LLaMA-7B (protein-oriented instruction-tuned) TPS calculator

Open weights Zhejiang University (ZJU) 7B parameters October 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

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 LLaMA-7B (protein-oriented instruction-tuned)?

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: Zhejiang University (ZJU)
Organisation type: Academia
Country: China
Published: 2 October 2023
Authors: Yin Fang, Xiaozhuan Liang, Ningyu Zhang, Kangwei Liu, Rui Huang, Zhuo Chen, Xiaohui Fan, Huajun Chen

What it does

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

Domain: Language, Biology
Task: Protein folding prediction, Protein generation, Other Biological Modeling, Protein or nucleotide language model (pLM/nLM), Protein question answering
Base model: LLaMA-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: 5,068,266,667 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: 2.8 × 10²² FLOP
How it was established: Hardware,Operation counting

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

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Record confidence: Confident
Citations: 145

Sources

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

Reference: Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run LLaMA-7B (protein-oriented instruction-tuned)

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 LLaMA-7B (protein-oriented instruction-tuned)

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

What it takes to run this model

Minimum card

Quadro 6000

Memory needed

5.1 GB

Fastest

484 tok/s

LLaMA-7B (protein-oriented instruction-tuned) 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.

The smallest card that holds it is 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.

What this model is

LLaMA-7B (protein-oriented instruction-tuned) was published by Zhejiang University (ZJU), in the country recorded as China, during October 2023. The publishing organisation is categorised as academia.

It works in the domain of Language, Biology, and is recorded as performing the task of protein folding prediction, Protein generation, Other Biological Modeling, Protein or nucleotide language model (pLM/nLM), Protein question answering.

Rather than being trained from scratch, it is derived from LLaMA-7B. That is the usual way a specialised model is produced.

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.

What decides the speed

Half the cards that hold it manage more than 26.1 tokens per second. Producing text faster than most people read it: 552 of them.

Being dense, it reads all of itself per token, which is why the ordering by speed below follows the ordering by memory bandwidth so closely.

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

How it was trained

Training it took a computation budget of roughly 2.8 × 10²² FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.

The training set ran to roughly 5,068,266,667 tokens of text.

Step by step

How to choose a GPU for LLaMA-7B (protein-oriented instruction-tuned)

The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.

01

Start from the memory column

Start from what it actually needs, which is the requirement of LLaMA-7B (protein-oriented instruction-tuned), needing around 5.1 GB at a compression of IQ4_XS. Capacity is the gate — a card either holds it or it does not.
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 a card that seemed fine stops fitting LLaMA-7B (protein-oriented instruction-tuned).
03

Choose how far you will compress it

Compression is what makes a model fit smaller cards, at some cost in accuracy, 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

Ranking by tokens per second follows memory bandwidth rather than core counts, for LLaMA-7B (protein-oriented instruction-tuned). 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

Read the fit column last

Tight means it loads and works with no room to raise the context later, in the case of LLaMA-7B (protein-oriented instruction-tuned). 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 LLaMA-7B (protein-oriented instruction-tuned).

Answers

LLaMA-7B (protein-oriented instruction-tuned) — common questions

LLaMA-7B (protein-oriented instruction-tuned)— 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.

LLaMA-7B (protein-oriented instruction-tuned)— 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.

LLaMA-7B (protein-oriented instruction-tuned)— 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.

LLaMA-7B (protein-oriented instruction-tuned)— 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.

LLaMA-7B (protein-oriented instruction-tuned)— 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.

LLaMA-7B (protein-oriented instruction-tuned)— 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.

LLaMA-7B (protein-oriented instruction-tuned)— 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.

LLaMA-7B (protein-oriented instruction-tuned)— how many parameters does it have?

It has a parameter count of 7B. In the name. 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.

LLaMA-7B (protein-oriented instruction-tuned)— who created it?

It was published by Zhejiang University (ZJU), based in China, an organisation categorised as academia.

LLaMA-7B (protein-oriented instruction-tuned)— when was it released?

It was published in October 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.

LLaMA-7B (protein-oriented instruction-tuned)— what is it used for?

It works in the domain of Language, Biology, and is recorded as handling the task of protein folding prediction, Protein generation, Other Biological Modeling, Protein or nucleotide language model (pLM/nLM), Protein question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.

LLaMA-7B (protein-oriented instruction-tuned)— 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.

LLaMA-7B (protein-oriented instruction-tuned)— how much compute was used to train it?

Training consumed around 2.8 × 10²² 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.

LLaMA-7B (protein-oriented instruction-tuned)— 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.

LLaMA-7B (protein-oriented instruction-tuned)— would two GPUs run it faster?

Capacity adds across cards; throughput does not. The number of cards already holding it on their own: 582. So a second card is rarely the answer here.

LLaMA-7B (protein-oriented instruction-tuned)— 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: 3. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

LLaMA-7B (protein-oriented instruction-tuned)— 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.

LLaMA-7B (protein-oriented instruction-tuned)— 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.

Source

Original publication

Record last updated 25 May 2026

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