Reka Core
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, Quantitative reasoning
- 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
- 67B
- Training data
- 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
- 8.4 × 10²⁴ FLOP
- How it was established
- Hardware
- Plausible range
- 6.3 × 10²³ – 2.7 × 10²⁶ FLOP
No direct information about Reka Core model ("Reka Core has not finished training and is still improving.") The smaller dense model Reka Flash has 21B parameters and was trained on 5 trillion language tokens. There is information about compute: "Our setup comprises of clusters from a mixture of vendors with our peak compute being approximately 2.5K H100s and 2.5K A100s." If we assume 2 months of training with 2.5k H100s and 2.5k A100s at utilization 0.5 we get 8.4e24 FLOP (2500*9.9e14+2500*3.…
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
- Why it is tracked
- Training cost
- Record confidence
- Speculative
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
Where it came from
Reka Core was published by Reka AI, in United States of America, in April 2024. The organisation is categorised as industry.
It works in Multimodal, Language, Vision, Video, Speech, and is recorded as doing chat, Language modeling/generation, Image captioning, Code generation, Code autocompletion, Question answering, Visual question answering, Video description, Speech recognition (ASR), Speech-to-text, Quantitative reasoning.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Producing it required around 8.4 × 10²⁴ FLOP of arithmetic, on NVIDIA A100,NVIDIA H100 SXM5 80GB, which is a statement about the training budget rather than about inference.
Its inclusion criterion is training cost.
Answers
Reka Core — common questions
How much compute was used to train Reka Core?
Around 8.4 × 10²⁴ FLOP, on 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.
What GPU do I need to run Reka Core?
None. Reka Core 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 Reka Core open source?
No. Reka Core has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Reka Core have?
Reka Core has 67B parameters. 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 Reka Core?
Reka Core was published by Reka AI, based in United States of America, categorised as industry.
When was Reka Core released?
Reka Core 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.
What is Reka Core used for?
Reka Core works in Multimodal, Language, Vision, Video, Speech, and is recorded as handling chat, Language modeling/generation, Image captioning, Code generation, Code autocompletion, Question answering, Visual question answering, Video description, Speech recognition (ASR), Speech-to-text, Quantitative reasoning. 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.
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.