AFM-server
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
- Apple
- Organisation type
- Industry
- Country
- United States of America
- Published
- 29 July 2024
- Authors
- Andy Narayanan, Aonan Zhang, Bowen Zhang, Chen Chen, Chong Wang, Chung-Cheng Chiu, David Qiu, Deepak Gopinath, Dian Ang Yap, Dong Yin, Feng Nan, Floris Weers, Guoli Yin, Haoshuo Huang, Jianyu Wang, Jiarui Lu, John Peebles, Ke Ye, Mark Lee, Nan Du, Qibin Chen, Quentin Keunebroek, Ruoming Pang, Sam Wiseman, Syd Evans, Tao Lei, Tom Gunter, Vivek Rathod, Xiang Kong, Xianzhi Du, Yanghao Li, Yongqiang W…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation
- Approach
- Self-supervised learning
- 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.
- Training data
- 7,400,000,000,000 tokens
- Epochs
- 1
- Batch size
- 18,949,753
Not explicitly mentioned, but I assume the 7.4T tokens do not involve multiple epochs.
Main pretraining uses sequence length of 4096 tokens; 4096 sequences per batch. During the "continued" pre-training stage, sequence length is upped to 8192 while batch size remains 4096. During context lengthening, sequence length is upped to 32768 while "the recipe is similar to that used for continued pre-training" implies same batch size of 4096. Weighting batch sizes by number of tokens seen in each stage: exp((6.3T * ln(4096 * 4096) + 1T * ln(8192 * 4096) + 100B * ln(32768 * 4096))/ 7.4T)…
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
- 4.3 × 10²⁴ FLOP
- How it was established
- Operation counting,Hardware
"The AFM base models are dense decoder-only models that build on the Transformer architecture" "We train AFM-server from scratch for 6.3T tokens on 8192 TPUv4 chips, using a sequence length of 4096 and a batch-size of 4096 sequences." "For both models we perform continued pre-training at a sequence length of 8192, with another 1T tokens from a mixture that upweights math and code, and down-weights the bulk web-crawl." "The sustained model-flop-utilization (MFU) for this training run was appro…
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 v4
- Chips used
- 8,192
- Hardware utilisation
- MFU 52.0%
- Power draw
- 5.5 MW
- Cloud vendor
- Google Cloud
- Data centre
- Google us-central2-b
"AFM-server was trained on 8192 TPUv4 chips provisioned as 8 × 1024 chip slices, where slices are connected together by the data-center network (DCN) [Chowdhery et al., 2022]. Only data-parallelism crosses the slice boundary, other types of state sharding are within-slice only as the within-slice interconnect bandwidth is orders of magnitude higher than the DCN. The sustained model-flop-utilization (MFU) for this training run was approximately 52%."
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.
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- Significant use
- Record confidence
- Likely
Currently in beta access only, but will be integrated into millions or billions of iPhones.
Sources
Where this record came from and when it was last checked.
- Reference
- Apple Intelligence Foundation Language Models
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
AFM-server was published by Apple, in United States of America, in July 2024. The organisation is categorised as industry.
It works in Language, and is recorded as doing language modeling/generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Producing it required around 4.3 × 10²⁴ FLOP of arithmetic, on Google TPU v4, which is a statement about the training budget rather than about inference.
Around 7,400,000,000,000 tokens went into training it.
Its inclusion criterion is significant use.
Answers
AFM-server — common questions
Who created AFM-server?
AFM-server was published by Apple, based in United States of America, categorised as industry.
When was AFM-server released?
AFM-server was published in July 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 AFM-server used for?
AFM-server works in Language, and is recorded as handling language modeling/generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train AFM-server?
Around 4.3 × 10²⁴ FLOP, on Google TPU v4. 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 AFM-server?
None. AFM-server 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 AFM-server open source?
No. AFM-server has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does AFM-server have?
No parameter count has been published for AFM-server, which is why no memory or speed figure appears on this page.
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.