Reka Flash
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
- Reka AI
- Organisation type
- Industry
- Country
- United States of America
- Published
- 15 April 2024
- Authors
- Aitor Ormazabal, Che Zheng, Cyprien de Masson d'Autume, Dani Yogatama, Deyu Fu, Donovan Ong, Eric Chen, Eugenie Lamprecht, Hai Pham, Isaac Ong, Kaloyan Aleksiev, Lei Li, Matthew Henderson, Max Bain, Mikel Artetxe, Nishant Relan, Piotr Padlewski, Qi Liu, Ren Chen, Samuel Phua, Yazheng Yang, Yi Tay, Yuqi Wang, Zhongkai Zhu, Zhihui Xie
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Language, Vision, Video, Speech
- Task
- Chat, Language modeling/generation, Image captioning, Code generation, Code autocompletion, Question answering, Visual question answering, Video description, Speech recognition (ASR), Speech-to-text
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
- 21B
- Training data
- tokens
21B dense
5T text tokens + unknown amount of multimodal data
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
- 6.3 × 10²³ FLOP
- How it was established
- Hardware,Operation counting
Reka Flash has 21B parameters and was trained on 5 trillion language tokens (+ unknown amount of multimodal data) 6 FLOP / token / parameter * 21B parameters * 5 trillion text tokens = 6.3 × 10^23 FLOP OOM agrees with GPU details: "Reka Flash and Edge were trained on several hundreds of H100s across a period of several weeks." 3 weeks * 300 H100s * 7 day/week * 24 hour/day * 3600 s/day * 9.9e14 FLOP/GPU-s * 0.3 [assumed utilization]= 1.6156506e+23 FLOP Not enough info to estimate SFT and RL…
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,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
- Closed — provider access only
- Model access
- API access
- Training code
- Unreleased
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
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Reka Flash was published by Reka AI, in the country recorded as United States of America, during April 2024. The publishing organisation is categorised as industry.
It works in the domain of Multimodal, Language, Vision, Video, Speech, and is recorded as performing the task of chat, Language modeling/generation, Image captioning, Code generation, Code autocompletion, Question answering, Visual question answering, Video description, Speech recognition (ASR), Speech-to-text.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Producing it required arithmetic totalling around 6.3 × 10²³ FLOP, on hardware recorded as NVIDIA A100,NVIDIA H100 SXM5 80GB. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Reka Flash — common questions
Reka Flash— what is it used for?
It works in the domain of Multimodal, Language, Vision, Video, Speech, and is recorded as handling the task of chat, Language modeling/generation, Image captioning, Code generation, Code autocompletion, Question answering, Visual question answering, Video description, Speech recognition (ASR), Speech-to-text. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Reka Flash— how much compute was used to train it?
Training consumed around 6.3 × 10²³ FLOP, on hardware recorded as NVIDIA A100,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.
Reka Flash— what GPU do I need to run it?
None. This 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.
Reka Flash— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Reka Flash— how many parameters does it have?
It has a parameter count of 21B. 21B dense. 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.
Reka Flash— who created it?
It was published by Reka AI, based in United States of America, an organisation categorised as industry.
Reka Flash— when was it released?
It was published in April 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.
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.