DBRX TPS calculator

Open weights Databricks 132B parameters March 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

38 cards that can run it

818 cards we hold specifications for

Smallest card that fits

RTX PRO 5000 72 GB Blackwell

72 GB · Q3_K_M · 42.5 tok/s

Fastest card

H100 NVL 94 GB

107 tok/s · 94 GB

Which GPUs can run DBRX?

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

38 cards match

Calculating

					Needs	Quantisation	Fit
107 tok/s 64–171 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	79.5 GB	Q4_K_M	Tight
97.1 tok/s 58–155 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	71.8 GB	IQ4_XS	Tight
97.1 tok/s 58–155 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	71.8 GB	IQ4_XS	Tight
94.1 tok/s 56–151 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	141.0 GB	Q8_0	Tight
94.1 tok/s 56–151 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	141.0 GB	Q8_0	Comfortable
91.3 tok/s 55–146 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	79.5 GB	Q4_K_M	Tight
91.3 tok/s 55–146 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	79.5 GB	Q4_K_M	Tight
91.3 tok/s 55–146 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	79.5 GB	Q4_K_M	Tight
87.3 tok/s 52–140 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	110.3 GB	Q6_K	Tight
83.6 tok/s 50–134 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	110.3 GB	Q6_K	Tight
83.6 tok/s 50–134 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	110.3 GB	Q6_K	Tight
75.2 tok/s 45–120 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	141.0 GB	Q8_0	Comfortable
75.2 tok/s 45–120 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	141.0 GB	Q8_0	Comfortable
71.0 tok/s 43–114 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	110.3 GB	Q6_K	Tight
58.9 tok/s 35–94 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	71.8 GB	IQ4_XS	Tight
58.9 tok/s 35–94 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	71.8 GB	IQ4_XS	Tight
58.9 tok/s 35–94 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	71.8 GB	IQ4_XS	Tight
58.9 tok/s 35–94 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	71.8 GB	IQ4_XS	Tight
58.9 tok/s 35–94 · low confidence	H100 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Oct 2022	71.8 GB	IQ4_XS	Tight
58.9 tok/s 35–94 · low confidence	H800 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Mar 2023	71.8 GB	IQ4_XS	Tight
56.0 tok/s 34–90 · low confidence	A100 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Jun 2021	71.8 GB	IQ4_XS	Tight
56.0 tok/s 34–90 · low confidence	A800 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Nov 2022	71.8 GB	IQ4_XS	Tight
55.1 tok/s 33–88 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	141.0 GB	Q8_0	Comfortable
48.9 tok/s 29–78 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	141.0 GB	Q8_0	Comfortable
48.9 tok/s 29–78 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	141.0 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: Databricks
Organisation type: Industry
Country: United States of America
Published: 27 March 2024
Authors: Mosaic Research Team

What it does

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

Domain: Language
Task: Chat, Code generation
Numerical format: BF16

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: 132B
Training data: 12,000,000,000,000 tokens
Epochs: 1

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: 2.6 × 10²⁴ 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.

Training hardware: NVIDIA H100 SXM5 80GB

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: databricks

How it is classified

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

Likely above 10²³ FLOP: Yes
Why it is tracked: Training cost
Record confidence: Confident

Sources

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

Reference: Introducing DBRX: A New State-of-the-Art Open LLM
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run DBRX

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 DBRX

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

RTX PRO 5000 72 GB Blackwell

Memory needed

64.2 GB

Fastest

107 tok/s

DBRX reaches a parameter count of 132B. That puts it above consumer hardware, into the range where a card is bought for this purpose rather than repurposed for it. The number of cards we track that can hold it: 38.

The smallest card that holds it is RTX PRO 5000 72 GB Blackwell, with a memory capacity of 72 GB, running it at a compression of Q3_K_M and producing around 42.5 tokens per second.

At the other end sits H100 NVL 94 GB, generating roughly 107 tokens per second on the strength of a memory bandwidth of 3,940 GB/s.

What this model is

DBRX was published by Databricks, in the country recorded as United States of America, during March 2024. The category the publisher falls under is industry.

It works in the domain of Language, and is recorded as performing the task of chat, Code generation.

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 databricks.

What decides the speed

Across every card that can run it, the middle of the range sits at 58.9 tokens per second. Clearing the ten tokens per second that roughly matches reading speed: 36 of them.

Mixture-of-experts routing is why the speeds here look high for the parameter count. Only a fraction is read per token, but the whole thing has to be loaded.

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.

How it was trained

The training run consumed about 2.6 × 10²⁴ FLOP, on hardware recorded as NVIDIA H100 SXM5 80GB. That figure measures what producing the model cost, and has no bearing on how fast it answers.

It was trained on a corpus of about 12,000,000,000,000 tokens of text.

Its inclusion criterion: training cost.

Step by step

How to choose a GPU for DBRX

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 DBRX, needing around 64.2 GB at a compression of Q3_K_M. 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 DBRX.
03

Decide how much compression you will accept

Compression is what makes a model fit smaller cards, at some cost in accuracy, reaching a compression of Q3_K_M 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

Rank by throughput rather than spec sheet

Sort by speed to see how cards rank for DBRX. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is H100 NVL 94 GB, at 107 tok/s.
05

Read the fit column last

The fit column separates cards that just manage it from those with room to spare, in the case of DBRX. 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

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

Answers

DBRX — common questions

DBRX— how fast is it on a GPU?

It depends on the card. The quickest we calculate is H100 NVL 94 GB, at about 107 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: 36.

DBRX— how much VRAM does it need?

It needs about 64.2 GB at a compression of Q3_K_M, 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.

DBRX— 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.

DBRX— how many parameters does it have?

It has a parameter count of 132B. 132B mixture of experts. 36B parameters active per inference. 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.

DBRX— who created it?

It was published by Databricks, based in United States of America, an organisation categorised as industry.

DBRX— when was it released?

It was published in March 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.

DBRX— what is it used for?

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

DBRX— where can I download it?

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

DBRX— how much compute was used to train it?

Training consumed around 2.6 × 10²⁴ FLOP, on hardware recorded as NVIDIA H100 SXM5 80GB. 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.

DBRX— 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 21.9 GB. Every figure here assumes the whole model is resident on the card.

DBRX— would two GPUs run it faster?

Capacity adds across cards; throughput does not. The number of cards already holding it on their own: 38. So a second card is rarely the answer here.

DBRX— 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: 5. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

DBRX— how accurate are these speed estimates?

They are calculated from specifications rather than measured, and each carries a range. One example: 64–171 tok/s on H100 NVL 94 GB. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

DBRX— what GPU do I need to run it?

The smallest card in our catalogue that holds it is RTX PRO 5000 72 GB Blackwell, with a memory capacity of 72 GB. It runs the model at a compression of Q3_K_M using about 64.2 GB, and produces roughly 42.5 tokens per second. The number of cards able to run it in total: 38.

Source

Original publication

Record last updated 28 November 2025

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