a card that cannot hold it.
02

Match the context to your actual use

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason MetaMath 7B (LLaMa finetune) stops fitting a card that seemed fine.
03

Decide how much compression you will accept

The quantisation column varies by card, because a bigger card holds a more accurate copy of MetaMath 7B (LLaMa finetune) — Q4_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Sort by speed

Sort by speed to see how cards rank for MetaMath 7B (LLaMa finetune). It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 484 tok/s.
05

Read the fit column last

Tight means MetaMath 7B (LLaMa finetune) loads and works, with no room to raise the context later. Comfortable means you can. The difference matters more than a few tokens per second.
06

See what else that card runs

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for MetaMath 7B (LLaMa finetune) alone — a card is usually bought for more than one model.

Answers

MetaMath 7B (LLaMa finetune) — common questions

How accurate are these MetaMath 7B (LLaMa finetune) speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 411–581 tok/s on the B200, for instance. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

What GPU do I need to run MetaMath 7B (LLaMa finetune)?

The smallest card in our catalogue that holds MetaMath 7B (LLaMa finetune) is the Xeon Phi 5110P, with 8 GB of memory. It runs the model at Q4_K_M using about 6.5 GB, and produces roughly 29.1 tokens per second. 509 cards in total can run it.

How fast is MetaMath 7B (LLaMa finetune) on a GPU?

It depends on the card. The quickest we calculate is a 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 482 of the cards that can run MetaMath 7B (LLaMa finetune) clear that.

How much VRAM does MetaMath 7B (LLaMa finetune) need?

About 6.5 GB at Q4_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 MetaMath 7B (LLaMa finetune) on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q4_K_M, using about 6.5 GB and generating roughly 208 tokens per second — a tight fit.

Can I run MetaMath 7B (LLaMa finetune) on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 9.8 GB and generating roughly 55.2 tokens per second — a tight fit.

Can I run MetaMath 7B (LLaMa finetune) on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 9.8 GB and generating roughly 68.4 tokens per second — a comfortable fit.

Can I run MetaMath 7B (LLaMa finetune) on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 9.8 GB and generating roughly 81.1 tokens per second — a comfortable fit.

Is MetaMath 7B (LLaMa finetune) open source?

Its weights are published, so MetaMath 7B (LLaMa finetune) 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 MetaMath 7B (LLaMa finetune) have?

MetaMath 7B (LLaMa finetune) has 7B parameters. 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.

Who created MetaMath 7B (LLaMa finetune)?

MetaMath 7B (LLaMa 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, based in United Kingdom of Great Britain and Northern Ireland, categorised as academia,Academia,Academia,Industry,Government,Academia.

When was MetaMath 7B (LLaMa finetune) released?

MetaMath 7B (LLaMa finetune) 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.

What is MetaMath 7B (LLaMa finetune) used for?

MetaMath 7B (LLaMa finetune) works in Language, and is recorded as handling 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.

Where can I download MetaMath 7B (LLaMa finetune)?

The weights for MetaMath 7B (LLaMa finetune) are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

Can I run MetaMath 7B (LLaMa finetune) if it does not fit in my GPU?

It can be split between the card and system memory, but MetaMath 7B (LLaMa finetune) generates painfully slowly that way — the nearest miss we calculate is short by 1.1 GB. Nothing on this page assumes offloading.

Would two GPUs run MetaMath 7B (LLaMa finetune) faster?

Two cards buy memory rather than speed. That matters for MetaMath 7B (LLaMa finetune) only if one card cannot hold it — 509 can, so a second adds little.

Why does the quantisation differ between cards for MetaMath 7B (LLaMa finetune)?

A larger card holds a more accurate copy. Across the cards that run MetaMath 7B (LLaMa finetune), 3 compression levels are used; the floor control above pins it to one.

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