Demist-2
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
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
- Darktrace
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 17 April 2025
- Authors
- Miguel Neves Fonseca, Philip Sellars, Tim Bazalgette
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Text classification, Question answering, Entity embedding
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
- 95M
- Training data
- 351,000,000,000 tokens
- Epochs
- 1.8
95M
C4: We extracted a 305 billion token subset URIs: We extracted the available URIs from several Common Crawl web scrapes. This allowed us to create a large corpus of 10 billion tokens. Linked Hostnames: Using the Common Crawl dataset, we extracted the observed links between web pages. <..> We further processed this data into rows of hostnames that shared linking properties which totaled 11 billion tokens. HTTP Data: 25 billion tokens. 305 + 10 + 11 + 25 = 351 b tokens "1.2 million optimization …
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
- 4.6 × 10²⁰ FLOP
- How it was established
- Operation counting,Hardware
6 FLOP / parameter / token * 95 * 10^6 parameters * 351 * 10^9 tokens [see dataset size notes] *1.8 epochs = 3.60126e+20 FLOP 312000000000000 FLOP / GPU / sec [A100 reported] * 8 GPUs * 216 hours [9 days reported] * 3600 sec / hour * 0.3 [assumed utilization] = 5.8226688e+20 FLOP sqrt(3.60126e+20 * 5.8226688e+20) = 4.579186e+20 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
- Wall-clock time
- 216 hours (9 days)
- Power draw
- 6.3 kW
- Cloud vendor
- AWS,Amazon Web Services
"DEMIST-2 was trained on 8 A100 GPUs over a nine-day period using the AWS Sagemaker platform." 9 days = 216 hours
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
- Closed — provider access only
- Model access
- Hosted access (no API)
- 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
Sources
Where this record came from and when it was last checked.
- Reference
- DEMIST-2: Darktrace Embedding Model for Investigation of Security Threats
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Demist-2 was published by Darktrace, in United Kingdom of Great Britain and Northern Ireland, in April 2025. The organisation is categorised as industry.
It works in Language, and is recorded as doing language modeling/generation, Text classification, Question answering, Entity embedding.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Training it took roughly 4.6 × 10²⁰ FLOP of computation, on NVIDIA A100 — a measure of what producing the model cost, not of how fast it answers.
Around 351,000,000,000 tokens went into training it.
Answers
Demist-2 — common questions
Who created Demist-2?
Demist-2 was published by Darktrace, based in United Kingdom of Great Britain and Northern Ireland, categorised as industry.
When was Demist-2 released?
Demist-2 was published in April 2025.
What is Demist-2 used for?
Demist-2 works in Language, and is recorded as handling language modeling/generation, Text classification, Question answering, Entity embedding. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train Demist-2?
Around 4.6 × 10²⁰ FLOP, on NVIDIA A100. 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.
What GPU do I need to run Demist-2?
None. Demist-2 is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Demist-2 open source?
No. Demist-2 has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Demist-2 have?
Demist-2 has 95M parameters. 95M. 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.
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.