CRYSTALCODER TPS calculator

Open weights Mohamed bin Zayed University of Artificial Intelligence (MBZUAI),Petuum,University of Southern California,Carnegie Mellon University (CMU),University of Illinois Urbana-Champaign (UIUC),University of California San Diego,LLM360 6.7B parameters December 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

589 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla K20c

5 GB · IQ4_XS · 27.4 tok/s

Fastest card

B200

506 tok/s · 180 GB

Which GPUs can run CRYSTALCODER?

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

589 cards match

Calculating

					Needs	Quantisation	Fit
506 tok/s 303–809 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	7.9 GB	Q8_0	Comfortable
506 tok/s 303–809 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	7.9 GB	Q8_0	Comfortable
404 tok/s 242–646 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	7.9 GB	Q8_0	Comfortable
404 tok/s 242–646 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	7.9 GB	Q8_0	Comfortable
323 tok/s 194–517 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	7.9 GB	Q8_0	Comfortable
309 tok/s 185–495 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	7.9 GB	Q8_0	Comfortable
309 tok/s 185–495 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	7.9 GB	Q8_0	Comfortable
296 tok/s 178–473 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	7.9 GB	Q8_0	Comfortable
263 tok/s 158–420 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	7.9 GB	Q8_0	Comfortable
263 tok/s 158–420 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	7.9 GB	Q8_0	Comfortable
263 tok/s 158–420 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	7.9 GB	Q8_0	Comfortable
249 tok/s 149–399 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	7.9 GB	Q8_0	Comfortable
212 tok/s 127–340 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	7.9 GB	Q8_0	Comfortable
212 tok/s 127–340 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	7.9 GB	Q8_0	Comfortable
212 tok/s 127–340 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	7.9 GB	Q8_0	Comfortable
212 tok/s 127–340 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	7.9 GB	Q8_0	Comfortable
212 tok/s 127–340 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	7.9 GB	Q8_0	Comfortable
162 tok/s 97–259 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	7.9 GB	Q8_0	Comfortable
162 tok/s 97–259 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	7.9 GB	Q8_0	Comfortable
137 tok/s 82–219 · low confidence	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	6.3 GB	Q6_K	Tight
135 tok/s 81–216 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	7.9 GB	Q8_0	Comfortable
132 tok/s 79–211 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	7.9 GB	Q8_0	Comfortable
129 tok/s 77–206 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	7.9 GB	Q8_0	Comfortable
129 tok/s 77–206 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	7.9 GB	Q8_0	Comfortable
129 tok/s 77–206 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	7.9 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: Mohamed bin Zayed University of Artificial Intelligence (MBZUAI),Petuum,University of Southern California,Carnegie Mellon University (CMU),University of Illinois Urbana-Champaign (UIUC),University of California San Diego,LLM360
Organisation type: Academia,Industry,Academia,Academia,Academia,Academia,Research collective
Country: United Arab Emirates, United States of America
Published: 11 December 2023
Authors: Zhengzhong Liu, Aurick Qiao, Willie Neiswanger, Hongyi Wang, Bowen Tan, Tianhua Tao, Junbo Li, Yuqi Wang, Suqi Sun, Omkar Pangarkar, Richard Fan, Yi Gu, Victor Miller, Yonghao Zhuang, Guowei He, Haonan Li, Fajri Koto, Liping Tang, Nikhil Ranjan, Zhiqiang Shen, Xuguang Ren, Roberto Iriondo, Cun Mu, Zhiting Hu, Mark Schulze, Preslav Nakov, Tim Baldwin, Eric P. Xing

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, Code generation

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: 6.7B
Training data: 1,382,000,000,000 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: 5.6 × 10²² FLOP
How it was established: Operation counting

The training run

What it physically took to train: which chips, how many, for how long, and what that drew from the wall.

Cloud vendor: Cerebras
Data centre: CRYSTALCODER is trained on the Cerebras Condor Galaxy 1 (CG-1), a 4 exaFLOPS, 54 million core, 64-node cloud AI supercomputer

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: Open source
Hugging Face: LLM360

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: LLM360: Towards Fully Transparent Open-Source LLMs
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run CRYSTALCODER

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 CRYSTALCODER

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

Tesla K20c

Memory needed

4.4 GB

Fastest

506 tok/s

CRYSTALCODER is small enough at 6.7B parameters that hardware is rarely the obstacle — 589 of the cards we track can run it, including cards several years old.

The smallest card that holds it is the Tesla K20c with 5 GB, running it at IQ4_XS and producing around 27.4 tokens per second.

A B200 is the fastest we calculate for it: about 506 tokens per second, from 8,000 GB/s of memory bandwidth.

What this model is

CRYSTALCODER was published by Mohamed bin Zayed University of Artificial Intelligence (MBZUAI),Petuum,University of Southern California,Carnegie Mellon University (CMU),University of Illinois Urbana-Champaign (UIUC),University of California San Diego,LLM360, in United Arab Emirates, in December 2023. It comes out of academia,Industry,Academia,Academia,Academia,Academia,Research collective.

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

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. It is published under the LLM360 organisation on Hugging Face.

What decides the speed

Half the cards that hold it manage more than 27.3 tokens per second, and 562 exceed reading speed outright.

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.

Memory here is estimated from size rather than computed from the architecture, which is not recorded for this model — the numbers are indicative rather than exact.

Training and provenance

Producing it required around 5.6 × 10²² FLOP of arithmetic, which is a statement about the training budget rather than about inference.

Around 1,382,000,000,000 tokens went into training it.

Step by step

How to choose a GPU for CRYSTALCODER

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

Look at what CRYSTALCODER actually needs — around 4.4 GB at IQ4_XS. 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 CRYSTALCODER stops fitting a card that seemed fine.
03

Choose how far you will compress it

Each card runs the least-compressed copy it can hold — IQ4_XS on the smallest card that fits. Setting a floor drops the cards that only manage CRYSTALCODER by squeezing it further than you would want.
04

Rank by throughput rather than spec sheet

The speed ordering for CRYSTALCODER is effectively an ordering by memory bandwidth, which is why the B200 tops it at 506 tok/s.
05

Read the fit column last

A tight fit runs CRYSTALCODER but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

Check the card from the other side

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond CRYSTALCODER.

Answers

CRYSTALCODER — common questions

Can I run CRYSTALCODER on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 7.9 GB and generating roughly 57.7 tokens per second — a comfortable fit.

Can I run CRYSTALCODER on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 7.9 GB and generating roughly 71.4 tokens per second — a comfortable fit.

Can I run CRYSTALCODER on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 7.9 GB and generating roughly 84.7 tokens per second — a comfortable fit.

Is CRYSTALCODER open source?

Its weights are published, so CRYSTALCODER 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 CRYSTALCODER have?

CRYSTALCODER has 6.7B parameters. 6.7B We used the exact same model architecture as LLaMA 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 CRYSTALCODER?

CRYSTALCODER was published by Mohamed bin Zayed University of Artificial Intelligence (MBZUAI),Petuum,University of Southern California,Carnegie Mellon University (CMU),University of Illinois Urbana-Champaign (UIUC),University of California San Diego,LLM360, based in United Arab Emirates, categorised as academia,Industry,Academia,Academia,Academia,Academia,Research collective.

When was CRYSTALCODER released?

CRYSTALCODER was published in December 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.

What is CRYSTALCODER used for?

CRYSTALCODER works in Language, and is recorded as handling language modeling/generation, Question answering, Code generation. 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 CRYSTALCODER?

Its weights are published under the LLM360 organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.

How much compute was used to train CRYSTALCODER?

Around 5.6 × 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.

Can I run CRYSTALCODER 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 1.2 GB. Our figures for CRYSTALCODER assume it is fully resident.

Would two GPUs run CRYSTALCODER faster?

Two cards buy memory rather than speed. That matters for CRYSTALCODER only if one card cannot hold it — 589 can, so a second adds little.

Why does the quantisation differ between cards for CRYSTALCODER?

Each card is shown running the least-compressed copy it can hold, and CRYSTALCODER appears at 4 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these CRYSTALCODER speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked. That is why each is published as a range such as 303–809 tok/s on the B200 rather than a single number.

What GPU do I need to run CRYSTALCODER?

The smallest card in our catalogue that holds CRYSTALCODER is the Tesla K20c, with 5 GB of memory. It runs the model at IQ4_XS using about 4.4 GB, and produces roughly 27.4 tokens per second. 589 cards in total can run it.

How fast is CRYSTALCODER on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 506 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 562 of the cards that can run CRYSTALCODER clear that.

How much VRAM does CRYSTALCODER need?

About 4.4 GB at IQ4_XS 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 CRYSTALCODER on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q6_K, using about 6.3 GB and generating roughly 137 tokens per second — a tight fit.

Source

Original publication

Record last updated 28 November 2025

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