FFN SwiGLU
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 14 February 2020
- Authors
- Noam Shazeer
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling, Question answering
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
- 220M
- Training data
- 50,600,083,456 tokens
"We use the same code base, model architecture, and training task as the base model from [Raffel et al.,2019]. The encoder and decoder each consist of 12 layers, with dmodel = 768. For the attention layers, h = 12 and dk = dv = 64. The FFN layers have hidden size df f = 3072." [Raffel et al.,2019]: "Specifically, both the encoder and decoder consist of 12 blocks (each block comprising self-attention, optional encoder-decoder attention, and a feed-forward network). The feed-forward networks in e…
pre-training: "Identically to [Raffel et al., 2019], we pre-train for 524,288 steps on the span-filling objective on the C4 dataset. Each training batch consists of 128 examples, each of which has an input of 512 tokens and an output of 114 tokens" 524288*128*(512+114) = 42010148864 fine-tuning: "Fine-tuning consists of 131072 steps <..>, the input sequences for each step have a combined length of approximately 65,536 tokens." 131072*65536 = 8589934592 42010148864+8589934592 = 50600083456
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
- 3.4 × 10¹⁹ FLOP
- How it was established
- Hardware,Operation counting
6 FLOP/parameter/token * 220000000 parameters * 50600083456 tokens = 66792110161920000000 FLOP (10^19) 45000000000000 FLOP/GPU/sec * 21.85 hours * 3600 sec / hour * 16 GPUs * 0.3 [assumed utilization] = 16990560000000002000 FLOP (10^19) sqrt(66792110161920000000*16990560000000002000) = 3.3687317e+19
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
- Google TPU v2
- Chips used
- 16
- Wall-clock time
- 22 hours
- Power draw
- 9.2 kW
"Each training step took approximately 0.15 seconds on a 32-core TPUv2 cluster" -> 16 chips "we pre-train for 524,288 steps" 524288*0.15/3600 ~ 21.85 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
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Historical significance
- Record confidence
- Confident
the paper introduced SwiGLU
Sources
Where this record came from and when it was last checked.
- Reference
- GLU Variants Improve Transformer
- Last updated
- 28 November 2025
What the numbers mean
What this model is
FFN SwiGLU was published by Google, in the country recorded as United States of America, during February 2020. It comes out of an organisation categorised as industry.
It works in the domain of Language, and is recorded as performing the task of language modeling, Question answering.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
The training run consumed about 3.4 × 10¹⁹ FLOP, on hardware recorded as Google TPU v2. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 50,600,083,456 tokens of text.
Its inclusion criterion: historical significance.
Answers
FFN SwiGLU — common questions
FFN SwiGLU— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
FFN SwiGLU— how many parameters does it have?
It has a parameter count of 220M. "We use the same code base, model architecture, and training task as the base model from [Raffel et al.,2019]. The encoder and decoder each consist of 12 layers, with dmodel = 768. For the attention layers, h = 12 and dk = dv = 64. The FFN layers have hidden size df f = 3072." [Raffel et al.,2019]: "Specifically, both the encoder and decoder consist of 12 blocks (each block comprising self-attention, optional encoder-decoder attention, and a feed-forward network). The feed-forward networks in each block consist of a dense layer with an output dimensionality of dff = 3072 followed by a ReLU nonlinearity and another dense layer. The “key” and “value” matrices of all attention mechanisms have an inner dimensionality of dkv = 64 and all attention mechanisms have 12 heads. All other sub-layers and embeddings have a dimensionality of dmodel = 768. In total, this results in a model with about 220 million 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.
FFN SwiGLU— who created it?
It was published by Google, based in United States of America, an organisation categorised as industry.
FFN SwiGLU— when was it released?
It was published in February 2020. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
FFN SwiGLU— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling, Question answering. 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.
FFN SwiGLU— how much compute was used to train it?
Training consumed around 3.4 × 10¹⁹ FLOP, on hardware recorded as Google TPU v2. 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.
FFN SwiGLU— 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.
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.